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Blogroll | Top

List of posts by date of publication | Top

  1. Skip the R/Python runtime: fast tabular dashboards with Observable Framework Aug 31, 2026
  2. PCARVFL vs CTGAN for synthetic tabular data generation on an insurance pricing dataset Aug 22, 2026
  3. 'Zero-Shot Probabilistic Stock Returns Forecasting with Pretrained RVFL Networks' accepted at COPA 2026 (and to appear in the Proceedings of Machine Learning Research) Aug 15, 2026
  4. 'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping Aug 10, 2026
  5. 'garchf': GARCH probabilistic forecasting with package 'forecast'-style interface (and 'rugarch' under the hood) Aug 1, 2026
  6. GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning Jul 26, 2026
  7. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R Jul 13, 2026
  8. Natively Interpretable Boosting Jul 12, 2026
  9. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus Jun 27, 2026
  10. Using scikit-learn models in R easily with the tisthemachinelearner package Jun 21, 2026
  11. No-Code Machine Learning in Excel with the Techtonique API Jun 14, 2026
  12. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3 Jun 7, 2026
  13. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now) May 31, 2026
  14. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R May 21, 2026
  15. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R May 17, 2026
  16. Probabilistic Time Series Cross-Validation with R package crossvalidation May 16, 2026
  17. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0 May 9, 2026
  18. You Don't Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis May 2, 2026
  19. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2 Apr 28, 2026
  20. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages Apr 26, 2026
  21. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets Apr 19, 2026
  22. `mlS3` — A Unified S3 Machine Learning Interface in R Apr 12, 2026
  23. One interface, (Almost) Every Classifier: unifiedml v0.2.1 Apr 4, 2026
  24. Techtonique dot net is down until further notice Apr 1, 2026
  25. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors) Mar 29, 2026
  26. Python version of 'Option pricing using time series models as market price of risk Pt.3' Mar 22, 2026
  27. Option pricing using time series models as market price of risk Pt.3 Mar 16, 2026
  28. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios) Mar 8, 2026
  29. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting Mar 1, 2026
  30. nnetsauce with and without jax for GPU acceleration Feb 23, 2026
  31. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters Feb 16, 2026
  32. R version of Python package survivalist, for model-agnostic survival analysis Feb 9, 2026
  33. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026) Feb 4, 2026
  34. Option pricing using time series models as market price of risk Feb 1, 2026
  35. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f) Jan 31, 2026
  36. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor Jan 29, 2026
  37. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling Jan 25, 2026
  38. GPopt for Machine Learning (hyperparameters' tuning) Jan 21, 2026
  39. rtopy: an R to Python bridge -- novelties Jan 8, 2026
  40. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Jan 3, 2026
  41. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition Jan 1, 2026
  42. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce Dec 17, 2025
  43. Overfitting Random Fourier Features: Universal Approximation Property Dec 13, 2025
  44. Counterfactual Scenario Analysis with ahead::ridge2f Dec 11, 2025
  45. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce Dec 10, 2025
  46. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint) Dec 7, 2025
  47. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis) Dec 3, 2025
  48. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn Dec 1, 2025
  49. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv Nov 21, 2025
  50. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans Nov 16, 2025
  51. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning Nov 13, 2025
  52. unifiedml in R: A Unified Machine Learning Interface Nov 5, 2025
  53. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates) Oct 28, 2025
  54. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation Oct 27, 2025
  55. RPweave: Unified R + Python + LaTeX System using uv Oct 19, 2025
  56. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator Oct 19, 2025
  57. R port of llama2.c Oct 9, 2025
  58. Native uncertainty quantification for time series with NGBoost Oct 8, 2025
  59. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving Oct 6, 2025
  60. Real-time pricing with a pretrained probabilistic stock return model Oct 1, 2025
  61. Combining any model with GARCH(1,1) for probabilistic stock forecasting Sep 23, 2025
  62. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R Sep 21, 2025
  63. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning Sep 16, 2025
  64. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation Sep 9, 2025
  65. Transfer Learning using ahead::ridge2f on synthetic stocks returns Sep 8, 2025
  66. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London... Sep 4, 2025
  67. external regressors in ahead::dynrmf's interface for Machine learning forecasting Sep 1, 2025
  68. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Aug 20, 2025
  69. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting Jul 26, 2025
  70. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) Jul 15, 2025
  71. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI. Jul 10, 2025
  72. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics). Jul 8, 2025
  73. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting Jul 7, 2025
  74. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting Jul 6, 2025
  75. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
  76. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
  77. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
  78. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
  79. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian) Jul 1, 2025
  80. R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
  81. Backpropagating quasi-randomized neural networks Jun 23, 2025
  82. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
  83. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
  84. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
  85. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
  86. Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
  87. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
  88. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
  89. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
  90. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
  91. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 3, 2025
  92. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
  93. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
  94. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis May 31, 2025
  95. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
  96. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
  97. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out May 28, 2025
  98. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
  99. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
  100. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
  101. Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
  102. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
  103. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
  104. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
  105. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series) Apr 14, 2025
  106. Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
  107. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
  108. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
  109. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
  110. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  111. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  112. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  113. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  114. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  115. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  116. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  117. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  118. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  119. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart Feb 10, 2025
  120. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  121. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  122. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  123. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition Jan 20, 2025
  124. Techtonique is released! (with a tutorial in various programming languages and formats) Jan 14, 2025
  125. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  126. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Jan 5, 2025
  127. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 31, 2024
  128. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  129. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  130. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  131. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  132. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  133. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  134. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  135. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  136. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  137. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  138. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  139. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  140. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  141. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  142. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  143. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  144. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  145. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  146. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  147. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  148. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  149. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  150. New versions of nnetsauce and ahead Sep 9, 2024
  151. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  152. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  153. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  154. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  155. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  156. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  157. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  158. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  159. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  160. learningmachine for Python (new version) Jul 15, 2024
  161. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  162. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  163. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  164. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  165. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  166. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  167. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  168. Forecasting the Economy May 27, 2024
  169. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  170. Probability of receiving a loan; using learningmachine May 12, 2024
  171. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  172. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  173. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  174. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  175. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  176. learningmachine v1.1.2: for Python Apr 1, 2024
  177. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  178. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  179. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  180. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  181. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  182. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  183. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  184. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  185. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  186. Subsampling continuous and discrete response variables Jan 22, 2024
  187. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  188. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  189. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  190. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction Dec 25, 2023
  191. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  192. Diffusion models in Python with esgtoolkit Dec 11, 2023
  193. Julia packaging at the command line Dec 4, 2023
  194. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  195. A plethora of datasets at your fingertips Nov 20, 2023
  196. A classifier that's very accurate (and deep) Nov 12, 2023
  197. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  198. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  199. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  200. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  201. A diffusion model: G2++ Oct 9, 2023
  202. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  203. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  204. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  205. Forecasting in Python with ahead Sep 11, 2023
  206. Risk-neutralize simulations Sep 4, 2023
  207. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  208. Reminder Apr 30, 2023
  209. Did you ask ChatGPT about who you are? Apr 16, 2023
  210. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  211. Simple interfaces to the forecasting API Nov 23, 2022
  212. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  213. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  214. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  215. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  216. A Machine Learning workflow using Techtonique Jun 6, 2022
  217. Super Mario Bros © in the browser using PyScript May 8, 2022
  218. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  219. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  220. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  221. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  222. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  223. Forecasting with `ahead` (Python version) Dec 13, 2021
  224. Tuning and interpreting LSBoost Nov 15, 2021
  225. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  226. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  227. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  228. Forecasting with `ahead` Oct 15, 2021
  229. Classification using linear regression Sep 26, 2021
  230. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  231. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  232. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  233. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  234. Hyperparameters tuning with GPopt Jun 11, 2021
  235. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  236. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  237. Bayesian Optimization with GPopt Apr 16, 2021
  238. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  239. Explaining xgboost predictions with the teller Mar 12, 2021
  240. An infinity of time series models in nnetsauce Mar 6, 2021
  241. New activation functions in mlsauce's LSBoost Feb 12, 2021
  242. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  243. A deeper learning architecture in nnetsauce Dec 18, 2020
  244. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  245. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  246. Generalized nonlinear models in nnetsauce Nov 28, 2020
  247. Boosting nonlinear penalized least squares Nov 21, 2020
  248. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  249. NEWS Oct 30, 2020
  250. A glimpse into my PhD journey Oct 23, 2020
  251. Submitting R package to CRAN Oct 16, 2020
  252. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  253. Forecasting lung disease progression Oct 2, 2020
  254. New nnetsauce Sep 25, 2020
  255. Technical documentation Sep 18, 2020
  256. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  257. Back next week, and a few announcements Sep 4, 2020
  258. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  259. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  260. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  261. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  262. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  263. Announcements Jun 26, 2020
  264. Parallel AdaOpt classification Jun 19, 2020
  265. Comments section and other news Jun 12, 2020
  266. Maximizing your tip as a waiter Jun 5, 2020
  267. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  268. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  269. AdaOpt May 15, 2020
  270. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  271. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  272. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  273. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  274. Grid search cross-validation using crossval Apr 10, 2020
  275. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  276. Time series cross-validation using crossval Mar 27, 2020
  277. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  278. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  279. R notebooks for nnetsauce Mar 6, 2020
  280. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  281. Create a specific feed in your Jekyll blog Feb 21, 2020
  282. Git/Github for contributing to package development Feb 14, 2020
  283. Feedback forms for contributing Feb 7, 2020
  284. nnetsauce for R Jan 31, 2020
  285. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  286. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  287. Search bar, new year 2020 Jan 10, 2020
  288. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  289. Understanding model interactions with the `teller` Dec 13, 2019
  290. Using the `teller` on a classifier Dec 6, 2019
  291. Benchmarking the querier's verbs Nov 29, 2019
  292. Composing the querier's verbs for data wrangling Nov 22, 2019
  293. Comparing and explaining model predictions with the teller Nov 15, 2019
  294. Tests for the significance of marginal effects in the teller Nov 8, 2019
  295. Introducing the teller Nov 1, 2019
  296. Introducing the querier Oct 25, 2019
  297. Prediction intervals for nnetsauce models Oct 18, 2019
  298. Using R in Python for statistical learning/data science Oct 11, 2019
  299. Model calibration with `crossval` Oct 4, 2019
  300. Bagging in the nnetsauce Sep 25, 2019
  301. Adaboost learning with nnetsauce Sep 18, 2019
  302. Change in blog's presentation Sep 4, 2019
  303. nnetsauce on Pypi Jun 5, 2019
  304. More nnetsauce (examples of use) May 9, 2019
  305. nnetsauce Mar 13, 2019
  306. crossval Mar 13, 2019
  307. test Mar 10, 2019

Categories | Top

Forecasting

  1. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  2. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  3. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  4. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  5. Techtonique is released! (with a tutorial in various programming languages and formats)
  6. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  7. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  8. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  9. 10 uncertainty quantification methods in nnetsauce forecasting
  10. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  11. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  12. DeepMTS, a Deep Learning Model for Multivariate Time Series
  13. Version v0.14.0 of nnetsauce for R and Python
  14. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  15. (News from) forecasting in Python with ahead (progress bars and plots)
  16. Forecasting in Python with ahead
  17. Simple interfaces to the forecasting API
  18. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  19. A dashboard illustrating bivariate time series forecasting with `ahead`
  20. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret

Misc

  1. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  2. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  3. Julia packaging at the command line
  4. A plethora of datasets at your fingertips
  5. Risk-neutralize simulations
  6. Comparing cross-validation results using crossval_ml and boxplots
  7. Reminder
  8. Did you ask ChatGPT about who you are?
  9. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  10. Boosted Configuration (_neural_) Networks for classification
  11. Super Mario Bros © in the browser using PyScript
  12. News from ESGtoolkit, ycinterextra, and nnetsauce
  13. Time series cross-validation using `crossvalidation` (Part 2)
  14. Fast and scalable forecasting with ahead::ridge2f
  15. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  16. Forecasting with `ahead`
  17. Documentation and source code for GPopt, a package for Bayesian optimization
  18. Hyperparameters tuning with GPopt
  19. A forecasting tool (API) with examples in curl, R, Python
  20. Bayesian Optimization with GPopt Part 2 (save and resume)
  21. Bayesian Optimization with GPopt
  22. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  23. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  24. NEWS
  25. A glimpse into my PhD journey
  26. Forecasting lung disease progression
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. Maximizing your tip as a waiter (Part 2)
  32. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  33. Announcements
  34. Comments section and other news
  35. Maximizing your tip as a waiter
  36. Custom errors for cross-validation using crossval::crossval_ml
  37. Encoding your categorical variables based on the response variable and correlations
  38. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  39. Grid search cross-validation using crossval
  40. Time series cross-validation using crossval
  41. On model specification, identification, degrees of freedom and regularization
  42. Create a specific feed in your Jekyll blog
  43. Git/Github for contributing to package development
  44. Feedback forms for contributing
  45. Change in blog's presentation
  46. test

Python

  1. Skip the R/Python runtime: fast tabular dashboards with Observable Framework
  2. PCARVFL vs CTGAN for synthetic tabular data generation on an insurance pricing dataset
  3. 'PCARVFLSimulator': a GAN-like tabular data synthesizer built from PCA scores, a Random Vector Functional-Link network, and residuals bootstrapping
  4. Natively Interpretable Boosting
  5. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  6. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  7. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  8. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  9. Survival analysis with sklearn, glmnet, keras, pytorch, lightgbm, xgboost, nnetsauce, mlsauce Part 2
  10. Any Sklearn Regressor as a Survival Model — Does It Actually Work? Benchmarking vs Established Packages
  11. Conformal Optimization Beats Bayesian Optimization, Optuna and Random Search on 72 classification Datasets
  12. Techtonique dot net is down until further notice
  13. Python version of 'Option pricing using time series models as market price of risk Pt.3'
  14. nnetsauce with and without jax for GPU acceleration
  15. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  16. R version of Python package survivalist, for model-agnostic survival analysis
  17. Overfitting and scaling (on GPU T4) tests on nnetsauce.CustomRegressor
  18. GPopt for Machine Learning (hyperparameters' tuning)
  19. rtopy: an R to Python bridge -- novelties
  20. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  21. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  22. Overfitting Random Fourier Features: Universal Approximation Property
  23. Zero-Shot Probabilistic Time Series Forecasting with TabPFN 2.5 and nnetsauce
  24. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  25. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  26. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  27. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  28. RPweave: Unified R + Python + LaTeX System using uv
  29. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  30. Native uncertainty quantification for time series with NGBoost
  31. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  32. Real-time pricing with a pretrained probabilistic stock return model
  33. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  34. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  35. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  36. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  37. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  38. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  39. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  40. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  41. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  42. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  43. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  44. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  45. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  46. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  47. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  48. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  49. Backpropagating quasi-randomized neural networks
  50. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  51. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  52. Free R, Python and SQL editors in techtonique dot net
  53. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  54. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  55. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  56. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  57. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  58. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis
  59. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  60. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  61. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  62. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  63. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  64. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  65. Survival stacking: survival analysis translated as supervised classification in R and Python
  66. Extending the Theta forecasting method to GLMs and attention
  67. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  68. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  69. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  70. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  71. Web app with DeepSeek R1 and Hugging Face API for chatting
  72. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  73. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  74. Command Line Interface (CLI) for techtonique.net's API
  75. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  76. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  77. Techtonique is released! (with a tutorial in various programming languages and formats)
  78. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  79. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  80. Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  81. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  82. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  83. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  84. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  85. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  86. GLMNet in Python: Generalized Linear Models
  87. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  88. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  89. Chat with your tabular data in www.techtonique.net
  90. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  91. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  92. R and Python consoles + JupyterLite in www.techtonique.net
  93. Gradient-Boosting anything (alert: high performance)
  94. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  95. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  96. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  97. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  98. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  99. New versions of nnetsauce and ahead
  100. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  101. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  102. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  103. Copulas for uncertainty quantification in time series forecasting
  104. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  105. learningmachine for Python (new version)
  106. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  107. 10 uncertainty quantification methods in nnetsauce forecasting
  108. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  109. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  110. Automated hyperparameter tuning using any conformalized surrogate
  111. Recognizing handwritten digits with Ridge2Classifier
  112. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  113. Probability of receiving a loan; using learningmachine
  114. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  115. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  116. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  117. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  118. learningmachine v1.1.2: for Python
  119. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  120. rtopy (v0.1.1): calling R functions in Python
  121. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  122. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  123. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  124. Tuning Machine Learning models with GPopt's new version Part 2
  125. Tuning Machine Learning models with GPopt's new version
  126. Subsampling continuous and discrete response variables
  127. DeepMTS, a Deep Learning Model for Multivariate Time Series
  128. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  129. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  130. Diffusion models in Python with esgtoolkit (Part2)
  131. Diffusion models in Python with esgtoolkit
  132. Quasi-randomized nnetworks in Julia, Python and R
  133. A plethora of datasets at your fingertips
  134. A classifier that's very accurate (and deep)
  135. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  136. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  137. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  138. Version v0.14.0 of nnetsauce for R and Python
  139. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  140. (News from) forecasting in Python with ahead (progress bars and plots)
  141. Forecasting in Python with ahead
  142. Did you ask ChatGPT about who you are?
  143. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  144. Simple interfaces to the forecasting API
  145. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  146. Prediction intervals (not only) for Boosted Configuration Networks in Python
  147. A Machine Learning workflow using Techtonique
  148. Super Mario Bros © in the browser using PyScript
  149. News from ESGtoolkit, ycinterextra, and nnetsauce
  150. Explaining a Keras _neural_ network predictions with the-teller
  151. New version of nnetsauce -- various quasi-randomized networks
  152. A dashboard illustrating bivariate time series forecasting with `ahead`
  153. Forecasting with `ahead` (Python version)
  154. Tuning and interpreting LSBoost
  155. Classification using linear regression
  156. Documentation and source code for GPopt, a package for Bayesian optimization
  157. Hyperparameters tuning with GPopt
  158. A forecasting tool (API) with examples in curl, R, Python
  159. Bayesian Optimization with GPopt Part 2 (save and resume)
  160. Bayesian Optimization with GPopt
  161. Compatibility of nnetsauce and mlsauce with scikit-learn
  162. Explaining xgboost predictions with the teller
  163. An infinity of time series models in nnetsauce
  164. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  165. A deeper learning architecture in nnetsauce
  166. Generalized nonlinear models in nnetsauce
  167. Boosting nonlinear penalized least squares
  168. Technical documentation
  169. A new version of nnetsauce, and a new Techtonique website
  170. Back next week, and a few announcements
  171. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  172. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  173. nnetsauce version 0.5.0, randomized neural networks on GPU
  174. Maximizing your tip as a waiter (Part 2)
  175. Parallel AdaOpt classification
  176. Maximizing your tip as a waiter
  177. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  178. AdaOpt
  179. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  180. Encoding your categorical variables based on the response variable and correlations
  181. Documentation for the querier, a query language for Data Frames
  182. Import data into the querier (now on Pypi), a query language for Data Frames
  183. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  184. A new version of nnetsauce (v0.3.1)
  185. Using R in Python for statistical learning/data science
  186. nnetsauce on Pypi

QuasiRandomizedNN

  1. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  2. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  3. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  4. 10 uncertainty quantification methods in nnetsauce forecasting
  5. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  6. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  7. Automated hyperparameter tuning using any conformalized surrogate
  8. Recognizing handwritten digits with Ridge2Classifier
  9. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  10. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  11. DeepMTS, a Deep Learning Model for Multivariate Time Series
  12. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  13. Quasi-randomized nnetworks in Julia, Python and R
  14. A classifier that's very accurate (and deep)
  15. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  16. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  17. Version v0.14.0 of nnetsauce for R and Python
  18. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  19. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  20. New version of nnetsauce -- various quasi-randomized networks
  21. Tuning and interpreting LSBoost
  22. Classification using linear regression
  23. An infinity of time series models in nnetsauce
  24. A deeper learning architecture in nnetsauce
  25. Classify penguins with nnetsauce's MultitaskClassifier
  26. Bayesian forecasting for uni/multivariate time series
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. nnetsauce version 0.5.0, randomized neural networks on GPU
  32. R notebooks for nnetsauce
  33. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  34. Feedback forms for contributing
  35. nnetsauce for R
  36. A new version of nnetsauce (v0.3.1)
  37. 2019 Recap, the nnetsauce, the teller and the querier
  38. Prediction intervals for nnetsauce models
  39. Bagging in the nnetsauce
  40. Adaboost learning with nnetsauce
  41. nnetsauce on Pypi
  42. More nnetsauce (examples of use)
  43. nnetsauce

R

  1. Skip the R/Python runtime: fast tabular dashboards with Observable Framework
  2. 'Zero-Shot Probabilistic Stock Returns Forecasting with Pretrained RVFL Networks' accepted at COPA 2026 (and to appear in the Proceedings of Machine Learning Research)
  3. 'garchf': GARCH probabilistic forecasting with package 'forecast'-style interface (and 'rugarch' under the hood)
  4. GPopt for R: Bayesian and conformal optimization of black-box functions and hyperparameter tuning
  5. My last R posts: How conformalization helps weak models, fast conformal prediction with jackknife+ (and no refitting), and sklearn in R
  6. Fast conformal prediction (no refitting) for some Machine Learning models via closed-form jackknife plus
  7. Using scikit-learn models in R easily with the tisthemachinelearner package
  8. How Conformal Prediction Makes Linear Models Good Enough — An Example Using R Package mlS3
  9. Techtonique dot net, the Machine Learning web API, is back online (but more like a passion project for now)
  10. Conformalized TabICL: Prediction Intervals for a State-Of-The-Art Tabular Foundation Model in Python and R
  11. Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R
  12. Probabilistic Time Series Cross-Validation with R package crossvalidation
  13. One interface, (Almost) Every Classifier (and Regressor): unifiedml v0.3.0
  14. You Don't Need to Learn All the Weights on tabular data: The Case for rvflnet (a nonlinear expressive glmnet) on regression, classification and survival analysis
  15. `mlS3` — A Unified S3 Machine Learning Interface in R
  16. One interface, (Almost) Every Classifier: unifiedml v0.2.1
  17. Techtonique dot net is down until further notice
  18. Explaining Time-Series Forecasts with Sensitivity Analysis (ahead::dynrmf and external regressors)
  19. Option pricing using time series models as market price of risk Pt.3
  20. Explaining Time-Series Forecasts with Exact Shapley Values (ahead::dynrmf with external regressors applied to scenarios)
  21. My Presentation at Risk 2026: Lightweight Transfer Learning for Financial Forecasting
  22. nnetsauce with and without jax for GPU acceleration
  23. Understanding Boosted Configuration Networks (combined neural networks and boosting): An Intuitive Guide Through Their Hyperparameters
  24. R version of Python package survivalist, for model-agnostic survival analysis
  25. Presenting Lightweight Transfer Learning for Financial Forecasting (Risk 2026)
  26. Option pricing using time series models as market price of risk
  27. Enhancing Time Series Forecasting (ahead::ridge2f) with Attention-Based Context Vectors (ahead::contextridge2f)
  28. Beyond Cross-validation: Hyperparameter Optimization via Generalization Gap Modeling
  29. rtopy: an R to Python bridge -- novelties
  30. Python examples for 'Beyond Nelson-Siegel and splines: A model- agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  31. Forecasting benchmark: Dynrmf (a new serious competitor in town) vs Theta Method on M-Competitions and Tourism competitition
  32. Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
  33. Overfitting Random Fourier Features: Universal Approximation Property
  34. Counterfactual Scenario Analysis with ahead::ridge2f
  35. ARIMA Pricing: Semi-Parametric Market price of risk for Risk-Neutral Pricing (code + preprint)
  36. Analyzing Paper Reviews with LLMs: I Used ChatGPT, DeepSeek, Qwen, Mistral, Gemini, and Claude (and you should too + publish the analysis)
  37. tisthemachinelearner: New Workflow with uv for R Integration of scikit-learn
  38. (ICYMI) RPweave: Unified R + Python + LaTeX System using uv
  39. unifiedml: A Unified Machine Learning Interface for R, is now on CRAN + Discussion about AI replacing humans
  40. Context-aware Theta forecasting Method: Extending Classical Time Series Forecasting with Machine Learning
  41. unifiedml in R: A Unified Machine Learning Interface
  42. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates)
  43. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  44. RPweave: Unified R + Python + LaTeX System using uv
  45. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  46. R port of llama2.c
  47. Native uncertainty quantification for time series with NGBoost
  48. Real-time pricing with a pretrained probabilistic stock return model
  49. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  50. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  51. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  52. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  53. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  54. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  55. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  56. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  57. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  58. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  59. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  60. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  61. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  62. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  63. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  64. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  65. R version of 'Backpropagating quasi-randomized neural networks'
  66. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  67. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  68. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  69. Free R, Python and SQL editors in techtonique dot net
  70. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  71. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  72. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  73. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  74. Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis
  75. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  76. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  77. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  78. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  79. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  80. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  81. Survival stacking: survival analysis translated as supervised classification in R and Python
  82. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  83. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  84. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  85. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  86. Extending the Theta forecasting method to GLMs and attention
  87. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  88. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  89. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  90. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  91. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  92. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  93. CRAN-like repository for most recent releases of Techtonique's R packages
  94. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  95. Web app with DeepSeek R1 and Hugging Face API for chatting
  96. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  97. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  98. A simple test of the martingale hypothesis in esgtoolkit
  99. Command Line Interface (CLI) for techtonique.net's API
  100. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  101. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition
  102. Techtonique is released! (with a tutorial in various programming languages and formats)
  103. Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on?
  104. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  105. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  106. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  107. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  108. GLMNet in Python: Generalized Linear Models
  109. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  110. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  111. Chat with your tabular data in www.techtonique.net
  112. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  113. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  114. R and Python consoles + JupyterLite in www.techtonique.net
  115. Gradient-Boosting anything (alert: high performance): Part2, R version
  116. Gradient-Boosting anything (alert: high performance)
  117. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  118. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  119. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  120. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  121. New versions of nnetsauce and ahead
  122. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  123. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  124. R package development workflow (assuming you're using macOS or Linux)
  125. A new method for deriving a nonparametric confidence interval for the mean
  126. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  127. Bayesian (nonlinear) adaptive learning
  128. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  129. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  130. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  131. Forecasting the Economy
  132. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  133. Probability of receiving a loan; using learningmachine
  134. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  135. Conformalized predictive simulations for univariate time series on more than 250 data sets
  136. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  137. Multiple examples of Machine Learning forecasting with ahead
  138. rtopy (v0.1.1): calling R functions in Python
  139. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  140. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  141. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  142. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  143. Diffusion models in Python with esgtoolkit (Part2)
  144. Diffusion models in Python with esgtoolkit
  145. Quasi-randomized nnetworks in Julia, Python and R
  146. A plethora of datasets at your fingertips
  147. A classifier that's very accurate (and deep)
  148. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  149. Version v0.14.0 of nnetsauce for R and Python
  150. A diffusion model: G2++
  151. Diffusion models in ESGtoolkit + announcements
  152. Risk-neutralize simulations
  153. Comparing cross-validation results using crossval_ml and boxplots
  154. Did you ask ChatGPT about who you are?
  155. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  156. Simple interfaces to the forecasting API
  157. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  158. Boosted Configuration (neural) Networks Pt. 2
  159. Boosted Configuration (_neural_) Networks for classification
  160. News from ESGtoolkit, ycinterextra, and nnetsauce
  161. New version of nnetsauce -- various quasi-randomized networks
  162. A dashboard illustrating bivariate time series forecasting with `ahead`
  163. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  164. Time series cross-validation using `crossvalidation` (Part 2)
  165. Fast and scalable forecasting with ahead::ridge2f
  166. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  167. Forecasting with `ahead`
  168. `crossvalidation` and random search for calibrating support vector machines
  169. parallel grid search cross-validation using `crossvalidation`
  170. `crossvalidation` on R-universe, plus a classification example
  171. A forecasting tool (API) with examples in curl, R, Python
  172. An infinity of time series models in nnetsauce
  173. New activation functions in mlsauce's LSBoost
  174. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  175. Classify penguins with nnetsauce's MultitaskClassifier
  176. Bayesian forecasting for uni/multivariate time series
  177. Boosting nonlinear penalized least squares
  178. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  179. Submitting R package to CRAN
  180. Simulation of dependent variables in ESGtoolkit
  181. Forecasting lung disease progression
  182. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  183. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  184. nnetsauce version 0.5.0, randomized neural networks on GPU
  185. Maximizing your tip as a waiter (Part 2)
  186. Maximizing your tip as a waiter
  187. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  188. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  189. Custom errors for cross-validation using crossval::crossval_ml
  190. Encoding your categorical variables based on the response variable and correlations
  191. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  192. Grid search cross-validation using crossval
  193. Time series cross-validation using crossval
  194. On model specification, identification, degrees of freedom and regularization
  195. R notebooks for nnetsauce
  196. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  197. Feedback forms for contributing
  198. nnetsauce for R
  199. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  200. Using R in Python for statistical learning/data science
  201. Model calibration with `crossval`
  202. crossval