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Blogroll | Top

List of posts by date of publication | Top

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