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Blogroll | Top


List of posts by date of publication | Top

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


Categories | Top

Forecasting

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

Misc

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

Python

  1. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  2. RPweave: Unified R + Python + LaTeX System using uv
  3. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  4. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  5. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  6. Native uncertainty quantification for time series with NGBoost
  7. NGBoost (Natural Gradient Boosting) for Regression, Classification, Time Series forecasting and Reserving
  8. Real-time pricing with a pretrained probabilistic stock return model
  9. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  10. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  11. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  12. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  13. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  14. Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting
  15. New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization)
  16. mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI.
  17. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  18. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  19. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  20. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  21. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  22. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  23. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  24. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  25. Backpropagating quasi-randomized neural networks
  26. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  27. An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters
  28. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  29. Free R, Python and SQL editors in techtonique dot net
  30. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  31. scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  32. Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  33. Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  34. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  35. 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
  36. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  37. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  38. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  39. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  40. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  41. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  42. Survival stacking: survival analysis translated as supervised classification in R and Python
  43. Extending the Theta forecasting method to GLMs and attention
  44. Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net
  45. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  46. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  47. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  48. Web app with DeepSeek R1 and Hugging Face API for chatting
  49. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  50. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  51. Command Line Interface (CLI) for techtonique.net's API
  52. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  53. 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
  54. Techtonique is out! (with a tutorial in various programming languages and formats)
  55. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  56. 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?
  57. 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)
  58. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  59. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  60. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  61. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  62. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  63. GLMNet in Python: Generalized Linear Models
  64. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  65. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  66. Chat with your tabular data in www.techtonique.net
  67. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  68. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  69. R and Python consoles + JupyterLite in www.techtonique.net
  70. Gradient-Boosting anything (alert: high performance)
  71. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  72. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  73. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  74. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  75. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  76. New versions of nnetsauce and ahead
  77. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  78. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  79. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  80. Copulas for uncertainty quantification in time series forecasting
  81. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  82. learningmachine for Python (new version)
  83. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  84. 10 uncertainty quantification methods in nnetsauce forecasting
  85. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  86. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  87. Automated hyperparameter tuning using any conformalized surrogate
  88. Recognizing handwritten digits with Ridge2Classifier
  89. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  90. Probability of receiving a loan; using learningmachine
  91. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  92. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  93. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  94. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  95. learningmachine v1.1.2: for Python
  96. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  97. rtopy (v0.1.1): calling R functions in Python
  98. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  99. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  100. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  101. Tuning Machine Learning models with GPopt's new version Part 2
  102. Tuning Machine Learning models with GPopt's new version
  103. Subsampling continuous and discrete response variables
  104. DeepMTS, a Deep Learning Model for Multivariate Time Series
  105. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  106. 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
  107. Diffusion models in Python with esgtoolkit (Part2)
  108. Diffusion models in Python with esgtoolkit
  109. Quasi-randomized nnetworks in Julia, Python and R
  110. A plethora of datasets at your fingertips
  111. A classifier that's very accurate (and deep)
  112. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  113. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  114. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  115. Version v0.14.0 of nnetsauce for R and Python
  116. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  117. (News from) forecasting in Python with ahead (progress bars and plots)
  118. Forecasting in Python with ahead
  119. Did you ask ChatGPT about who you are?
  120. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  121. Simple interfaces to the forecasting API
  122. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  123. Prediction intervals (not only) for Boosted Configuration Networks in Python
  124. A Machine Learning workflow using Techtonique
  125. Super Mario Bros © in the browser using PyScript
  126. News from ESGtoolkit, ycinterextra, and nnetsauce
  127. Explaining a Keras _neural_ network predictions with the-teller
  128. New version of nnetsauce -- various quasi-randomized networks
  129. A dashboard illustrating bivariate time series forecasting with `ahead`
  130. Forecasting with `ahead` (Python version)
  131. Tuning and interpreting LSBoost
  132. Classification using linear regression
  133. Documentation and source code for GPopt, a package for Bayesian optimization
  134. Hyperparameters tuning with GPopt
  135. A forecasting tool (API) with examples in curl, R, Python
  136. Bayesian Optimization with GPopt Part 2 (save and resume)
  137. Bayesian Optimization with GPopt
  138. Compatibility of nnetsauce and mlsauce with scikit-learn
  139. Explaining xgboost predictions with the teller
  140. An infinity of time series models in nnetsauce
  141. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  142. A deeper learning architecture in nnetsauce
  143. Generalized nonlinear models in nnetsauce
  144. Boosting nonlinear penalized least squares
  145. Technical documentation
  146. A new version of nnetsauce, and a new Techtonique website
  147. Back next week, and a few announcements
  148. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  149. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  150. nnetsauce version 0.5.0, randomized neural networks on GPU
  151. Maximizing your tip as a waiter (Part 2)
  152. Parallel AdaOpt classification
  153. Maximizing your tip as a waiter
  154. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  155. AdaOpt
  156. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  157. Encoding your categorical variables based on the response variable and correlations
  158. Documentation for the querier, a query language for Data Frames
  159. Import data into the querier (now on Pypi), a query language for Data Frames
  160. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  161. A new version of nnetsauce (v0.3.1)
  162. Using R in Python for statistical learning/data science
  163. 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. Deterministic Shift Adjustment in Arbitrage-Free Pricing (historical to risk-neutral short rates)
  2. New instantaneous short rates models with their deterministic shift adjustment, for historical and risk-neutral simulation
  3. RPweave: Unified R + Python + LaTeX System using uv
  4. GAN-like Synthetic Data Generation Examples (on univariate, multivariate distributions, digits recognition, Fashion-MNIST, stock returns, and Olivetti faces) with DistroSimulator
  5. Part2 of More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  6. More data (> 150 files) on T. Moudiki's situation: a riddle/puzzle (including R, Python, bash interfaces to the game -- but everyone can play)
  7. R port of llama2.c
  8. Native uncertainty quantification for time series with NGBoost
  9. Real-time pricing with a pretrained probabilistic stock return model
  10. Combining any model with GARCH(1,1) for probabilistic stock forecasting
  11. Generating Synthetic Data with R-vine Copulas using esgtoolkit in R
  12. Reimagining Equity Solvency Capital Requirement Approximation (one of my Master's Thesis subjects): From Bilinear Interpolation to Probabilistic Machine Learning
  13. Transfer Learning using ahead::ridge2f on synthetic stocks returns Pt.2: synthetic data generation
  14. Transfer Learning using ahead::ridge2f on synthetic stocks returns
  15. I'm supposed to present 'Conformal Predictive Simulations for Univariate Time Series' at COPA CONFERENCE 2025 in London...
  16. external regressors in ahead::dynrmf's interface for Machine learning forecasting
  17. Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation'
  18. A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics).
  19. Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting
  20. Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting
  21. Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting
  22. Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting
  23. Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting
  24. Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting
  25. Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian)
  26. R version of 'Backpropagating quasi-randomized neural networks'
  27. Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting
  28. Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf
  29. Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models
  30. Exceptionally, and on a more personal note (otherwise I may get buried alive)...
  31. Free R, Python and SQL editors in techtonique dot net
  32. Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation
  33. R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)
  34. Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)
  35. Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line)
  36. 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
  37. A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios
  38. Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose
  39. Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out
  40. How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API
  41. Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API
  42. Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor
  43. Survival stacking: survival analysis translated as supervised classification in R and Python
  44. 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package
  45. A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction
  46. A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf
  47. Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series)
  48. Extending the Theta forecasting method to GLMs and attention
  49. Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models)
  50. Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize
  51. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  52. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  53. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  54. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  55. CRAN-like repository for most recent releases of Techtonique's R packages
  56. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  57. Web app with DeepSeek R1 and Hugging Face API for chatting
  58. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  59. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  60. A simple test of the martingale hypothesis in esgtoolkit
  61. Command Line Interface (CLI) for techtonique.net's API
  62. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  63. 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
  64. Techtonique is out! (with a tutorial in various programming languages and formats)
  65. 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?
  66. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  67. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  68. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  69. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  70. GLMNet in Python: Generalized Linear Models
  71. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  72. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  73. Chat with your tabular data in www.techtonique.net
  74. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  75. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  76. R and Python consoles + JupyterLite in www.techtonique.net
  77. Gradient-Boosting anything (alert: high performance): Part2, R version
  78. Gradient-Boosting anything (alert: high performance)
  79. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  80. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  81. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  82. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  83. New versions of nnetsauce and ahead
  84. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  85. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  86. R package development workflow (assuming you're using macOS or Linux)
  87. A new method for deriving a nonparametric confidence interval for the mean
  88. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  89. Bayesian (nonlinear) adaptive learning
  90. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  91. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  92. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  93. Forecasting the Economy
  94. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  95. Probability of receiving a loan; using learningmachine
  96. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  97. Conformalized predictive simulations for univariate time series on more than 250 data sets
  98. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  99. Multiple examples of Machine Learning forecasting with ahead
  100. rtopy (v0.1.1): calling R functions in Python
  101. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  102. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  103. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  104. 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
  105. Diffusion models in Python with esgtoolkit (Part2)
  106. Diffusion models in Python with esgtoolkit
  107. Quasi-randomized nnetworks in Julia, Python and R
  108. A plethora of datasets at your fingertips
  109. A classifier that's very accurate (and deep)
  110. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  111. Version v0.14.0 of nnetsauce for R and Python
  112. A diffusion model: G2++
  113. Diffusion models in ESGtoolkit + announcements
  114. Risk-neutralize simulations
  115. Comparing cross-validation results using crossval_ml and boxplots
  116. Did you ask ChatGPT about who you are?
  117. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  118. Simple interfaces to the forecasting API
  119. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  120. Boosted Configuration (neural) Networks Pt. 2
  121. Boosted Configuration (_neural_) Networks for classification
  122. News from ESGtoolkit, ycinterextra, and nnetsauce
  123. New version of nnetsauce -- various quasi-randomized networks
  124. A dashboard illustrating bivariate time series forecasting with `ahead`
  125. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  126. Time series cross-validation using `crossvalidation` (Part 2)
  127. Fast and scalable forecasting with ahead::ridge2f
  128. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  129. Forecasting with `ahead`
  130. `crossvalidation` and random search for calibrating support vector machines
  131. parallel grid search cross-validation using `crossvalidation`
  132. `crossvalidation` on R-universe, plus a classification example
  133. A forecasting tool (API) with examples in curl, R, Python
  134. An infinity of time series models in nnetsauce
  135. New activation functions in mlsauce's LSBoost
  136. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  137. Classify penguins with nnetsauce's MultitaskClassifier
  138. Bayesian forecasting for uni/multivariate time series
  139. Boosting nonlinear penalized least squares
  140. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  141. Submitting R package to CRAN
  142. Simulation of dependent variables in ESGtoolkit
  143. Forecasting lung disease progression
  144. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  145. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  146. nnetsauce version 0.5.0, randomized neural networks on GPU
  147. Maximizing your tip as a waiter (Part 2)
  148. Maximizing your tip as a waiter
  149. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  150. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  151. Custom errors for cross-validation using crossval::crossval_ml
  152. Encoding your categorical variables based on the response variable and correlations
  153. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  154. Grid search cross-validation using crossval
  155. Time series cross-validation using crossval
  156. On model specification, identification, degrees of freedom and regularization
  157. R notebooks for nnetsauce
  158. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  159. Feedback forms for contributing
  160. nnetsauce for R
  161. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  162. Using R in Python for statistical learning/data science
  163. Model calibration with `crossval`
  164. crossval