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Blogroll | Top

List of posts by date of publication | Top

  1. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
  2. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
  3. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
  4. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
  5. CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
  6. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
  7. Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
  8. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
  9. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
  10. 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
  11. A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
  12. Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
  13. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
  14. 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
  15. Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
  16. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
  17. 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
  18. 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
  19. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
  20. No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
  21. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
  22. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
  23. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
  24. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
  25. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
  26. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  27. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  28. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  29. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  30. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  31. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  32. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  33. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  34. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  35. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  36. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  37. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  38. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  39. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  40. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  41. New versions of nnetsauce and ahead Sep 9, 2024
  42. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  43. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  44. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  45. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  46. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  47. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  48. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  49. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  50. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  51. learningmachine for Python (new version) Jul 15, 2024
  52. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  53. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  54. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  55. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  56. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  57. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  58. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  59. Forecasting the Economy May 27, 2024
  60. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  61. Probability of receiving a loan; using learningmachine May 12, 2024
  62. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  63. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  64. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  65. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  66. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  67. learningmachine v1.1.2: for Python Apr 1, 2024
  68. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  69. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  70. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  71. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  72. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  73. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  74. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  75. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  76. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  77. Subsampling continuous and discrete response variables Jan 22, 2024
  78. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  79. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  80. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  81. 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
  82. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  83. Diffusion models in Python with esgtoolkit Dec 11, 2023
  84. Julia packaging at the command line Dec 4, 2023
  85. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  86. A plethora of datasets at your fingertips Nov 20, 2023
  87. A classifier that's very accurate (and deep) Nov 12, 2023
  88. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  89. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  90. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  91. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  92. A diffusion model: G2++ Oct 9, 2023
  93. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  94. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  95. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  96. Forecasting in Python with ahead Sep 11, 2023
  97. Risk-neutralize simulations Sep 4, 2023
  98. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  99. Reminder Apr 30, 2023
  100. Did you ask ChatGPT about who you are? Apr 16, 2023
  101. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  102. Simple interfaces to the forecasting API Nov 23, 2022
  103. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  104. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  105. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  106. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  107. A Machine Learning workflow using Techtonique Jun 6, 2022
  108. Super Mario Bros © in the browser using PyScript May 8, 2022
  109. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  110. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  111. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  112. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  113. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  114. Forecasting with `ahead` (Python version) Dec 13, 2021
  115. Tuning and interpreting LSBoost Nov 15, 2021
  116. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  117. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  118. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  119. Forecasting with `ahead` Oct 15, 2021
  120. Classification using linear regression Sep 26, 2021
  121. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  122. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  123. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  124. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  125. Hyperparameters tuning with GPopt Jun 11, 2021
  126. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  127. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  128. Bayesian Optimization with GPopt Apr 16, 2021
  129. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  130. Explaining xgboost predictions with the teller Mar 12, 2021
  131. An infinity of time series models in nnetsauce Mar 6, 2021
  132. New activation functions in mlsauce's LSBoost Feb 12, 2021
  133. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  134. A deeper learning architecture in nnetsauce Dec 18, 2020
  135. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  136. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  137. Generalized nonlinear models in nnetsauce Nov 28, 2020
  138. Boosting nonlinear penalized least squares Nov 21, 2020
  139. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  140. NEWS Oct 30, 2020
  141. A glimpse into my PhD journey Oct 23, 2020
  142. Submitting R package to CRAN Oct 16, 2020
  143. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  144. Forecasting lung disease progression Oct 2, 2020
  145. New nnetsauce Sep 25, 2020
  146. Technical documentation Sep 18, 2020
  147. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  148. Back next week, and a few announcements Sep 4, 2020
  149. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  150. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  151. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  152. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  153. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  154. Announcements Jun 26, 2020
  155. Parallel AdaOpt classification Jun 19, 2020
  156. Comments section and other news Jun 12, 2020
  157. Maximizing your tip as a waiter Jun 5, 2020
  158. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  159. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  160. AdaOpt May 15, 2020
  161. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  162. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  163. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  164. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  165. Grid search cross-validation using crossval Apr 10, 2020
  166. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  167. Time series cross-validation using crossval Mar 27, 2020
  168. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  169. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  170. R notebooks for nnetsauce Mar 6, 2020
  171. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  172. Create a specific feed in your Jekyll blog Feb 21, 2020
  173. Git/Github for contributing to package development Feb 14, 2020
  174. Feedback forms for contributing Feb 7, 2020
  175. nnetsauce for R Jan 31, 2020
  176. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  177. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  178. Search bar, new year 2020 Jan 10, 2020
  179. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  180. Understanding model interactions with the `teller` Dec 13, 2019
  181. Using the `teller` on a classifier Dec 6, 2019
  182. Benchmarking the querier's verbs Nov 29, 2019
  183. Composing the querier's verbs for data wrangling Nov 22, 2019
  184. Comparing and explaining model predictions with the teller Nov 15, 2019
  185. Tests for the significance of marginal effects in the teller Nov 8, 2019
  186. Introducing the teller Nov 1, 2019
  187. Introducing the querier Oct 25, 2019
  188. Prediction intervals for nnetsauce models Oct 18, 2019
  189. Using R in Python for statistical learning/data science Oct 11, 2019
  190. Model calibration with `crossval` Oct 4, 2019
  191. Bagging in the nnetsauce Sep 25, 2019
  192. Adaboost learning with nnetsauce Sep 18, 2019
  193. Change in blog's presentation Sep 4, 2019
  194. nnetsauce on Pypi Jun 5, 2019
  195. More nnetsauce (examples of use) May 9, 2019
  196. nnetsauce Mar 13, 2019
  197. crossval Mar 13, 2019
  198. test Mar 10, 2019

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. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  2. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  3. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  4. Web app with DeepSeek R1 and Hugging Face API for chatting
  5. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  6. Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart
  7. Command Line Interface (CLI) for techtonique.net's API
  8. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  9. 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
  10. Techtonique is out! (with a tutorial in various programming languages and formats)
  11. Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN
  12. 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?
  13. 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)
  14. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  15. survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce
  16. Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt
  17. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting
  18. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  19. GLMNet in Python: Generalized Linear Models
  20. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  21. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  22. Chat with your tabular data in www.techtonique.net
  23. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  24. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  25. R and Python consoles + JupyterLite in www.techtonique.net
  26. Gradient-Boosting anything (alert: high performance)
  27. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  28. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  29. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  30. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  31. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  32. New versions of nnetsauce and ahead
  33. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  34. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  35. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  36. Copulas for uncertainty quantification in time series forecasting
  37. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  38. learningmachine for Python (new version)
  39. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  40. 10 uncertainty quantification methods in nnetsauce forecasting
  41. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  42. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  43. Automated hyperparameter tuning using any conformalized surrogate
  44. Recognizing handwritten digits with Ridge2Classifier
  45. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  46. Probability of receiving a loan; using learningmachine
  47. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  48. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  49. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  50. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  51. learningmachine v1.1.2: for Python
  52. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  53. rtopy (v0.1.1): calling R functions in Python
  54. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  55. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  56. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  57. Tuning Machine Learning models with GPopt's new version Part 2
  58. Tuning Machine Learning models with GPopt's new version
  59. Subsampling continuous and discrete response variables
  60. DeepMTS, a Deep Learning Model for Multivariate Time Series
  61. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  62. 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
  63. Diffusion models in Python with esgtoolkit (Part2)
  64. Diffusion models in Python with esgtoolkit
  65. Quasi-randomized nnetworks in Julia, Python and R
  66. A plethora of datasets at your fingertips
  67. A classifier that's very accurate (and deep)
  68. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  69. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  70. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  71. Version v0.14.0 of nnetsauce for R and Python
  72. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  73. (News from) forecasting in Python with ahead (progress bars and plots)
  74. Forecasting in Python with ahead
  75. Did you ask ChatGPT about who you are?
  76. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  77. Simple interfaces to the forecasting API
  78. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  79. Prediction intervals (not only) for Boosted Configuration Networks in Python
  80. A Machine Learning workflow using Techtonique
  81. Super Mario Bros © in the browser using PyScript
  82. News from ESGtoolkit, ycinterextra, and nnetsauce
  83. Explaining a Keras _neural_ network predictions with the-teller
  84. New version of nnetsauce -- various quasi-randomized networks
  85. A dashboard illustrating bivariate time series forecasting with `ahead`
  86. Forecasting with `ahead` (Python version)
  87. Tuning and interpreting LSBoost
  88. Classification using linear regression
  89. Documentation and source code for GPopt, a package for Bayesian optimization
  90. Hyperparameters tuning with GPopt
  91. A forecasting tool (API) with examples in curl, R, Python
  92. Bayesian Optimization with GPopt Part 2 (save and resume)
  93. Bayesian Optimization with GPopt
  94. Compatibility of nnetsauce and mlsauce with scikit-learn
  95. Explaining xgboost predictions with the teller
  96. An infinity of time series models in nnetsauce
  97. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  98. A deeper learning architecture in nnetsauce
  99. Generalized nonlinear models in nnetsauce
  100. Boosting nonlinear penalized least squares
  101. Technical documentation
  102. A new version of nnetsauce, and a new Techtonique website
  103. Back next week, and a few announcements
  104. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  105. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  106. nnetsauce version 0.5.0, randomized neural networks on GPU
  107. Maximizing your tip as a waiter (Part 2)
  108. Parallel AdaOpt classification
  109. Maximizing your tip as a waiter
  110. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  111. AdaOpt
  112. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  113. Encoding your categorical variables based on the response variable and correlations
  114. Documentation for the querier, a query language for Data Frames
  115. Import data into the querier (now on Pypi), a query language for Data Frames
  116. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  117. A new version of nnetsauce (v0.3.1)
  118. Using R in Python for statistical learning/data science
  119. 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. My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance
  2. Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels
  3. (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction
  4. Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion
  5. CRAN-like repository for most recent releases of Techtonique's R packages
  6. Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier
  7. Web app with DeepSeek R1 and Hugging Face API for chatting
  8. tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R)
  9. R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction)
  10. A simple test of the martingale hypothesis in esgtoolkit
  11. Command Line Interface (CLI) for techtonique.net's API
  12. Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend
  13. 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
  14. Techtonique is out! (with a tutorial in various programming languages and formats)
  15. 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?
  16. Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich)
  17. No-code Machine Learning Cross-validation and Interpretability in techtonique.net
  18. You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS
  19. Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates')
  20. GLMNet in Python: Generalized Linear Models
  21. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  22. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  23. Chat with your tabular data in www.techtonique.net
  24. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  25. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  26. R and Python consoles + JupyterLite in www.techtonique.net
  27. Gradient-Boosting anything (alert: high performance): Part2, R version
  28. Gradient-Boosting anything (alert: high performance)
  29. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  30. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  31. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  32. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  33. New versions of nnetsauce and ahead
  34. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  35. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  36. R package development workflow (assuming you're using macOS or Linux)
  37. A new method for deriving a nonparametric confidence interval for the mean
  38. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  39. Bayesian (nonlinear) adaptive learning
  40. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  41. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  42. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  43. Forecasting the Economy
  44. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  45. Probability of receiving a loan; using learningmachine
  46. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  47. Conformalized predictive simulations for univariate time series on more than 250 data sets
  48. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  49. Multiple examples of Machine Learning forecasting with ahead
  50. rtopy (v0.1.1): calling R functions in Python
  51. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  52. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  53. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  54. 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
  55. Diffusion models in Python with esgtoolkit (Part2)
  56. Diffusion models in Python with esgtoolkit
  57. Quasi-randomized nnetworks in Julia, Python and R
  58. A plethora of datasets at your fingertips
  59. A classifier that's very accurate (and deep)
  60. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  61. Version v0.14.0 of nnetsauce for R and Python
  62. A diffusion model: G2++
  63. Diffusion models in ESGtoolkit + announcements
  64. Risk-neutralize simulations
  65. Comparing cross-validation results using crossval_ml and boxplots
  66. Did you ask ChatGPT about who you are?
  67. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  68. Simple interfaces to the forecasting API
  69. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  70. Boosted Configuration (neural) Networks Pt. 2
  71. Boosted Configuration (_neural_) Networks for classification
  72. News from ESGtoolkit, ycinterextra, and nnetsauce
  73. New version of nnetsauce -- various quasi-randomized networks
  74. A dashboard illustrating bivariate time series forecasting with `ahead`
  75. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  76. Time series cross-validation using `crossvalidation` (Part 2)
  77. Fast and scalable forecasting with ahead::ridge2f
  78. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  79. Forecasting with `ahead`
  80. `crossvalidation` and random search for calibrating support vector machines
  81. parallel grid search cross-validation using `crossvalidation`
  82. `crossvalidation` on R-universe, plus a classification example
  83. A forecasting tool (API) with examples in curl, R, Python
  84. An infinity of time series models in nnetsauce
  85. New activation functions in mlsauce's LSBoost
  86. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  87. Classify penguins with nnetsauce's MultitaskClassifier
  88. Bayesian forecasting for uni/multivariate time series
  89. Boosting nonlinear penalized least squares
  90. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  91. Submitting R package to CRAN
  92. Simulation of dependent variables in ESGtoolkit
  93. Forecasting lung disease progression
  94. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  95. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  96. nnetsauce version 0.5.0, randomized neural networks on GPU
  97. Maximizing your tip as a waiter (Part 2)
  98. Maximizing your tip as a waiter
  99. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  100. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  101. Custom errors for cross-validation using crossval::crossval_ml
  102. Encoding your categorical variables based on the response variable and correlations
  103. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  104. Grid search cross-validation using crossval
  105. Time series cross-validation using crossval
  106. On model specification, identification, degrees of freedom and regularization
  107. R notebooks for nnetsauce
  108. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  109. Feedback forms for contributing
  110. nnetsauce for R
  111. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  112. Using R in Python for statistical learning/data science
  113. Model calibration with `crossval`
  114. crossval