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Previous publications

1. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
2. New versions of nnetsauce and ahead Sep 9, 2024
3. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
4. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
5. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
6. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
7. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
8. Bayesian (nonlinear) adaptive learning Aug 12, 2024
9. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
10. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
11. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
12. learningmachine for Python (new version) Jul 15, 2024
13. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
14. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
15. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
16. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
17. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
18. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
19. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
20. Forecasting the Economy May 27, 2024
21. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
22. Probability of receiving a loan; using learningmachine May 12, 2024
23. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
24. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
25. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
26. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
27. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
28. learningmachine v1.1.2: for Python Apr 1, 2024
29. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
30. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
31. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
32. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
33. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
34. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
35. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
36. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
37. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
38. Subsampling continuous and discrete response variables Jan 22, 2024
39. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
40. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
41. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
42. 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
43. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
44. Diffusion models in Python with esgtoolkit Dec 11, 2023
45. Julia packaging at the command line Dec 4, 2023
46. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
47. A plethora of datasets at your fingertips Nov 20, 2023
48. A classifier that's very accurate (and deep) Nov 12, 2023
49. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
50. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
51. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
52. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
53. A diffusion model: G2++ Oct 9, 2023
54. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
55. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
56. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
57. Forecasting in Python with ahead Sep 11, 2023
58. Risk-neutralize simulations Sep 4, 2023
59. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
60. Reminder Apr 30, 2023
61. Did you ask ChatGPT about who you are? Apr 16, 2023
62. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
63. Simple interfaces to the forecasting API Nov 23, 2022
64. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
65. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
66. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
67. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
68. A Machine Learning workflow using Techtonique Jun 6, 2022
69. Super Mario Bros © in the browser using PyScript May 8, 2022
70. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
71. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
72. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
73. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
74. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
75. Forecasting with `ahead` (Python version) Dec 13, 2021
76. Tuning and interpreting LSBoost Nov 15, 2021
77. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
78. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
79. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
80. Forecasting with `ahead` Oct 15, 2021
81. Classification using linear regression Sep 26, 2021
82. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
83. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
84. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
85. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
86. Hyperparameters tuning with GPopt Jun 11, 2021
87. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
88. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
89. Bayesian Optimization with GPopt Apr 16, 2021
90. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
91. Explaining xgboost predictions with the teller Mar 12, 2021
92. An infinity of time series models in nnetsauce Mar 6, 2021
93. New activation functions in mlsauce's LSBoost Feb 12, 2021
94. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
95. A deeper learning architecture in nnetsauce Dec 18, 2020
96. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
97. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
98. Generalized nonlinear models in nnetsauce Nov 28, 2020
99. Boosting nonlinear penalized least squares Nov 21, 2020
100. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
101. NEWS Oct 30, 2020
102. A glimpse into my PhD journey Oct 23, 2020
103. Submitting R package to CRAN Oct 16, 2020
104. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
105. Forecasting lung disease progression Oct 2, 2020
106. New nnetsauce Sep 25, 2020
107. Technical documentation Sep 18, 2020
108. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
109. Back next week, and a few announcements Sep 4, 2020
110. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
111. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
112. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
113. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
114. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
115. Announcements Jun 26, 2020
116. Parallel AdaOpt classification Jun 19, 2020
117. Comments section and other news Jun 12, 2020
118. Maximizing your tip as a waiter Jun 5, 2020
119. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
120. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
121. AdaOpt May 15, 2020
122. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
123. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
124. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
125. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
126. Grid search cross-validation using crossval Apr 10, 2020
127. Documentation for the querier, a query language for Data Frames Apr 3, 2020
128. Time series cross-validation using crossval Mar 27, 2020
129. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
130. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
131. R notebooks for nnetsauce Mar 6, 2020
132. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
133. Create a specific feed in your Jekyll blog Feb 21, 2020
134. Git/Github for contributing to package development Feb 14, 2020
135. Feedback forms for contributing Feb 7, 2020
136. nnetsauce for R Jan 31, 2020
137. A new version of nnetsauce (v0.3.1) Jan 24, 2020
138. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
139. Search bar, new year 2020 Jan 10, 2020
140. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
141. Understanding model interactions with the `teller` Dec 13, 2019
142. Using the `teller` on a classifier Dec 6, 2019
143. Benchmarking the querier's verbs Nov 29, 2019
144. Composing the querier's verbs for data wrangling Nov 22, 2019
145. Comparing and explaining model predictions with the teller Nov 15, 2019
146. Tests for the significance of marginal effects in the teller Nov 8, 2019
147. Introducing the teller Nov 1, 2019
148. Introducing the querier Oct 25, 2019
149. Prediction intervals for nnetsauce models Oct 18, 2019
150. Using R in Python for statistical learning/data science Oct 11, 2019
151. Model calibration with `crossval` Oct 4, 2019
152. Bagging in the nnetsauce Sep 25, 2019
153. Adaboost learning with nnetsauce Sep 18, 2019
154. Change in blog's presentation Sep 4, 2019
155. nnetsauce on Pypi Jun 5, 2019
156. More nnetsauce (examples of use) May 9, 2019
157. nnetsauce Mar 13, 2019
158. crossval Mar 13, 2019
159. test Mar 10, 2019