Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi [2601.05444] What Functions Does XGBoost Learn? 2026-06-04T18:10:45+00:00 https://arxiv.org/abs/2601.05444 cshalizi to:NB functional_analysis boosting ensemble_methods decision_trees via:msw https://pinboard.in/ https://pinboard.in/u:cshalizi/b:85d5009a07b7/ [2512.01819] Decision Tree Embedding by Leaf-Means 2025-12-06T14:30:34+00:00 https://arxiv.org/abs/2512.01819 cshalizi to:NB decision_trees neural_networks classifiers priebe.carey_e. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a1ccf503ec38/ Learning Accurate and Interpretable Decision Trees | OpenReview 2024-11-02T19:51:04+00:00 https://openreview.net/forum?id=skdlnUYRzQ cshalizi in_NB to_read decision_trees missing_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e2dda7b0a0e7/ [2402.01502] Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers 2024-02-27T19:59:40+00:00 https://arxiv.org/abs/2402.01502 cshalizi to_read ensemble_methods random_forests decision_trees learning_theory in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:afc37c93e049/ [2301.07015] Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection 2023-05-01T20:37:24+00:00 https://arxiv.org/abs/2301.07015 cshalizi to:NB classifiers networked_life deceiving_us_has_become_an_industrial_process decision_trees to_teach:data-mining philip_k_dick_and_the_fake_humans_rules_everything_around_me https://pinboard.in/ https://pinboard.in/u:cshalizi/b:26709234aea1/ Journal of Open Source Software: policytree: Policy learning via doubly robust empirical welfare maximization over trees 2023-04-27T14:40:08+00:00 https://joss.theoj.org/papers/10.21105/joss.02232 cshalizito:NB decision_trees athey.susan wager.stefan statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:364aa068e29b/ Approximation trees: statistical reproducibility in model distillation | SpringerLink 2023-01-17T06:19:08+00:00 https://link.springer.com/article/10.1007/s10618-022-00907-3 cshalizi to:NB decision_trees data_mining hooker.giles https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2edc09ab8e94/ [2207.08815] Why do tree-based models still outperform deep learning on tabular data? 2022-08-25T16:03:35+00:00 https://arxiv.org/abs/2207.08815 cshalizi to:NB to_read your_favorite_deep_neural_network_sucks ensemble_methods decision_trees to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2b5e2a0c03ab/ [2206.04661] Distillation Decision Tree 2022-06-10T14:08:46+00:00 https://arxiv.org/abs/2206.04661 cshalizi to:NB decision_trees neural_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2a7e816e9336/ [2104.13881] Universal Consistency of Decision Trees for High Dimensional Additive Models 2021-04-29T03:30:43+00:00 https://arxiv.org/abs/2104.13881 cshalizi decision_trees additive_models in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9dcb98265561/ [2101.11190] Boost-S: Gradient Boosted Trees for Spatial Data and Its Application to FDG-PET Imaging Data 2021-02-05T20:12:15+00:00 https://arxiv.org/abs/2101.11190 cshalizi to:NB ensemble_methods boosting decision_trees spatial_statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9fdb3ea321fd/ [2012.14563] Random Planted Forest: a directly interpretable tree ensemble 2021-01-03T20:06:28+00:00 https://arxiv.org/abs/2012.14563 cshalizi to:NB regression nonparametrics ensemble_methods decision_trees to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f6e4ba73e30f/ [2012.12802] Machine Learning Advances for Time Series Forecasting 2020-12-24T15:33:23+00:00 https://arxiv.org/abs/2012.12802 cshalizi to:NB time_series prediction data_mining decision_trees random_forests neural_networks ensemble_methods to_teach:data_over_space_and_time https://pinboard.in/ https://pinboard.in/u:cshalizi/b:30ad1b56b9ba/ [1902.03999] KTBoost: Combined Kernel and Tree Boosting 2020-11-19T19:46:20+00:00 https://arxiv.org/abs/1902.03999 cshalizi to:NB ensemble_methods boosting kernel_methods decision_trees to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8dff463b6f9b/ Classification in the Wild | The MIT Press 2020-09-21T04:00:00+00:00 https://mitpress.mit.edu/books/classification-wild cshalizi books:noted gigerenzer.gerd decision-making decision_trees heuristics in_NB downloaded https://pinboard.in/ https://pinboard.in/u:cshalizi/b:be88add55585/ adabag: An R Package for Classification with Boosting and Bagging | Alfaro | Journal of Statistical Software 2019-12-01T15:47:34+00:00 https://www.jstatsoft.org/article/view/v054i02 cshalizi to:NB boosting bagging ensemble_methods classifiers decision_trees R to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:af00024c2969/ Random Forests | SpringerLink 2019-11-25T15:59:39+00:00 https://link.springer.com/article/10.1023%2FA%3A1010933404324 cshalizi have_read breiman.leo ensemble_methods decision_trees random_forests to_teach:data-mining machine_learning statistics prediction in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:11aee8d7c62c/ [1911.03054] An Experimental Comparison of Old and New Decision Tree Algorithms 2019-11-11T20:46:39+00:00 https://arxiv.org/abs/1911.03054 cshalizi decision_trees color_me_skeptical in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:93442542625d/ [1911.00190] Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success 2019-11-10T22:34:10+00:00 https://arxiv.org/abs/1911.00190 cshalizi to:NB random_forests statistics decision_trees ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cf2088f4c949/ [1806.03467] Orthogonal Random Forest for Causal Inference 2019-10-01T16:11:28+00:00 https://arxiv.org/abs/1806.03467 cshalizi to:NB decision_trees ensemble_methods regression causal_inference statistics nonparametrics random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:64b039275305/ [1909.11799] Manifold Forests: Closing the Gap on Neural Networks 2019-10-01T13:53:57+00:00 https://arxiv.org/abs/1909.11799 cshalizi to:NB decision_trees ensemble_methods to_read random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9e7ddf9742fb/ [1906.10086] Analyzing CART 2019-09-04T19:48:03+00:00 https://arxiv.org/abs/1906.10086 cshalizi statistics decision_trees cart in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:63bdf2a831c6/ [1908.06852] SIRUS: making random forests interpretable 2019-08-20T14:22:39+00:00 https://arxiv.org/abs/1908.06852 cshalizi to:NB classifiers ensemble_methods random_forests decision_trees data_mining statistics to_teach:data-mining have_skimmed https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e72b24d4a589/ [1807.11408] Local Linear Forests 2019-06-23T17:33:39+00:00 https://arxiv.org/abs/1807.11408 cshalizi to:NB linear_regression ensemble_methods decision_trees athey.susan statistics random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e4550a9d18a1/ [1906.07177] (f)RFCDE: Random Forests for Conditional Density Estimation and Functional Data 2019-06-19T15:33:10+00:00 https://arxiv.org/abs/1906.07177 cshalizi to:NB ensemble_methods regression density_estimation statistics kith_and_kin decision_trees lee.ann_b. random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7a354f01b8c9/ [1904.07830] Scalable and Efficient Hypothesis Testing with Random Forests 2019-05-10T03:17:06+00:00 https://arxiv.org/abs/1904.07830 cshalizi to:NB ensemble_methods decision_trees classifiers statistics hypothesis_testing random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bfb279e5da3b/ FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees 2017-08-01T14:47:49+00:00 http://journal.sjdm.org/17/17217/jdm17217.html cshalizi decision_trees heuristics cognitive_science R have_read to_teach:undergrad-ADA re:ADAfaEPoV in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cc94e08f69e8/ [1311.4555] Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife 2016-12-04T21:31:22+00:00 https://arxiv.org/abs/1311.4555 cshalizi to:NB bootstrap confidence_sets ensemble_methods random_forests decision_trees statistics nonparametrics efron.bradley hastie.trevor https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7e987d8e279f/ [1311.6392] A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees 2016-12-01T20:29:54+00:00 https://arxiv.org/abs/1311.6392 cshalizi regression decision_trees nonparametrics statistics ensemble_methods color_me_skeptical in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d14d31d5a173/ [1202.1561] Tree models for difference and change detection in a complex environment 2016-11-30T01:56:46+00:00 https://arxiv.org/abs/1202.1561 cshalizi decision_trees statistics re:network_differences have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:047cb0ac88d5/ [1510.04342] Estimation and Inference of Heterogeneous Treatment Effects using Random Forests 2016-03-28T01:03:42+00:00 http://arxiv.org/abs/1510.04342 cshalizi to:NB decision_trees causal_inference statistics regression ensemble_methods athey.susan random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:07956dd36801/ [1504.01132] Recursive Partitioning for Heterogeneous Causal Effects 2016-03-28T01:02:49+00:00 http://arxiv.org/abs/1504.01132 cshalizi to:NB decision_trees regression causal_inference cross-validation statistics athey.susan imbens.guido_w. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fe6061dfaa75/ The NSA’s SKYNET program may be killing thousands of innocent people | Ars Technica UK 2016-02-16T17:56:29+00:00 http://arstechnica.co.uk/security/2016/02/the-nsas-skynet-program-may-be-killing-thousands-of-innocent-people/ cshalizi the_continuing_crises national_surveillance_state machine_learning classifiers cross-validation bad_data_analysis terrorism_fears drones decision_trees ensemble_methods to_teach:data-mining to:blog random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1248c0c8cf03/ Machine Learning & Non-Linear Models 2015-10-30T18:17:56+00:00 https://86f47fe78a5d63da83a76fa1cc0162ae153f55d8.googledrive.com/secure/AB5fxWAw7W8ON_fg5_i2BCNy2LshJNVmE4EMbqSNR5OWGqBwte0kTnus963Bif38YUWr4YlEDexVD9nKw03gVJkD9tkUQwHDZ2Sig-JvNGYxMP7DQp-IAFAzGrOlmbfmQHaS-0OQgvfEcku8_ZFRNLNowdtKDAAJLP-QSTN6s1MOggTcGfTOBgvoSSO3VT4DXrauXYk0w39lek68H_gn9Ugl93SXTKG8fEZ5nyty3TGbcouXclbDxbZS4aOTo0V9tVFKCtLEPo8F54aPqghEGNwqeowM0pakzi7grg3LD3W6Rf6I9zHwTxh3G8cO0eBFZefO2eUhuY9qenswApUYKs3fIr3kKTj27ryP5s0iBQcy1AheJM-uXQGbxjz6jBFHAOuGk99SkY8iAAB2VBSc_ufumkoaaRSRJSR-GOTt-j04L4rpZV7ozIUh6CW4UUVQUUjMe0anAQdhYt8ZsXXDE60nUI38QqDyQtH9ltY1JGGnlkmptReKewJm14wZWV-LPrHZYWnivrqrL9-hf5DAZ-DgF0zPwWWikIgB5sZnKvRsq0qvpfezAWGnpZ-MvWKUI93eBKIK_3zd/host/0Byvk0A8Ic21Yck9ScVUzaEVsNG8/#/ cshalizi regression additive_models classifiers decision_trees statistics ensemble_methods sociology via:kjhealy have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4c1d43eafccd/ [1405.0352] Asymptotic Theory for Random Forests 2015-01-23T05:09:10+00:00 http://arxiv.org/abs/1405.0352 cshalizi to:NB ensemble_methods statistics prediction decision_trees https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c880d1ae24a6/ [1405.1533] A consistent deterministic regression tree for non-parametric prediction of time series 2015-01-22T00:28:48+00:00 http://arxiv.org/abs/1405.1533 cshalizi to_read time_series nonparametrics decision_trees regression learning_theory statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:68696c063b54/ [1409.2090] On the asymptotics of random forests 2015-01-20T14:07:18+00:00 http://arxiv.org/abs/1409.2090 cshalizi to:NB decision_trees ensemble_methods empirical_processes statistics random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1fb865de58fb/ [1405.2881] Consistency of Random Forests 2015-01-20T14:06:18+00:00 http://arxiv.org/abs/1405.2881 cshalizi to:NB ensemble_methods decision_trees statistics random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:02de34d24f22/ [1406.1845] Detecting Feature Interactions in Bagged Trees and Random Forests 2014-07-12T00:28:16+00:00 http://arxiv.org/abs/1406.1845 cshalizi to:NB additive_models ensemble_methods statistics hypothesis_testing decision_trees hooker.giles random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b9147b01495b/ [1402.4293] The Random Forest Kernel and other kernels for big data from random partitions 2014-03-08T22:35:09+00:00 http://arxiv.org/abs/1402.4293 cshalizi to:NB data_mining kernel_methods statistics ghahramani.zoubin decision_trees https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7d42e150ce86/ [1310.5677] Penalized Split Criteria for Interpretable Trees 2013-10-23T19:48:53+00:00 http://arxiv.org/abs/1310.5677 cshalizi decision_trees classifiers regression data_mining statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a55b5eed28e8/ [1309.7733] Regression Trees for Longitudinal Data 2013-10-11T23:28:42+00:00 http://arxiv.org/abs/1309.7733 cshalizi to:NB time_series decision_trees regression nonparametrics statistics to_teach:undergrad-ADA to_teach:data_over_space_and_time https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f815c23bfec4/ [1310.1415] Narrowing the Gap: Random Forests In Theory and In Practice 2013-10-10T13:29:08+00:00 http://arxiv.org/abs/1310.1415 cshalizi to:NB ensemble_methods decision_trees regression statistics random_forests https://pinboard.in/ https://pinboard.in/u:cshalizi/b:52b2f5841082/ Encoding Through Patterns: Regression Tree–Based Neuronal Population Models 2013-06-27T16:36:43+00:00 http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00464 cshalizi to:NB neural_data_analysis neural_coding_and_decoding neuroscience statistics kith_and_kin haslinger.rob decision_trees https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c49f9237a525/ Loh , Zheng : Regression trees for longitudinal and multiresponse data 2013-04-10T15:52:21+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1365527208 cshalizi decision_trees regression time_series statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b864d6960d24/ Taylor & Francis Online :: Morse–Smale Regression - Journal of Computational and Graphical Statistics - Volume 22, Issue 1 2013-03-28T17:30:10+00:00 http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.657132 cshalizi to:NB decision_trees regression topology statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:df788d063c97/ [1302.4853] Consistency of Online Random Forests 2013-02-21T23:39:26+00:00 http://arxiv.org/abs/1302.4853 cshalizi to:NB decision_trees classifiers machine_learning learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c0910fc190af/ Enhanced spatiotemporal relational probability trees and forests - Springer 2013-01-14T21:11:51+00:00 http://link.springer.com/article/10.1007/s10618-012-0261-2 cshalizi to:NB data_mining spatio-temporal_statistics statistics relational_learning decision_trees https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fe6f222fbdb9/ "Reinforcement Learning Trees" by Ruoqing Zhu, Donglin Zeng et al. 2013-01-11T14:43:34+00:00 http://biostats.bepress.com/uncbiostat/art33/ cshalizi regression statistics reinforcement_learning decision_trees to_teach:data-mining in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e00f77a7dea3/ [1206.4620] Improved Information Gain Estimates for Decision Tree Induction 2012-06-23T13:49:09+00:00 http://arxiv.org/abs/1206.4620 cshalizi decision_trees data_mining machine_learning entropy_estimation information_theory classifiers to_teach:data-mining in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a03c429b2274/ [1205.2609] Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? 2012-05-15T11:33:37+00:00 http://arxiv.org/abs/1205.2609 cshalizi to:NB decision_trees prediction regression statistics dimension_reduction machine_learning https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b908fc0ee0d7/ [1202.1523] Information Forests 2012-02-10T18:12:11+00:00 http://arxiv.org/abs/1202.1523 cshalizi decision_trees information_theory classifiers machine_learning to_teach:data-mining re:AoS_project have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7f7eb0d797fe/ [1202.1561] Tree Models for Difference and Change Detection in a Complex Environment 2012-02-10T05:18:16+00:00 http://arxiv.org/abs/1202.1561 cshalizi re:network_differences statistics hypothesis_testing density_estimation decision_trees have_read data_mining two-sample_tests in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1d69327d5561/ RE-EM Trees: A Data Ming Approach for Longitudinal and Clustered Data 2012-01-24T23:49:05+00:00 http://www.springerlink.com/content/ng44781g47736260/ cshalizi to:NB machine_learning decision_trees data_mining statistics hierarchical_statistical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5da6a371e870/ Additive Groves of Regression Trees - Sorokina et al. 2008-07-17T17:50:33+00:00 http://www.cs.cornell.edu/~daria/papers/Groves.pdf cshaliziregression decision_trees ensemble_methods statistics machine_learning sorokina.daria caruana.rich riedewald.mirek have_read https://pinboard.in/u:cshalizi/b:f69860d3cd51/ Detecting Statistical Interactions with Additive Groves of Trees - Sorokina et al. 2008-07-17T17:47:41+00:00 http://www.cs.cornell.edu/~daria/papers/Interactions.pdf cshaliziregression decision_trees statistics ensemble_methods sorokina.daria caruana.rich riedewald.mirek machine_learning statistical_interaction fink.daniel have_read https://pinboard.in/u:cshalizi/b:69e01d82961f/ Showing the Obama-Clinton Divide in Decision Tree Infographic | FlowingData 2008-04-24T13:26:56+00:00 http://flowingdata.com/2008/04/23/showing-the-obama-clinton-divide-in-decision-tree-infographic/ cshalizito_teach:data-mining decision_trees classifiers cox.amanda via:klk us_politics obama.barack clinton.hillary blogged https://pinboard.in/u:cshalizi/b:711b9a159942/ [0804.0650] Storms prediction : Logistic regression vs random forest for unbalanced data 2008-04-10T19:03:44+00:00 http://arxiv.org/abs/0804.0650 cshalizi have_read machine_learning weather_prediction decision_trees to_teach:data-mining ensemble_methods random_forests https://pinboard.in/u:cshalizi/b:0c8fb572085f/ [0711.2434] Variable importance in binary regression trees and forests 2007-11-16T22:17:33+00:00 http://arxiv.org/abs/0711.2434 cshalizistatistics decision_trees to_teach:data-mining in_NB https://pinboard.in/u:cshalizi/b:91be0a2ef5c0/ [0708.1820] Confidence sets for split points in decision trees 2007-11-15T15:06:40+00:00 http://arxiv.org/abs/0708.1820 cshaliziconfidence_sets decision_trees statistics in_NB https://pinboard.in/u:cshalizi/b:26310d6f4e3d/