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/ [2601.10825] Reasoning Models Generate Societies of Thought 2026-04-08T16:58:19+00:00 https://arxiv.org/abs/2601.10825 cshalizi to:NB to_read artificial_intelligence large_language_models_(so_called) ensemble_methods evans.james kith_and_kin https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f2cf63b3474b/ [2603.12228] Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights 2026-03-22T04:13:16+00:00 https://arxiv.org/abs/2603.12228 cshalizi to:NB neural_networks large_language_models_(so_called) ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a9f750012e1c/ [2602.01011] Multi-Agent Teams Hold Experts Back 2026-02-12T17:22:39+00:00 https://arxiv.org/abs/2602.01011 cshalizi to:NB large_language_models_(so_called) ensemble_methods collective_cognition via:henry_farrell https://pinboard.in/ https://pinboard.in/u:cshalizi/b:39e92ff5a350/ [2405.06691v3] Fleet of Agents: Coordinated Problem Solving with Large Language Models 2025-09-05T16:01:14+00:00 https://arxiv.org/abs/2405.06691v3 cshalizi to:NB ensemble_methods particle_filters collective_cognition large_language_models_(so_called) https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4492f638f68c/ Peirce in the Machine: How Mixture of Experts Models Perform Hypothesis Construction | Philosophy of Science | Cambridge Core 2025-09-05T15:03:49+00:00 https://www.cambridge.org/core/journals/philosophy-of-science/article/peirce-in-the-machine-how-mixture-of-experts-models-perform-hypothesis-construction/2C92DF1A6805195170683CC6EC446125?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles cshalizi to:NB philosophy_of_science ensemble_methods color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b0495f96c9bf/ [2503.03213] Convergence Rates for Softmax Gating Mixture of Experts 2025-04-09T14:16:09+00:00 https://arxiv.org/abs/2503.03213 cshalizi to:NB kith_and_kin rinaldo.alessandro ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:69ffdb506cef/ [1503.02531] Distilling the Knowledge in a Neural Network 2025-03-04T18:58:35+00:00 https://arxiv.org/abs/1503.02531 cshalizi to:NB ensemble_methods to_read to_teach:data-mining via:rvenkat https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a950b4f51e90/ Model compression | Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining 2025-03-04T18:57:34+00:00 https://dl.acm.org/doi/10.1145/1150402.1150464 cshalizi to:NB ensemble_methods to_read to_teach:data-mining via:rvenkat https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5a3d0fd453f6/ [2408.05446] Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness 2024-08-21T11:55:03+00:00 https://arxiv.org/abs/2408.05446 cshalizi to:NB adversarial_examples ensemble_methods neural_networks to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:538176a719c4/ [2406.11741] Transcendence: Generative Models Can Outperform The Experts That Train Them 2024-06-24T13:34:00+00:00 https://arxiv.org/abs/2406.11741 cshalizi to_read large_language_models_(so_called) ensemble_methods kakade.sham collective_cognition in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:81c40714a462/ [2303.00586] FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling 2024-03-06T15:18:30+00:00 https://arxiv.org/abs/2303.00586 cshalizi ensemble_methods algorithmic_fairness re:codename:one_law_for_the_lion_and_ox_is_oppression hooker.sara in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:905e0f62063f/ [2110.11216] User-friendly introduction to PAC-Bayes bounds 2024-03-02T19:25:18+00:00 https://arxiv.org/abs/2110.11216 cshalizi to:NB to_read learning_theory ensemble_methods to_teach:childs_garden_of_statistical_learning_theory alquier.pierre https://pinboard.in/ https://pinboard.in/u:cshalizi/b:27997bd5aeb9/ [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/ [2301.11562] Is My Prediction Arbitrary? The Confounding Effects of Variance in Fair Classification Benchmarks 2023-09-15T19:39:01+00:00 https://arxiv.org/abs/2301.11562 cshalizi algorithmic_fairness via:rvenkat classifiers have_read ensemble_methods uncertainty_for_neural_networks in_NB to_teach:data-mining https://pinboard.in/u:cshalizi/b:bec8057ed430/ [2202.02950] Jury Learning: Integrating Dissenting Voices into Machine Learning Models 2023-08-10T19:33:54+00:00 https://arxiv.org/abs/2202.02950 cshalizi to:NB to_read ensemble_methods text_mining networked_life social_life_of_the_mind via:henry_farrell https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dcc0ae80623f/ [2111.14377] Collective Intelligence for Deep Learning: A Survey of Recent Developments 2023-03-24T16:57:45+00:00 https://arxiv.org/abs/2111.14377 cshalizi to:NB neural_networks distributed_systems ensemble_methods complexity self-organization cellular_automata to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5fe97f6b6e1a/ [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/ Model Averaging Is Asymptotically Better Than Model Selection For Prediction 2022-07-19T13:59:00+00:00 https://jmlr.org/papers/v23/20-874.html cshalizi in_NB model_selection ensemble_methods regression to_teach:data-mining to_teach:childs_garden_of_statistical_learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9781f6f1983c/ [2206.04902] Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! 2022-06-13T17:40:28+00:00 https://arxiv.org/abs/2206.04902 cshalizi to:NB time_series prediction macroeconomics re:your_favorite_dsge_sucks ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b6484bcc0127/ Bagged Filters for Partially Observed Interacting Systems: Journal of the American Statistical Association: Vol 0, No 0 2022-06-11T04:57:01+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1974867 cshalizi to:NB time_series state_estimation state-space_models particle_filters ensemble_methods ionides.edward https://pinboard.in/ https://pinboard.in/u:cshalizi/b:737c28aede3c/ Model Averaging Is Asymptotically Better Than Model Selection For Prediction 2022-03-27T15:53:13+00:00 https://www.jmlr.org/papers/v23/20-874.html cshalizi to:NB ensemble_methods regression to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5b7a2e7ec1aa/ [2109.00173] FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes 2021-09-07T05:21:00+00:00 https://arxiv.org/abs/2109.00173 cshalizi to:NB prediction statistics ensemble_methods algorithmic_fairness kith_and_kin approved_the_thesis mishler.alan kennedy.edward_h. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9f7430fb6211/ [2107.04208] From Many to One: Consensus Inference in a MIP 2021-07-12T14:51:51+00:00 https://arxiv.org/abs/2107.04208 cshalizi to:NB ensemble_methods science_fiction cressie.noel statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8317ff977650/ [2002.05211] Bagged filters for partially observed interacting systems 2021-06-28T03:45:08+00:00 https://arxiv.org/abs/2002.05211 cshalizi to:NB state-space_models ensemble_methods particle_filters spatio-temporal_statistics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0b4c5b2a7b88/ [2106.05918] Bias, Consistency, and Alternative Perspectives of the Infinitesimal Jackknife 2021-06-24T20:42:42+00:00 https://arxiv.org/abs/2106.05918 cshalizi to:NB bootstrap confidence_sets ensemble_methods statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a62e1cc12b78/ [2008.07063] To Bag is to Prune 2021-06-10T02:10:01+00:00 https://arxiv.org/abs/2008.07063 cshalizi to:NB learning_theory ensemble_methods random_forests statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4e7116d33251/ [2106.02589] On Ensembling vs Merging: Least Squares and Random Forests under Covariate Shift 2021-06-08T13:55:10+00:00 https://arxiv.org/abs/2106.02589 cshalizi to:NB ensemble_methods prediction clustering statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c784d0848e5a/ [2106.02803] Network Estimation by Mixing: Adaptivity and More 2021-06-08T13:54:31+00:00 https://arxiv.org/abs/2106.02803 cshalizi to:NB network_data_analysis ensemble_methods to_read statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:06ee46836e84/ [2106.01092] Statistical optimality conditions for compressive ensembles 2021-06-07T03:51:56+00:00 https://arxiv.org/abs/2106.01092 cshalizi to:NB learning_theory random_projections ensemble_methods statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:18adca924829/ [2005.14458] Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression 2021-06-01T17:33:18+00:00 https://arxiv.org/abs/2005.14458 cshalizi to:NB density_estimation random_fields buhlmann.peter ensemble_methods statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:35774f99f672/ [2006.08855] RaSE: Random Subspace Ensemble Classification 2021-06-01T13:35:35+00:00 https://arxiv.org/abs/2006.08855 cshalizi to:NB ensemble_methods random_features statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d7e2672c3097/ [2101.11083] Tree boosting for learning probability measures 2021-05-30T21:09:35+00:00 https://arxiv.org/abs/2101.11083 cshalizi to:NB ensemble_methods density_estimation statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1d7502cf7e69/ [2009.09036] Causal Rule Ensemble: Interpretable Inference of Heterogeneous Treatment Effects 2021-05-30T20:43:38+00:00 https://arxiv.org/abs/2009.09036 cshalizi to:NB causal_inference causal_discovery ensemble_methods statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:43a67140d232/ AMF: Aggregated Mondrian forests for online learning - Mourtada - - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library 2021-05-20T13:53:05+00:00 https://doi.org/10.1111/rssb.12425 cshalizi to:NB to_read ensemble_methods random_forests regression classifiers to_teach:data-mining online_learning statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:42bd56d40bd2/ [2105.04134] Bagging cross-validated bandwidth selection in nonparametric regression estimation with applications to large-sized samples 2021-05-12T18:15:40+00:00 https://arxiv.org/abs/2105.04134 cshalizi to:NB cross-validation ensemble_methods bootstrap to_teach:data-mining kernel_smoothing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f2a3494ec6a3/ [2105.02569] Machine Collaboration 2021-05-10T22:48:51+00:00 https://arxiv.org/abs/2105.02569 cshalizi to:NB ensemble_methods prediction re:democratic_cognition to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d3a50065611d/ A NEW STUDY ON ASYMPTOTIC OPTIMALITY OF LEAST SQUARES MODEL AVERAGING | Econometric Theory | Cambridge Core 2021-04-21T16:08:41+00:00 https://www.cambridge.org/core/journals/econometric-theory/article/abs/new-study-on-asymptotic-optimality-of-least-squares-model-averaging/5252EEB71F41DCF4B613C02F1440D4A1 cshalizi to:NB model_averaging ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9cbc6fcccb0c/ [2103.05766] Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests 2021-03-21T18:40:32+00:00 https://arxiv.org/abs/2103.05766 cshalizi to:NB confidence_sets prediction statistics random_forests ensemble_methods data_mining to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fdba38e0badc/ Distilling the Wisdom of Crowds: Prediction Markets versus Prediction Polls | Academy of Management Proceedings 2021-03-01T07:53:19+00:00 https://journals.aom.org/doi/10.5465/ambpp.2015.15192abstract cshalizi to:NB collective_cognition ensemble_methods re:democratic_cognition via:henry_farrell https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b270f873ed06/ [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/ On Constraining Projections of Future Climate Using Observations and Simulations From Multiple Climate Models: Journal of the American Statistical Association: Vol 0, No 0 2021-01-27T15:05:40+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1851696 cshalizi to:NB climatology climate_change ensemble_methods prediction model_checking statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:390646258391/ [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/ Kriging Riemannian Data via Random Domain Decompositions: Journal of Computational and Graphical Statistics: Vol 0, No 0 2021-01-03T19:47:56+00:00 https://www.tandfonline.com/doi/full/10.1080/10618600.2020.1853548 cshalizi to:NB statistics_on_manifolds spatial_statistics ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:821c74f708d0/ [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/ [2012.11649] On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates 2020-12-23T02:22:20+00:00 https://arxiv.org/abs/2012.11649 cshalizi to:NB prediction ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:78ce8bc230bf/ [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/ [2011.07476] Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration 2020-11-18T17:19:44+00:00 https://arxiv.org/abs/2011.07476 cshalizi to:NB prediction ensemble_methods mechanism_design https://pinboard.in/ https://pinboard.in/u:cshalizi/b:74be020d03a3/ COVID-19 Projections Using Machine Learning | We take a data-driven approach rooted in epidemiology to forecast infections, deaths, and recovery timelines of the COVID-19 / coronavirus epidemic in the US and around the world 2020-04-30T00:17:40+00:00 https://covid19-projections.com/about/ cshalizi prediction epidemic_models coronavirus_pandemic_of_2019-- ensemble_methods to_teach:data_over_space_and_time via:carl_bergstrom https://pinboard.in/ https://pinboard.in/u:cshalizi/b:29b9c6fe79d5/ 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/ [1911.00190] 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/ [1910.11743] Boosting heritability: estimating the genetic component of phenotypic variation with multiple sample splitting 2019-10-29T14:29:28+00:00 https://arxiv.org/abs/1910.11743 cshalizi to:NB heritability statistics genetics ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:427e435cf974/ [1910.11445] Finite Mixtures of ERGMs for Ensembles of Networks 2019-10-29T14:28:44+00:00 https://arxiv.org/abs/1910.11445 cshalizi to:NB exponential_family_random_graphs ensemble_methods network_data_analysis statistics to_teach:baby-nets butts.carter_t. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:132d040c5832/ [1906.01235] Universal Boosting Variational Inference 2019-10-29T02:18:39+00:00 https://arxiv.org/abs/1906.01235 cshalizi to:NB density_estimation probability ensemble_methods computational_statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:68d19002e973/ The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence | SpringerLink 2019-10-24T15:58:28+00:00 https://link.springer.com/article/10.1007/s11023-019-09506-6 cshalizi to:NB anthropomorphism artificial_intelligence neural_networks ensemble_methods lasso rhetoric https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c79f17e90fab/ [1910.04743] The Implicit Regularization of Ordinary Least Squares Ensembles 2019-10-12T03:47:52+00:00 https://arxiv.org/abs/1910.04743 cshalizi to:NB ensemble_methods regression statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c6ad104d2722/ [1909.12299] ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts 2019-10-01T17:37:43+00:00 https://arxiv.org/abs/1909.12299 cshalizi to:NB neural_data_analysis fmri mixture_models statistics ensemble_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dffb8437bc01/ [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/ [1909.07578] Stacking Models for Nearly Optimal Link Prediction in Complex Networks 2019-09-25T03:26:04+00:00 https://arxiv.org/abs/1909.07578 cshalizi to:NB ensemble_methods network_data_analysis link_prediction statistics clauset.aaron airoldi.edo galstyan.aram https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9eaee2511c1c/ [1908.06951] Gradient Boosting Machine: A Survey 2019-08-20T14:23:05+00:00 https://arxiv.org/abs/1908.06951 cshalizi to:NB ensemble_methods boosting statistics machine_learning https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c32dc04478cc/ [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/ [1908.02718] A Characterization of Mean Squared Error for Estimator with Bagging 2019-08-08T13:06:15+00:00 https://arxiv.org/abs/1908.02718 cshalizi to:NB ensemble_methods prediction regression statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:28984b0de8c0/ [1908.02723] Advocacy Learning: Learning through Competition and Class-Conditional Representations 2019-08-08T12:57:47+00:00 https://arxiv.org/abs/1908.02723 cshalizi to:NB machine_learning collective_cognition ensemble_methods to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3f593a60f6e1/ [1908.01251] Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting 2019-08-06T14:47:00+00:00 https://arxiv.org/abs/1908.01251 cshalizi to:NB ensemble_methods computational_statistics statistics to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8e148c2e58df/ A universal method for evaluating the quality of aggregators 2019-08-03T23:06:47+00:00 http://journal.sjdm.org/19/190215/jdm190215.html cshalizi to:NB ensemble_methods statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bf2e20f601a6/ [1907.11452] An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems 2019-07-30T00:08:02+00:00 https://arxiv.org/abs/1907.11452 cshalizi to:NB information_theory bounded_rationality collective_cognition social_life_of_the_mind ensemble_methods re:democratic_cognition to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:44a695733c24/ [1907.08742] Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap 2019-07-24T14:00:32+00:00 https://arxiv.org/abs/1907.08742 cshalizi to:NB ensemble_methods computational_statistics statistics prediction to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e4f2433d5ca3/ [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/