Pinboard (cshalizi)

Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi [2401.15800] Statistical Significance of Feature Importance Rankings 2025-03-31T23:37:55+00:00 https://arxiv.org/abs/2401.15800 cshalizi to:NB statistics variable_selection hooker.giles https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a0e052646ae3/ [2309.10140] A Geometric Framework for Neural Feature Learning 2023-12-08T14:18:57+00:00 https://arxiv.org/abs/2309.10140 cshalizi to:NB information_geometry variable_selection neural_networks statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:47874c9b2e08/ LASSO-driven inference in time and space 2021-08-10T14:07:16+00:00 https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-3/LASSO-driven-inference-in-time-and-space/10.1214/20-AOS2019.short cshalizi to:NB lasso sparsity regression time_series spatial_statistics variable_selection statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:86f9e897007b/ [1911.01850] Stabilizing Variable Selection and Regression 2021-05-24T22:54:41+00:00 https://arxiv.org/abs/1911.01850 cshalizi to:NB variable_selection regression prediction statistics generalizability buhlmann.peter https://pinboard.in/ https://pinboard.in/u:cshalizi/b:92e2cf839bf8/ [2101.01603] Comparing methods addressing multi-collinearity when developing prediction models 2021-01-06T16:51:35+00:00 https://arxiv.org/abs/2101.01603 cshalizi to:NB linear_regression variable_selection re:TALR https://pinboard.in/ https://pinboard.in/u:cshalizi/b:91c2d6d885ca/ [2012.04171] Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization 2021-01-03T19:48:45+00:00 https://arxiv.org/abs/2012.04171 cshalizi to:NB variable_selection sparsity factor_analysis additive_models statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c88603e68cdb/ Baker , Tang , Allen : Feature selection for data integration with mixed multiview data 2020-12-19T16:42:14+00:00 https://projecteuclid.org/euclid.aoas/1608346892 cshalizi to:NB variable_selection lasso sparsity statistics allen.genevera_i. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c18ec82da65c/ [2011.12154] Identifying important predictors in large data bases -- multiple testing and model selection 2020-11-25T14:26:20+00:00 https://arxiv.org/abs/2011.12154 cshalizi n are introduced and compared with a variety of penalized likelihood methods, in particular SLOPE and SLOBE. The focus is on methods which control the FDR in terms of model identification. Theoretical results are provided both with respect to model identification and prediction and various simulation results are presented which illustrate the performance of the different methods in different situations."]]> to:NB multiple_testing variable_selection high-dimensional_statistics statistics regression https://pinboard.in/ https://pinboard.in/u:cshalizi/b:04b56518e578/ [2002.06060] Causality in cognitive neuroscience: concepts, challenges, and distributional robustness 2020-08-20T15:50:02+00:00 https://arxiv.org/abs/2002.06060 cshalizi to:NB to_read causal_inference statistics variable_selection neuroscience peters.jonas methodological_advice https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4b63620b847f/ High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking | SpringerLink 2020-02-23T15:45:43+00:00 https://link.springer.com/article/10.1007/s11222-019-09914-9 cshalizi to:NB regression prediction statistics high-dimensional_statistics lasso to_teach:linear_models re:TALR variable_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0959893a9ed3/ [1910.14212] Sobolev Independence Criterion 2019-11-11T20:03:54+00:00 https://arxiv.org/abs/1910.14212 cshalizi to:NB dependence_measures statistics variable_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:62d8f23fed2e/ [1910.12327] A simple measure of conditional dependence 2019-10-29T02:24:47+00:00 https://arxiv.org/abs/1910.12327 cshalizi to:NB dependence_measures statistics variable_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:aa7ecdf18065/ [1910.00174] Randomized Ablation Feature Importance 2019-10-02T15:21:03+00:00 https://arxiv.org/abs/1910.00174 cshalizi to:NB prediction variable_selection statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2e63850756a8/ IPAD: Stable Interpretable Forecasting with Knockoffs Inference: Journal of the American Statistical Association: Vol 0, No 0 2019-09-18T12:36:47+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1654878 cshalizi to:NB statistics regression high-dimensional_statistics factor_analysis variable_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c602c00b2afc/ [1612.08468] Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models 2019-08-21T13:12:57+00:00 https://arxiv.org/abs/1612.08468 cshalizi to:NB variable_selection visual_display_of_quantitative_information statistics regression https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9eb5cd7d3c48/ High-Dimensional Adaptive Minimax Sparse Estimation With Interactions - IEEE Journals & Magazine 2019-08-20T15:51:54+00:00 https://ieeexplore.ieee.org/document/8700269 cshalizi to:NB statistics high-dimensional_statistics regression sparsity variable_selection linear_regression https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e3bf750aafd7/ [1507.03133] Best Subset Selection via a Modern Optimization Lens 2019-08-20T14:48:29+00:00 https://arxiv.org/abs/1507.03133 cshalizi to:NB optimization variable_selection statistics via:tslumley https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cd5299ec8dca/ [1907.07384] Feature Selection via Mutual Information: New Theoretical Insights 2019-07-18T23:27:29+00:00 https://arxiv.org/abs/1907.07384 cshalizi variable_selection information_theory statistics to_teach:data-mining in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1e47f8cec091/ [1906.01990] A Model-free Approach to Linear Least Squares Regression with Exact Probabilities and Applications to Covariate Selection 2019-06-06T13:45:37+00:00 https://arxiv.org/abs/1906.01990 cshalizi linear_regression regression statistics variable_selection in_NB color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bb93af1c2633/ [1811.00645] The Holdout Randomization Test: Principled and Easy Black Box Feature Selection 2019-05-30T16:04:56+00:00 https://arxiv.org/abs/1811.00645 cshalizi cross-validation variable_selection statistics blei.david have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e4ed13c6dd3c/ [1905.10573] Selective inference after variable selection via multiscale bootstrap 2019-05-28T16:46:26+00:00 https://arxiv.org/abs/1905.10573 cshalizi variable_selection post-selection_inference statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:801500782dac/ [1801.03896] Robust inference with knockoffs 2018-09-13T16:38:52+00:00 https://arxiv.org/abs/1801.03896 cshalizi regression variable_selection statistics samworth.richard_j. knockoffs to_teach:linear_models in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:888d5db086e8/ Oracle M-Estimation for Time Series Models - Giurcanu - 2016 - Journal of Time Series Analysis - Wiley Online Library 2017-04-04T13:16:54+00:00 http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12221/abstract cshalizi bootstrap time_series statistics estimation in_NB sparsity variable_selection high-dimensional_statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7728d02c1d9a/ [0906.4391] KNIFE: Kernel Iterative Feature Extraction 2016-11-30T02:04:49+00:00 https://arxiv.org/abs/0906.4391 cshalizi statistics regression variable_selection data_mining to_teach:data-mining kernel_methods in_NB heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:061ce2697602/ [1507.05315] Confidence Sets Based on the Lasso Estimator 2015-08-05T15:13:50+00:00 http://arxiv.org/abs/1507.05315 cshalizi lasso regression confidence_sets model_selection variable_selection statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:800bb38e54a8/ [1406.0052] Variable selection in high-dimensional additive models based on norms of projections 2014-07-12T00:22:46+00:00 http://arxiv.org/abs/1406.0052 cshalizi additive_models variable_selection hilbert_space statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5e6c4de3a03e/ [1404.2007] A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection 2014-04-20T18:12:43+00:00 http://arxiv.org/abs/1404.2007 cshalizi variable_selection model_selection lasso high-dimensional_statistics nobel.andrew statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f3a8dfd23a3c/ [1403.7063] A Significance Test for Covariates in Nonparametric Regression 2014-04-03T18:29:39+00:00 http://arxiv.org/abs/1403.7063 cshalizi variable_selection hypothesis_testing statistics nonparametrics regression to_teach:undergrad-ADA in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d2e07646c1a5/ [1403.7023] Worst possible sub-directions in high-dimensional models 2014-04-01T21:19:01+00:00 http://arxiv.org/abs/1403.7023 cshalizi to:NB high-dimensional_statistics lasso sparsity variable_selection statistics van_de_geer.sara https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d88ae59edb90/ [1403.4296] Inference for feature selection using the Lasso with high-dimensional data 2014-03-22T19:27:24+00:00 http://arxiv.org/abs/1403.4296 cshalizi lasso regression variable_selection re:what_is_the_right_null_model_for_linear_regression high-dimensional_statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5559a8fb3f09/ [1403.4544] On the Sensitivity of the Lasso to the Number of Predictor Variables 2014-03-21T15:52:20+00:00 http://arxiv.org/abs/1403.4544 cshalizi lasso regression variable_selection high-dimensional_statistics cross-validation statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c0b013c52c01/ [1401.8097] An Algorithm for Nonlinear, Nonparametric Model Choice and Prediction 2014-02-03T20:29:28+00:00 http://arxiv.org/abs/1401.8097 cshalizi model_selection regression nonparametrics variable_selection statistics hall.peter in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cd1695d5b681/ [1312.1706] Swapping Variables for High-Dimensional Sparse Regression from Correlated Measurements 2014-01-02T18:27:15+00:00 http://arxiv.org/abs/1312.1706 cshalizi to:NB high-dimensional_statistics lasso sparsity variable_selection statistics vats.divyanshu https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c1912ea2ab6c/ [1312.1473] Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models 2013-12-26T00:33:14+00:00 http://arxiv.org/abs/1312.1473 cshalizi lasso time_series variable_selection statistics re:your_favorite_dsge_sucks in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7fcc3eb7d15b/ [1312.5556] Hierarchical Testing in the High-Dimensional Setting with Correlated Variables 2013-12-23T16:31:50+00:00 http://arxiv.org/abs/1312.5556 cshalizi to:NB to_read high-dimensional_statistics variable_selection buhlmann.peter hierarchical_statistical_models hierarchical_structure https://pinboard.in/ https://pinboard.in/u:cshalizi/b:332c500f5bc7/ [1310.4887] Variable Selection Inference for Bayesian Additive Regression Trees 2013-10-23T14:26:06+00:00 http://arxiv.org/abs/1310.4887 cshalizi statistics high-dimensional_statistics variable_selection regression kith_and_kin jensen.shane george.ed in_NB gene_expression_data_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cb14a0bcfae3/ [1309.2068] Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models 2013-09-10T18:24:51+00:00 http://arxiv.org/abs/1309.2068 cshalizi cross-validation lasso regression statistics high-dimensional_statistics variable_selection in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:809b6dd19675/ The Steep Price of Sparsity « Normal Deviate 2013-07-29T19:52:06+00:00 http://normaldeviate.wordpress.com/2013/07/27/the-steep-price-of-sparsity/ cshalizimodel_selection track_down_references statistics sparsity variable_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1185172426ca/ [1306.6557] Optimal Feature Selection in High-Dimensional Discriminant Analysis 2013-06-30T03:40:07+00:00 http://arxiv.org/abs/1306.6557 cshalizi classifiers high-dimensional_statistics sparsity variable_selection statistics liu.han kolar.mladen in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b9f4ee25efd5/ [1306.5505] Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression 2013-06-27T15:19:31+00:00 http://arxiv.org/abs/1306.5505 cshalizi to:NB lasso regression variable_selection high-dimensional_statistics statistics estimation yu.bin https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c95bb73aab6b/ [1304.5678] Analytic Feature Selection for Support Vector Machines 2013-04-23T22:31:26+00:00 http://arxiv.org/abs/1304.5678 cshalizi to:NB variable_selection data_mining to_teach:data-mining text_mining classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c6d333cf0207/ [1304.5245] Feature Elimination in empirical risk minimization and support vector machines 2013-04-22T17:21:47+00:00 http://arxiv.org/abs/1304.5245 cshalizi to:NB variable_selection classifiers learning_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8abc27f2cd48/ [0906.4391] KNIFE: Kernel Iterative Feature Extraction 2012-11-03T15:56:41+00:00 http://arxiv.org/abs/0906.4391 cshalizi to:NB statistics machine_learning allen.genevera_i. regression classifiers kernel_methods to_teach:data-mining to_teach:undergrad-ADA have_read variable_selection https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fce2678790d0/ Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR 2012-09-30T13:44:42+00:00 http://people.ee.duke.edu/~lcarin/OSCAR.pdf cshalizi to:NB regression variable_selection statistics via:ryantibs https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3d45c29a3a2b/ [1208.2572] Nonparametric sparsity and regularization 2012-09-04T02:09:59+00:00 http://arxiv.org/abs/1208.2572 cshalizi to:NB to_read nonparametrics regression variable_selection sparsity statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5404e1979240/ [1205.6843] Significance Testing and Group Variable Selection 2012-06-23T15:17:19+00:00 http://arxiv.org/abs/1205.6843 cshalizi variable_selection model_selection regression nonparametrics to_teach:undergrad-ADA in_NB kernel_smoothing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1e1c49d015a0/ [1206.2696] Flexible Variable Selection for Recovering Sparsity in Nonadditive Nonparametric Models 2012-06-23T15:09:41+00:00 http://arxiv.org/abs/1206.2696 cshalizi to:NB regression nonparametrics additive_models variable_selection sparsity statistics to_read to_teach:undergrad-ADA https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7b43e31d14f3/ A Confidence Region Approach to Tuning for Variable Selection - Journal of Computational and Graphical Statistics - Volume 21, Issue 2 2012-06-23T14:56:55+00:00 http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.679890 cshalizi to:NB variable_selection regression statistics confidence_sets lasso https://pinboard.in/ https://pinboard.in/u:cshalizi/b:69f5fef13f87/ Variable selection with error control: another look at stability selection - Shah - 2012 - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library 2012-06-23T14:30:40+00:00 http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2011.01034.x/abstract cshalizi to:NB variable_selection statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6b054752ae8b/ [1206.4682] Copula-based Kernel Dependency Measures 2012-06-23T14:19:27+00:00 http://arxiv.org/abs/1206.4682 cshalizi information_theory entropy_estimation poczos.barnabas variable_selection machine_learning copulas kernel_methods in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:617bbe4e33dd/ [1206.4680] Fast Prediction of New Feature Utility 2012-06-23T14:18:26+00:00 http://arxiv.org/abs/1206.4680 cshalizi machine_learning prediction regression classifiers variable_selection have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3461e24caec3/ [0801.1158] Hierarchical selection of variables in sparse high-dimensional regression 2012-06-17T21:26:12+00:00 http://arxiv.org/abs/0801.1158 cshalizi to:NB variable_selection high-dimensional_statistics regression statistics re:what_is_the_right_null_model_for_linear_regression bickel.peter_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:045bc9afe3b3/ [1205.6761] Nonparametric Model Checking and Variable Selection 2012-06-07T16:02:38+00:00 http://arxiv.org/abs/1205.6761 cshalizi variable_selection regression nonparametrics statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5b928ac92a1f/ Feature Selection via Dependence Maximization 2012-06-07T15:59:35+00:00 http://jmlr.csail.mit.edu/papers/v13/song12a.html cshalizi to:NB variable_selection hilbert_space machine_learning information_theory https://pinboard.in/ https://pinboard.in/u:cshalizi/b:93feda6cd8e0/ [1206.1024] Conditional Sure Independence Screening 2012-06-07T15:41:24+00:00 http://arxiv.org/abs/1206.1024 cshalizi to:NB variable_selection high-dimensional_statistics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4b61d8167ea5/ [0805.1179] Autoregressive Process Modeling via the Lasso Procedure 2012-03-04T17:12:03+00:00 http://arxiv.org/abs/0805.1179 cshalizi time_series statistics lasso sparsity variable_selection kith_and_kin heard_the_talk rinaldo.alessandro nardi.yuval in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:519c50513386/ [1102.3616] Tight conditions for consistent variable selection in high dimensional nonparametric regression 2012-02-27T00:02:40+00:00 http://arxiv.org/abs/1102.3616 cshalizi regression variable_selection nonparametrics statistics in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8b693691cb40/ Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection 2012-02-10T18:10:39+00:00 http://jmlr.csail.mit.edu/papers/v13/brown12a.html cshalizi information_theory statistics variable_selection model_selection to_teach:data-mining to:blog machine_learning classifiers have_read in_NB graphical_models https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3584eb0c3974/ Nonparametric estimation of the link function including variable selection - Gerhard Tutz and Sebastian Petry - Statistics and Computing, Volume 22, Number 2 2011-12-01T12:54:28+00:00 http://www.springerlink.com/content/582v131176130h06/ cshalizi regression variable_selection statistics nonparametrics to_read to_teach:undergrad-ADA in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6413527c3ed3/ Variance estimation using refitted cross-validation in ultrahigh dimensional regression - Fan - 2011 - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library 2011-10-10T12:16:53+00:00 http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2011.01005.x/abstract cshalizi statistics regression variable_selection cross-validation estimation fan.jianqing variance_estimation in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f86a2929f2a4/ A Framework for Unbiased Model Selection Based on Boosting 2011-07-12T17:40:02+00:00 http://pubs.amstat.org/doi/abs/10.1198/jcgs.2011.09220 cshalizimodel_selection variable_selection boosting ensemble_methods statistics https://pinboard.in/u:cshalizi/b:6492001fa93c/ Statistics for High-Dimensional Data 2011-07-05T13:18:31+00:00 http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-642-20191-2?cm_mmc=NBA-_-Jul-11_WEST_8259992-_-product-_-978-3-642-20191-2 cshalizistatistics machine_learning variable_selection lasso sparsity buhlmann.peter van_de_geer.sara books:recommended books:owned https://pinboard.in/u:cshalizi/b:71f041b9a3fb/ [1106.5242] High Dimensional Sparse Econometric Models: An Introduction 2011-06-28T14:57:54+00:00 http://arxiv.org/abs/1106.5242 cshalizi regression lasso variable_selection econometrics https://pinboard.in/u:cshalizi/b:a5ec335e85a0/ Radchenko , James : Improved variable selection with Forward-Lasso adaptive shrinkage 2011-03-23T13:40:05+00:00 http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1300715197 cshalizilasso sparsity regression variable_selection model_selection to:NB https://pinboard.in/u:cshalizi/b:7aef4dc9dfd2/ [1009.2302] The Predictive Lasso 2010-09-16T01:54:03+00:00 http://arxiv.org/abs/1009.2302 cshalizi regression lasso variable_selection sparsity information_theory statistics https://pinboard.in/u:cshalizi/b:9bcc63589a6c/ "Partial Generalized Additive Models: An Information-Theoretic Approach for Dealing With Concurvity and Selecting Variables" (Gu, Kenny, Zhu, 2010) 2010-09-16T01:52:49+00:00 http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.07139 cshalizi regression additive_models information_theory variable_selection statistics https://pinboard.in/u:cshalizi/b:92d9d94d50d9/ Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions 2010-02-05T05:28:31+00:00 http://jmlr.csail.mit.edu/papers/v11/aliferis10b.html cshalizigraphical_models causal_inference variable_selection classifiers machine_learning statistics https://pinboard.in/u:cshalizi/b:9ad623c602b8/ Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation 2010-02-05T05:28:13+00:00 http://jmlr.csail.mit.edu/papers/v11/aliferis10a.html cshalizigraphical_models causal_inference variable_selection classifiers machine_learning statistics https://pinboard.in/u:cshalizi/b:6bef18f55d93/ [0909.0844] High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning 2009-09-15T17:59:15+00:00 http://arxiv.org/abs/0909.0844 cshalizivariable_selection regression kernel_methods statistics machine_learning in_NB https://pinboard.in/u:cshalizi/b:59c474a0ed79/ [0909.1308] Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling 2009-09-08T11:22:28+00:00 http://arxiv.org/abs/0909.1308 cshalizimachine_learning sparsity random_fields conditional_random_fields to:NB variable_selection graphical_models https://pinboard.in/u:cshalizi/b:7c173069e09d/