Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi High-Order Flow Matching: Unified Framework and Sharp Statistical Rates | OpenReview 2026-01-30T12:12:17+00:00 https://openreview.net/forum?id=ib0aV2hphN cshalizi to:NB density_estimation neural_networks nonparametrics liu.han https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5033f0d08de2/ Density‐Valued ARMA Models by Spline Mixtures - Matsuda - Journal of Time Series Analysis - Wiley Online Library 2025-10-25T20:02:50+00:00 https://onlinelibrary.wiley.com/doi/full/10.1111/jtsa.70025?campaign=wolearlyview cshalizi to:NB splines time_series nonparametrics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2c29c195dd77/ On the rate of convergence of an over-parametrized deep neural network regression estimate with ReLU activation function learned by gradient descent 2025-10-24T19:40:42+00:00 https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-19/issue-2/On-the-rate-of-convergence-of-an-over-parametrized-deep/10.1214/25-EJS2444.full cshalizi to:NB regression neural_networks learning_theory nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:deab690bc7f0/ Nonparametric regression using deep neural networks with ReLU activation function 2025-08-07T14:59:12+00:00 https://projecteuclid.org/journals/annals-of-statistics/volume-48/issue-4/Nonparametric-regression-using-deep-neural-networks-with-ReLU-activation-function/10.1214/19-AOS1875.full cshalizi to:NB neural_networks regression nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bec55e957341/ [2203.06469] Semiparametric doubly robust targeted double machine learning: a review 2025-04-28T01:51:30+00:00 https://arxiv.org/abs/2203.06469 cshalizi causal_inference nonparametrics kith_and_kin kennedy.edward_h. re:ADAfaEPoV have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0dbbe6f7f177/ quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression 2025-03-02T14:59:23+00:00 https://journal.r-project.org/archive/2016/RJ-2016-052/RJ-2016-052.pdf cshalizi to:NB nonparametrics quantile_regression re:codename:catherine_wheel to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:18bdd27ba880/ [2502.04309] Targeted Learning for Data Fairness 2025-02-11T13:39:36+00:00 https://arxiv.org/abs/2502.04309 cshalizi to:NB algorithmic_fairness hooker.giles entropy_estimation nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:03e80943f40f/ Accounting for Individual-Specific Heterogeneity in Intergenerational Income Mobility | NBER 2025-01-21T02:07:46+00:00 https://www.nber.org/papers/w33349 cshalizi to:NB economics to_read nonparametrics transmission_of_inequality to_teach:statistics_of_inequality_and_discrimination durlauf.steven_n. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e3273025483b/ [2305.04116] The Fundamental Limits of Structure-Agnostic Functional Estimation 2024-12-11T19:37:38+00:00 https://arxiv.org/abs/2305.04116 cshalizi to:NB to_read statistics nonparametrics entropy_estimation kith_and_kin kennedy.edward_h. wasserman.larry balakrishnan.sivaraman causal_inference https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0ee8eb04a835/ [2402.16422] Bayesian nonparametric statistics, St-Flour lecture notes 2024-02-27T20:00:22+00:00 https://arxiv.org/abs/2402.16422 cshalizi to:NB nonparametrics bayesianism https://pinboard.in/ https://pinboard.in/u:cshalizi/b:88ac8b8a01b7/ [2309.17016] Efficient Agnostic Learning with Average Smoothness 2023-12-08T17:15:57+00:00 https://arxiv.org/abs/2309.17016 cshalizi in_NB nonparametrics learning_theory empirical_processes kith_and_kin kontorovich.aryeh hanneke.steve https://pinboard.in/ https://pinboard.in/u:cshalizi/b:372bb023a730/ Comparing Nonparametric Versus Parametric Regression Fits 2023-05-01T19:55:30+00:00 https://projecteuclid.org/journals/annals-of-statistics/volume-21/issue-4/Comparing-Nonparametric-Versus-Parametric-Regression-Fits/10.1214/aos/1176349403.full cshalizi regression goodness-of-fit model_checking nonparametrics bootstrap hardle.wolfgang re:ADAfaEPoV cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7023957251ed/ [2302.06005] Near-optimal learning with average Hölder smoothness 2023-02-24T03:27:08+00:00 https://arxiv.org/abs/2302.06005 cshalizi to:NB learning_theory nonparametrics kontorovich.aryeh hanneke.steve https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3454a701b130/ [2112.03626] Phase transitions in nonparametric regressions: a curse of exploiting higher degree smoothness assumptions in finite samples 2022-07-25T17:15:55+00:00 https://arxiv.org/abs/2112.03626 cshalizi in_NB regression nonparametrics minimax learning_theory statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e1ba3a57ef77/ [2008.02915] Kernel Ordinary Differential Equations 2022-02-15T14:55:41+00:00 https://arxiv.org/abs/2008.02915 cshalizi equations_of_motion_from_a_time_series dynamical_systems kernel_methods statistics nonparametrics splines in_NB have_skimmed https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8188454a9772/ When is TSLS Actually LATE? | NBER 2022-02-02T23:00:41+00:00 https://www.nber.org/papers/w29709 cshalizi instrumental_variables causal_inference nonparametrics re:ADAfaEPoV in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4f733f8f89d2/ [2107.07257] Nonparametric, tuning-free estimation of S-shaped functions 2021-08-13T16:14:59+00:00 https://arxiv.org/abs/2107.07257 cshalizi to:NB regression nonparametrics optimization samworth.richard_j. carroll.raymond_j. statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ed6d665b235e/ Spectrally-truncated kernel ridge regression and its free lunch 2021-07-22T16:38:51+00:00 https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-15/issue-2/Spectrally-truncated-kernel-ridge-regression-and-its-free-lunch/10.1214/21-EJS1873.full cshalizi to:NB nonparametrics regression kernel_methods hilbert_space statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:79a20beb303a/ Adaptive estimation of nonparametric functionals 2021-06-07T03:48:52+00:00 https://jmlr.org/papers/v22/19-892.html cshalizi to:NB causal_inference nonparametrics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ab3ddf8ce1b4/ Towards a Unified Analysis of Random Fourier Features 2021-06-07T02:23:38+00:00 https://jmlr.org/papers/v22/20-1369.html cshalizi to:NB to_read random_features nonparametrics kernel_methods statistics re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:eaf8d8877596/ [2106.01529] Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs 2021-06-07T02:19:49+00:00 https://arxiv.org/abs/2106.01529 cshalizi to:NB smoothing nonparametrics network_data_analysis regression kith_and_kin green.alden balakrishnan.sivaraman tibshirani.ryan statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5874c1e3bc39/ [2105.15197] A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees 2021-06-01T13:38:00+00:00 https://arxiv.org/abs/2105.15197 cshalizi to:NB confidence_sets nonparametrics to_read statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a2a745b5e206/ [2105.14075] Distribution-free inference for regression: discrete, continuous, and in between 2021-06-01T13:34:48+00:00 https://arxiv.org/abs/2105.14075 cshalizi to:NB regression confidence_sets nonparametrics barber.rina_foygel statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:31da98979df1/ [1904.00989] Counterfactual Sensitivity and Robustness 2021-05-18T18:00:43+00:00 https://arxiv.org/abs/1904.00989 cshalizi to:NB causal_inference nonparametrics sensitivity_analysis model_checking statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a58a88c92b64/ [2105.04754] Non-Parametric Estimation of Manifolds from Noisy Data 2021-05-12T18:21:01+00:00 https://arxiv.org/abs/2105.04754 cshalizi to:NB manifold_learning nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d2b1a4fadc3e/ [2012.13106] Dependence of variance on covariate design in nonparametric link regression 2021-05-12T18:12:37+00:00 https://arxiv.org/abs/2012.13106 cshalizi to:NB network_data_analysis regression nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:32e8bc8f0998/ [2104.10601] Statistical inference for generative adversarial networks 2021-04-22T15:20:39+00:00 https://arxiv.org/abs/2104.10601 cshalizi neural_networks confidence_sets nonparametrics your_favorite_deep_neural_network_sucks in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:6f7375fd36ee/ [2010.04855] Reproducing Kernel Methods for Nonparametric and Semiparametric Treatment Effects 2021-04-22T15:18:05+00:00 https://arxiv.org/abs/2010.04855 cshalizi to:NB causal_inference nonparametrics kernel_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:44a4baf7ac02/ [2104.09935] CATE meets ML - The Conditional Average Treatment Effect and Machine Learning 2021-04-21T19:55:02+00:00 https://arxiv.org/abs/2104.09935 cshalizi to:NB causal_inference nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c2795042cb03/ [1406.5362] Predicting the Future Behavior of a Time-Varying Probability Distribution 2021-04-21T19:54:13+00:00 https://arxiv.org/abs/1406.5362 cshalizi to:NB prediction probability hilbert_space nonparametrics re:AoS_project https://pinboard.in/ https://pinboard.in/u:cshalizi/b:aa1867d0c690/ [2004.14497] Optimal doubly robust estimation of heterogeneous causal effects 2021-04-21T19:44:40+00:00 https://arxiv.org/abs/2004.14497 cshalizi causal_inference nonparametrics heard_the_talk have_read in_NB kennedy.edward_h. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e1f9f232f51d/ Consistent Sparse Deep Learning: Theory and Computation: Journal of the American Statistical Association: Vol 0, No 0 2021-04-21T16:09:37+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1895175 cshalizi neural_networks nonparametrics sparsity bayesian_consistency in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:eaf86c3cae34/ [1802.08667] De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers 2021-04-14T14:48:58+00:00 https://arxiv.org/abs/1802.08667 cshalizi to:NB causal_inference sparsity nonparametrics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:56b3f2bb1d9f/ [1809.05224] Automatic Debiased Machine Learning of Causal and Structural Effects 2021-04-14T14:48:17+00:00 https://arxiv.org/abs/1809.05224 cshalizi to:NB causal_inference nonparametrics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:00926a601fb1/ Factorized Estimation of High‐Dimensional Nonparametric Covariance Models - Zhang - - Scandinavian Journal of Statistics - Wiley Online Library 2021-04-12T03:44:07+00:00 https://onlinelibrary.wiley.com/doi/10.1111/sjos.12529 cshalizi to:NB variance_estimation nonparametrics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d4c95ff04060/ [2101.10255] Consistent specification testing under spatial dependence 2021-01-26T05:47:28+00:00 https://arxiv.org/abs/2101.10255 cshalizi to:NB spatial_statistics misspecification statistics regression nonparametrics to_read to_teach:data_over_space_and_time https://pinboard.in/ https://pinboard.in/u:cshalizi/b:69cd6ce3b5cf/ [1806.05161] Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate 2021-01-22T20:14:51+00:00 https://arxiv.org/abs/1806.05161 cshalizi learning_theory nonparametrics minimax belkin.mikhail hsu.daniel to_read to_teach:childs_garden_of_statistical_learning_theory in_NB interpolation_aka_memorizing_the_training_data https://pinboard.in/ https://pinboard.in/u:cshalizi/b:14df95f530fb/ [1806.09471] Does data interpolation contradict statistical optimality? 2021-01-22T20:12:52+00:00 https://arxiv.org/abs/1806.09471 cshalizi learning_theory nonparametrics minimax belkin.mikhail rakhlin.alexander to_teach:childs_garden_of_statistical_learning_theory tsybakov.alexandre_b. have_read in_NB interpolation_aka_memorizing_the_training_data https://pinboard.in/ https://pinboard.in/u:cshalizi/b:299dc566e146/ [2101.04783] Variable bandwidth kernel regression estimation 2021-01-14T15:59:57+00:00 https://arxiv.org/abs/2101.04783 cshalizi to:NB smoothing regression nonparametrics kernel_smoothing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bbc254231a9c/ Extracting multistage screening rules from online dating activity data | PNAS 2021-01-07T21:14:16+00:00 https://www.pnas.org/content/113/38/10530 cshalizi to:NB decision-making statistics nonparametrics mixture_models practices_relating_to_the_transmission_of_genetic_information to_teach:undergrad-ADA via:gabriel_rossman feinberg.fred https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a8ecf81231c6/ [2101.01535] An RKHS-Based Semiparametric Approach to Nonlinear Sufficient Dimension Reduction 2021-01-06T17:10:25+00:00 https://arxiv.org/abs/2101.01535 cshalizi to:NB nonparametrics hilbert_space dimension_reduction statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4499fc48f267/ [2012.14482] Multivariate Smoothing via the Fourier Integral Theorem and Fourier Kernel 2021-01-03T20:13:12+00:00 https://arxiv.org/abs/2012.14482 cshalizi to:NB fourier_analysis smoothing computational_statistics density_estimation nonparametrics regression statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7b370340cb7a/ [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/ [2006.11937] Learning of Discrete Graphical Models with Neural Networks 2020-12-26T17:46:11+00:00 https://arxiv.org/abs/2006.11937 cshalizi to:NB graphical_models causal_discovery nonparametrics statistics neural_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:db060342bc93/ Gao , Ma : Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing 2020-12-21T14:11:01+00:00 https://projecteuclid.org/euclid.ss/1608541216 cshalizi to:NB network_data_analysis graph_limits hypothesis_testing minimax nonparametrics re:smoothing_adjacency_matrices community_discovery to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4146f1114577/ [2007.04286] Kernel-based Prediction of Non-Markovian Time Series 2020-12-17T20:50:05+00:00 https://arxiv.org/abs/2007.04286 cshalizi to:NB time_series regression prediction statistics nonparametrics kernel_smoothing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8dd8fb122f34/ unified approach to the calculation of information operators in semiparametric models | Biometrika | Oxford Academic 2020-12-17T01:41:19+00:00 https://academic.oup.com/biomet/article-abstract/107/4/983/5857283?redirectedFrom=fulltext cshalizi to:NB statistics nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fce82a81bbf6/ [2005.06371] Nonparametric regression for locally stationary random fields under stochastic sampling design 2020-12-16T17:47:23+00:00 https://arxiv.org/abs/2005.06371 cshalizi to:NB random_fields spatial_statistics spatio-temporal_statistics smoothing nonparametrics regression additive_models statistics to_teach:data_over_space_and_time kernel_smoothing https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9759167da0d4/ [2012.08444] Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression 2020-12-16T15:11:23+00:00 https://arxiv.org/abs/2012.08444 cshalizi to:NB network_data_analysis regression nonparametrics re:smoothing_adjacency_matrices to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:de76b10c1394/ [2012.03376] A Lecture About the Use of Orlicz Spaces in Information Geometry 2020-12-09T18:48:27+00:00 https://arxiv.org/abs/2012.03376 cshalizi to:NB statistics information_geometry nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d2b76a0b7bf0/ [2011.12215] Searching for Interactions: Why the Laplace Kernel is your Friend 2020-11-25T14:25:54+00:00 https://arxiv.org/abs/2011.12215 cshalizi to:NB sparsity nonparametrics statistics kernel_methods https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2c6e8e00596e/ [1712.00038] Augmented Minimax Linear Estimation 2020-11-23T17:43:08+00:00 https://arxiv.org/abs/1712.00038 cshalizi to:NB causal_inference nonparametrics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:26f6c8f0c2be/ System Identification: A Machine Learning Perspective | Annual Review of Control, Robotics, and Autonomous Systems 2020-11-19T05:21:49+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-control-053018-023744 cshalizi to:NB nonparametrics statistics learning_theory dynamical_systems to_teach:childs_garden_of_statistical_learning_theory learning_under_dependence https://pinboard.in/ https://pinboard.in/u:cshalizi/b:72d9379f7c5e/ Donnet , Rivoirard , Rousseau : Nonparametric Bayesian estimation for multivariate Hawkes processes 2020-11-18T22:50:20+00:00 https://projecteuclid.org/euclid.aos/1600480929 cshalizi to:NB point_processes functional_connectivity bayesian_consistency nonparametrics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4b84047a5538/ Cannings , Berrett , Samworth : Local nearest neighbour classification with applications to semi-supervised learning 2020-11-18T22:44:02+00:00 https://projecteuclid.org/euclid.aos/1594972839 cshalizi4ρ>4 (as well as other regularity conditions), a local choice of kk can yield a rate of convergence of the excess risk of O(n−4/(d+4))O(n−4/(d+4)), where nn is the sample size, whereas for the standard kk-nearest neighbour classifier, our theory would require d≥5d≥5 and ρ>4d/(d−4)ρ>4d/(d−4) finite moments to achieve this rate. These results motivate a new kk-nearest neighbour classifier for semi-supervised learning problems, where the unlabelled data are used to obtain an estimate of the marginal feature density, and fewer neighbours are used for classification when this density estimate is small. Our worst-case rates are complemented by a minimax lower bound, which reveals that the local, semi-supervised kk-nearest neighbour classifier attains the minimax optimal rate over our classes for the excess risk, up to a subpolynomial factor in nn. These theoretical improvements over the standard kk-nearest neighbour classifier are also illustrated through a simulation study."]]> classifiers nearest_neighbors statistics nonparametrics samworth.richard_j. in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:357925370f16/ Nickl , Ray : Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions 2020-11-18T21:47:49+00:00 https://projecteuclid.org/euclid.aos/1594972822 cshalizi to:NB stochastic_differential_equations nonparametrics statistical_inference_for_stochastic_processes statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c8f20a18002d/ Liang , Rakhlin : Just interpolate: Kernel “Ridgeless” regression can generalize 2020-11-18T21:46:27+00:00 https://projecteuclid.org/euclid.aos/1594972820 cshalizi kernel_methods regression nonparametrics learning_theory statistics rakhlin.alexander to_teach:childs_garden_of_statistical_learning_theory in_NB interpolation_aka_memorizing_the_training_data https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5e5a05c937da/ [2011.07275] Inference Functions for Semiparametric Models 2020-11-18T17:21:55+00:00 https://arxiv.org/abs/2011.07275 cshalizi to:NB nonparametrics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4ff47a06a003/ Liu , Shang , Cheng : Nonparametric distributed learning under general designs 2020-11-16T16:22:30+00:00 https://projecteuclid.org/euclid.ejs/1597975224 cshalizi to:NB statistics distributed_systems regression nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bf1d42c85277/ Kim , Lee , Lei : Global and local two-sample tests via regression 2020-11-16T16:11:48+00:00 https://projecteuclid.org/euclid.ejs/1576573369 cshalizi to:NB two-sample_tests nonparametrics high-dimensional_statistics regression kith_and_kin lee.ann_b. lei.jing heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d95b8656b5cd/ <italic>k</italic>-Vectors: An Alternating Minimization Algorithm for Learning Regression Functions - IEEE Journals & Magazine 2020-11-16T16:08:36+00:00 https://ieeexplore.ieee.org/document/9136791 cshalizi to:NB clustering regression nonparametrics statistics k-means nearest_neighbors to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fd9acdc3e960/ [1910.00943] A note on the consistency of the random forest algorithm 2020-01-12T23:01:47+00:00 https://arxiv.org/abs/1910.00943 cshalizi to:NB random_forests regression statistics nonparametrics to_teach:data-mining color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:be94315284cc/ [1908.03606] Goodness-of-fit testing in high-dimensional generalized linear models 2020-01-12T22:26:07+00:00 https://arxiv.org/abs/1908.03606 cshalizi to:NB goodness-of-fit regression statistics linear_regression nonparametrics buhlmann.peter to_read re:ADAfaEPoV misspecification samworth.richard_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1d17667d900b/ Kleijn , Zhao : Criteria for posterior consistency and convergence at a rate 2019-12-01T22:58:13+00:00 https://projecteuclid.org/euclid.ejs/1574240425 cshalizi to:NB statistics bayesian_consistency nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dba2c5a15af5/ [1807.05748] Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching 2019-11-10T21:46:11+00:00 https://arxiv.org/abs/1807.05748 cshalizi to:NB stochastic_differential_equations statistical_inference_for_stochastic_processes gaussian_processes statistics nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5db7dca15d4e/ Veitch , Roy : Sampling and estimation for (sparse) exchangeable graphs 2019-11-01T00:50:42+00:00 https://projecteuclid.org/euclid.aos/1572487393 cshalizi to:NB graph_limits network_data_analysis graphons nonparametrics statistics veitch.victor to_teach:graphons https://pinboard.in/ https://pinboard.in/u:cshalizi/b:16e640f46676/ [1907.02306] Consistent Regression using Data-Dependent Coverings 2019-10-29T14:34:19+00:00 https://arxiv.org/abs/1907.02306 cshalizi to:NB statistics regression nonparametrics to_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:37c6b7b378f3/ [1603.07632] Statistical inference in sparse high-dimensional additive models 2019-10-22T13:49:54+00:00 https://arxiv.org/abs/1603.07632 cshalizi additive_models statistics regression nonparametrics sparsity in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:254865861d99/ [1910.08477] Density estimation on an unknown submanifold 2019-10-21T15:51:21+00:00 https://arxiv.org/abs/1910.08477 cshalizi to:NB density_estimation statistics_on_manifolds statistics nonparametrics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5cf29a2b288f/ [1910.06028] Accuracy of Gaussian approximation in nonparametric Bernstein -- von Mises Theorem 2019-10-16T14:07:23+00:00 https://arxiv.org/abs/1910.06028 cshalizi to:NB bayesian_consistency nonparametrics statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:dca1f764b77f/