Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi Vector Media 2026-03-10T10:58:15+00:00 https://www.upress.umn.edu/9781517921675/vector-media/ cshalizi to:NB books:noted neural_networks large_language_models_(so_called) generative_diffusion_models heard_the_talk color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7ba58da0e6ae/ [cond-mat/0102181] Regularities Unseen, Randomness Observed: Levels of Entropy Convergence 2026-03-02T18:38:10+00:00 https://arxiv.org/abs/cond-mat/0102181 cshalizi in_NB information_theory computational_mechanics prediction have_read heard_the_talk heard_the_talk_a_quarter_century_ago_in_fact kith_and_kin crutchfield.james_p. feldman.david_p. probability https://pinboard.in/ https://pinboard.in/u:cshalizi/b:90869a348890/ Large language model validity via enhanced conformal prediction methods 2025-04-23T14:47:22+00:00 https://proceedings.neurips.cc/paper_files/paper/2024/hash/d02ff1aeaa5c268dc34790dd1ad21526-Abstract-Conference.html cshalizi in_NB conformal_prediction large_language_models_(so_called) heard_the_talk cherian.john to_teach:statistics_and_generative_ai candes.emmanuel_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fc1ac2262c40/ How do we know how smart AI systems are? | Science 2025-03-17T00:34:41+00:00 https://www.science.org/doi/10.1126/science.adj5957 cshalizimitchell.melanie artificial_intelligence large_language_models_(so_called) embers_of_autoregression have_read kith_and_kin heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ca2836374b16/ [2411.14215] Evaluating the Robustness of Analogical Reasoning in Large Language Models 2024-12-09T21:33:51+00:00 https://arxiv.org/abs/2411.14215 cshalizi heard_the_talk analogy large_language_models_(so_called) mitchell.melanie kith_and_kin embers_of_autoregression in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f6e22b45f064/ [2205.15680] Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems 2024-08-21T17:34:47+00:00 https://arxiv.org/abs/2205.15680 cshalizi confidence_sets simulation-based_inference heard_the_talk kith_and_kin in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f7483a54c021/ [2206.06421] Repro Samples Method for Finite- and Large-Sample Inferences 2023-10-04T17:51:27+00:00 https://arxiv.org/abs/2206.06421 cshalizi heard_the_talk confidence_sets simulation-based_inference statistics re:codename:catherine_wheel in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:72263b788043/ [2102.10717] Abstraction and Analogy-Making in Artificial Intelligence 2023-06-29T15:38:15+00:00 https://arxiv.org/abs/2102.10717 cshalizi in_NB analogy artificial_intelligence kith_and_kin mitchell.melanie heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:77d900ff481c/ Communication Norms and the Collective Cognitive Performance of “Invisible Colleges” | SpringerLink 2023-05-10T02:44:02+00:00 https://link.springer.com/chapter/10.1007/978-3-662-03738-6_7 cshalizi in_NB collective_cognition sociology_of_science david.paul_a. science_as_a_social_process random_fields voter_model heard_the_talk cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f2dd9bd2f1fb/ Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences1 | American Journal of Sociology: Vol 110, No 4 2022-04-13T12:35:21+00:00 https://www.journals.uchicago.edu/doi/abs/10.1086/421508 cshalizi in_NB sociology social_networks network_formation white.douglas_r. kith_and_kin heard_the_talk have_read to_teach:baby-nets https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b4de8caddc75/ Philosophies | Free Full-Text | On Falsifiable Statistical Hypotheses 2022-04-13T02:27:19+00:00 https://www.mdpi.com/2409-9287/7/2/40 cshalizi to:NB philosophy_of_science falsifiability probability statistics foundations_of_statistics genin.konstantin heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ad26e3ac86f0/ The computational philosophy: simulation as a core philosophical method | SpringerLink 2022-02-14T13:33:09+00:00 https://link.springer.com/article/10.1007/s11229-020-02950-3 cshalizi to:NB simulation philosophy mayo-wilson.conor zollman.kevin heard_the_talk via:rvenkat https://pinboard.in/ https://pinboard.in/u:cshalizi/b:55e5def7b4d2/ [2107.00248] Randomization-only Inference in Experiments with Interference 2021-10-25T20:04:21+00:00 https://arxiv.org/abs/2107.00248 cshalizi to:NB have_skimmed heard_the_talk experimental_design experiments_on_networks network_data_analysis statistics kith_and_kin to_read choi.david_s. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4d7e366be396/ Consistent Estimation of Number of Communities in Stochastic Block Models using Cross‐Validation - Qin - - Stat - Wiley Online Library 2021-10-11T19:50:46+00:00 https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.426 cshalizi to:NB stochastic_block_models community_discovery cross-validation network_data_analysis kith_and_kin lei.jing heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:23e1965abcdf/ Causal discovery in heavy-tailed models 2021-08-10T14:07:45+00:00 https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-3/Causal-discovery-in-heavy-tailed-models/10.1214/20-AOS2021.short cshalizi to:NB heard_the_talk heavy_tails causal_inference causal_discovery statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2b38a05b49f0/ [2006.06138] Conformal Inference of Counterfactuals and Individual Treatment Effects 2021-05-13T05:25:32+00:00 https://arxiv.org/abs/2006.06138 cshalizi causal_inference confidence_sets conformal_prediction in_NB heard_the_talk candes.emmanuel_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:90982831f385/ [2104.12871] Why AI is Harder Than We Think 2021-04-29T15:02:42+00:00 https://arxiv.org/abs/2104.12871 cshalizi have_read heard_the_talk artificial_intelligence kith_and_kin mitchell.melanie in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9e2c3fff728b/ [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/ Conor Mayo-Wilson, Kevin J. S. Zollman & David Danks, The Independence Thesis: When Individual and Social Epistemology Diverge - PhilPapers 2021-04-16T15:03:00+00:00 https://philpapers.org/rec/MAYTIT cshalizi to:NB heard_the_talk collective_cognition philosophy_of_science rationality mayo-wilson.conor danks.david kith_and_kin zollman.kevin_j._s. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7d1aed47a5fe/ Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data | NBER 2021-04-11T03:31:25+00:00 https://www.nber.org/papers/w23673 cshalizi to:NB data_mining macroeconomics ng.serena heard_the_talk to_read re:your_favorite_dsge_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:59ca1ea7aed8/ Kim , Ramdas , Singh , Wasserman : Classification accuracy as a proxy for two-sample testing 2021-02-04T15:31:39+00:00 https://projecteuclid.org/euclid.aos/1611889233 cshalizi0ϵ>0 as d,n→∞d,n→∞, then (a) the permutation-based test is consistent (has power approaching to one), (b) a computationally efficient test based on a Gaussian approximation of the null distribution is also consistent. To get a finer understanding of the rates of consistency, we study a specialized setting of distinguishing Gaussians with mean-difference δδ and common (known or unknown) covariance ΣΣ, when d/n→c∈(0,∞)d/n→c∈(0,∞). We study variants of Fisher’s linear discriminant analysis (LDA) such as “naive Bayes” in a nontrivial regime when ϵ→0ϵ→0 (the Bayes classifier has true accuracy approaching 1/2), and contrast their power with corresponding variants of Hotelling’s test. Surprisingly, the expressions for their power match exactly in terms of nn, dd, δδ, ΣΣ, and the LDA approach is only worse by a constant factor, achieving an asymptotic relative efficiency (ARE) of 1/π‾‾√1/π for balanced samples. We also extend our results to high-dimensional elliptical distributions with finite kurtosis. Other results of independent interest include minimax lower bounds, and the optimality of Hotelling’s test when d=o(n)d=o(n). Simulation results validate our theory, and we present practical takeaway messages along with natural open problems."]]> to:NB hypothesis_testing two-sample_tests classifiers high-dimensional_statistics heard_the_talk kith_and_kin singh.aarti wasserman.larry ramdas.aaditya https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9a8de542290c/ Injustice: Political Theory for the Real World - Oxford Scholarship 2021-01-16T08:02:00+00:00 https://doi.org/10.1093/oso/9780190692421.001.0001 cshalizi to:NB books:noted moral_philosophy political_philosophy heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0d0ee8ff565a/ The potential of likelihood-free inference of cosmological parameters with weak lensing data | Proceedings of the International Astronomical Union | Cambridge Core 2020-12-13T23:58:29+00:00 https://www.cambridge.org/core/journals/proceedings-of-the-international-astronomical-union/article/potential-of-likelihoodfree-inference-of-cosmological-parameters-with-weak-lensing-data/0E1FEF317A0C09039B52C8791E63670D cshalizi have_read heard_the_talk approved_the_thesis astronomy approximate_bayesian_computation sufficiency exponential_families dimension_reduction simulation-based_estimation statistics in_NB re:codename:catherine_wheel https://pinboard.in/ https://pinboard.in/u:cshalizi/b:af0f8495eafc/ [2002.10399] Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting 2020-12-13T23:29:48+00:00 https://arxiv.org/abs/2002.10399 cshalizi have_read heard_the_talk approved_the_thesis_proposal simulation-based_inference lee.ann_b. izbicki.rafael dalmasso.niccolo statistics confidence_sets hypothesis_testing in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f4683e889416/ Hierarchical Community Detection by Recursive Partitioning: Journal of the American Statistical Association: Vol 0, No 0 2020-11-25T15:43:25+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1833888 cshalizi to:NB heard_the_talk community_discovery bickel.peter_j. sarkar.purnamrita levina.elizaveta https://pinboard.in/ https://pinboard.in/u:cshalizi/b:840444c82d8d/ Network Dependence Can Lead to Spurious Associations and Invalid Inference: Journal of the American Statistical Association: Vol 0, No 0 2020-11-20T19:49:26+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1782219 cshalizi to:NB have_read causal_inference network_data_analysis networks kith_and_kin ogburn.elizabeth to_teach:baby-nets re:homophily_and_confounding social_science_methodology heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:64e51a725339/ [2011.03395] Underspecification Presents Challenges for Credibility in Modern Machine Learning 2020-11-18T05:38:17+00:00 https://arxiv.org/abs/2011.03395 cshalizi machine_learning prediction statistics misspecification model_selection to_teach:childs_garden_of_statistical_learning_theory have_read in_NB heard_the_talk damour.alexander https://pinboard.in/ https://pinboard.in/u:cshalizi/b:18af6bd6c84a/ Verdinelli , Wasserman : Hybrid Wasserstein distance and fast distribution clustering 2020-11-16T16:12:38+00:00 https://projecteuclid.org/euclid.ejs/1576119710 cshalizi to:NB probability statistics clustering kith_and_kin wasserman.larry verdinelli.isa heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:105837eab23d/ 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/ [1607.07570] Random graph models for dynamic networks 2020-07-15T15:37:53+00:00 https://arxiv.org/abs/1607.07570 cshalizi to:NB heard_the_talk network_data_analysis markov_models kith_and_kin moore.cristopher newman.mark networks_in_and_over_time https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3e41f2daf2a9/ A large-scale analysis of racial disparities in police stops across the United States | Nature Human Behaviour 2020-06-14T17:14:58+00:00 https://www.nature.com/articles/s41562-020-0858-1 cshalizi to:NB heard_the_talk racism police statistics to_teach:statistics_of_inequality_and_discrimination https://pinboard.in/ https://pinboard.in/u:cshalizi/b:eb8f949614df/ Estimating the Causal Impact of Recommendation Systems from Observational Data 2019-11-27T17:22:36+00:00 https://dl.acm.org/citation.cfm?id=2764488 cshalizi causal_inference recommender_systems watts.duncan hofman.jake to_teach:data-mining heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:284968d0bdaa/ [1911.00535] Think-aloud interviews: A tool for exploring student statistical reasoning 2019-11-18T21:51:44+00:00 https://arxiv.org/abs/1911.00535 cshalizi to:NB heard_the_talk kith_and_kin statistics cognitive_science education protocol_analysis expertise have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cc522a226b76/ [1910.09007] Permutation-Based Causal Structure Learning with Unknown Intervention Targets 2019-10-22T13:15:26+00:00 https://arxiv.org/abs/1910.09007 cshalizi to:NB causal_discovery statistics gene_expression_data_analysis uhler.caroline heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:96c3323d9aed/ [1910.09014] Ordering-Based Causal Structure Learning in the Presence of Latent Variables 2019-10-22T13:13:07+00:00 https://arxiv.org/abs/1910.09014 cshalizi to:NB causal_discovery statistics uhler.caroline heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5be51ca8c588/ [1905.12741] Using Text Embeddings for Causal Inference 2019-09-30T23:11:20+00:00 https://arxiv.org/abs/1905.12741 cshalizi to:NB text_mining causal_inference blei.david heard_the_talk to_read to_present_in_causal_inference_reading_group https://pinboard.in/ https://pinboard.in/u:cshalizi/b:068d07ae4ba3/ [1804.00778] High-Dimensional Joint Estimation of Multiple Directed Gaussian Graphical Models 2019-08-20T15:34:51+00:00 https://arxiv.org/abs/1804.00778 cshalizi to:NB causal_discovery graphical_models statistics uhler.caroline heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fc4c9373e554/ [1908.05097] Causal discovery in heavy-tailed models 2019-08-15T20:55:04+00:00 https://arxiv.org/abs/1908.05097 cshalizi heard_the_talk causal_discovery causal_inference statistics heavy_tails in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9170ebc3301d/ [1908.00520] Network Dependence and Confounding by Network Structure Lead to Invalid Inference 2019-08-02T13:18:03+00:00 https://arxiv.org/abs/1908.00520 cshalizi network_data_analysis social_networks causal_inference heard_the_talk ogburn.elizabeth in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:551a8a71a8b5/ [1307.0366] Learning directed acyclic graphs based on sparsest permutations 2019-07-30T17:44:29+00:00 https://arxiv.org/abs/1307.0366 cshalizi graphical_models statistics causal_discovery uhler.caroline heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:327b9feb1461/ [1801.06229] Anchor regression: heterogeneous data meets causality 2019-06-17T16:43:41+00:00 https://arxiv.org/abs/1801.06229 cshalizi to:NB statistics causal_inference heard_the_talk peters.jonas buhlmann.peter regression instrumental_variables https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a84147f9c682/ [1705.08527] Causal inference for social network data 2019-04-30T14:15:07+00:00 https://arxiv.org/abs/1705.08527 cshalizi to:NB to_read heard_the_talk causal_inference network_data_analysis kith_and_kin ogburn.elizabeth van_der_laan.mark re:homophily_and_confounding to_teach:baby-nets https://pinboard.in/ https://pinboard.in/u:cshalizi/b:911a880fa6fb/ [1710.03296] Testing for Network and Spatial Autocorrelation 2019-04-30T13:49:14+00:00 https://arxiv.org/abs/1710.03296 cshalizi heard_the_talk ogburn.elizabeth kith_and_kin statistics spatial_statistics network_data_analysis to_teach:baby-nets to_teach:data_over_space_and_time re:neutral_cultural_networks in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:2edb23fc0e33/ [1810.10333] Memorization in Overparameterized Autoencoders 2019-04-11T00:38:09+00:00 https://arxiv.org/abs/1810.10333 cshalizi to:NB neural_networks heard_the_talk uhler.caroline belkin.mikhail your_favorite_deep_neural_network_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fc3c46dc71af/ [1903.08560] Surprises in High-Dimensional Ridgeless Least Squares Interpolation 2019-04-11T00:22:53+00:00 https://arxiv.org/abs/1903.08560 cshalizi to_read regression high-dimensional_statistics kith_and_kin tibshirani.ryan rosset.saharon montanari.andrea hastie.trevor statistics neural_networks heard_the_talk in_NB interpolation_aka_memorizing_the_training_data https://pinboard.in/ https://pinboard.in/u:cshalizi/b:078d131f8f8b/ [1810.03579] Long ties accelerate noisy threshold-based contagions 2018-10-12T04:04:01+00:00 https://arxiv.org/abs/1810.03579 cshalizi re:do-institutions-evolve eckles.dean heard_the_talk in_NB epidemics_on_networks have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:192c47c559c5/ [1706.04317] Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics 2018-10-10T16:55:31+00:00 https://arxiv.org/abs/1706.04317 cshalizi artificial_intelligence reinforcement_learning graphical_models heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c45fc9a765a8/ [1711.06642] Nonparametric independence testing via mutual information 2018-09-13T16:27:54+00:00 https://arxiv.org/abs/1711.06642 cshalizi in_NB heard_the_talk dependence_measures hypothesis_testing nonparametrics nearest-neighbors statistics samworth.richard_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1b4719cceeb7/ [1606.00304] Efficient multivariate entropy estimation via $k$-nearest neighbour distances 2018-09-13T16:27:11+00:00 https://arxiv.org/abs/1606.00304 cshalizi in_NB heard_the_talk entropy_estimation statistics nearest-neighors samworth.richard_j. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:79295b1694da/ [1808.00023] The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning 2018-08-20T20:03:31+00:00 https://arxiv.org/abs/1808.00023 cshalizi prediction algorithmic_fairness goel.sharad via:rvenkat have_read heard_the_talk in_NB to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination https://pinboard.in/ https://pinboard.in/u:cshalizi/b:bc431ac37e38/ Injustice: Political Theory for the Real World - Paperback - Michael Goodhart - Oxford University Press 2018-08-14T19:08:45+00:00 https://global.oup.com/academic/product/injustice-9780190692438?lang=en&cc=us# cshalizi to:NB books:noted political_philosophy moral_philosophy moral_responsibility heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:0202eb02e7dc/ [1705.08105] FRK: An R Package for Spatial and Spatio-Temporal Prediction with Large Datasets 2018-08-07T15:44:59+00:00 https://arxiv.org/abs/1705.08105 cshalizi to_read R heard_the_talk prediction spatial_statistics spatio-temporal_statistics to_teach:data_over_space_and_time in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:31e79e2da0ae/ [1804.07203] The Hardness of Conditional Independence Testing and the Generalised Covariance Measure 2018-05-18T01:12:57+00:00 https://arxiv.org/abs/1804.07203 cshalizi to:NB independence_testing hypothesis_testing statistics causal_discovery heard_the_talk to_read peters.jonas nonparametrics have_skimmed https://pinboard.in/ https://pinboard.in/u:cshalizi/b:06e5ea6a376c/ [1706.08576] Invariant Causal Prediction for Nonlinear Models 2018-05-18T01:11:07+00:00 https://arxiv.org/abs/1706.08576 cshalizi to:NB causal_inference causal_discovery statistics regression prediction peters.jonas meinshausen.nicolai to_read heard_the_talk to_teach:undergrad-ADA re:ADAfaEPoV https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c56e1a37ba95/ [1501.01332] Causal inference using invariant prediction: identification and confidence intervals 2018-05-18T01:10:13+00:00 https://arxiv.org/abs/1501.01332 cshalizi to:NB to_read causal_inference causal_discovery statistics prediction regression buhlmann.peter meinshausen.nicolai peters.jonas heard_the_talk re:ADAfaEPoV to_teach:undergrad-ADA https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e39d59855089/ An empirical analysis of journal policy effectiveness for computational reproducibility | PNAS 2018-05-07T22:34:59+00:00 http://www.pnas.org/content/115/11/2584 cshalizi to:NB why_oh_why_cant_we_have_a_better_academic_publishing_system heard_the_talk kith_and_kin stodden.victoria https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d6ae4d920316/ [1611.05401] Bootstrapping and Sample Splitting For High-Dimensional, Assumption-Free Inference 2018-04-05T15:06:34+00:00 https://arxiv.org/abs/1611.05401 cshalizi to:NB heard_the_talk linear_regression model_selection bootstrap kith_and_kin wasserman.larry rinaldo.alessandro g'sell.max lei.jing high-dimensional_statistics statistics to_teach:linear_models post-selection_inference https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7ccbe54bc5d5/ Niezink , Snijders : Co-evolution of social networks and continuous actor attributes 2018-04-03T16:12:45+00:00 https://projecteuclid.org/euclid.aoas/1514430273 cshalizi to:NB social_networks network_data_analysis social_influence stochastic_differential_equations statistics have_read heard_the_talk to_teach:baby-nets niezink.nynke snijders.tom https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b61846490014/ Empirical prediction intervals improve energy forecasting 2017-08-07T22:35:02+00:00 http://www.pnas.org/content/early/2017/07/27/1619938114.short cshalizi to:NB to_read heard_the_talk energy prediction statistics to_teach:undergrad-ADA https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d475d9942844/ [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/ Governed by a Spirit of Opposition: The Origins of American Political Practice in Colonial Philadelphia 2016-08-06T01:06:45+00:00 https://jhupbooks.press.jhu.edu/content/governed-spirit-opposition cshalizi books:noted american_history civil_society self-organization heard_the_talk where_by_"heard_the_talk"_i_mean_"heard_it_explained_over_drinks" institutions re:democratic_cognition democracy to_download in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:37f309015211/ [1507.03652] Lasso adjustments of treatment effect estimates in randomized experiments 2016-07-18T19:04:49+00:00 http://arxiv.org/abs/1507.03652 cshalizi to:NB heard_the_talk have_skimmed yu.bin lasso regression causal_inference statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:99775c38b50e/ [1602.00795] Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks 2016-02-08T21:31:26+00:00 http://arxiv.org/abs/1602.00795 cshalizi to:NB sexism science_as_a_social_process inequality academia kith_and_kin clauset.aaron heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:28cde13c11e2/ [1510.02706] Conditional Risk Minimization for Stochastic Processes 2015-10-17T18:08:31+00:00 http://arxiv.org/abs/1510.02706 cshalizi learning_theory stochastic_processes re:risk_bounds_for_time_series to_teach:childs_garden_of_statistical_learning_theory in_NB have_read heard_the_talk https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3ec04b70196f/ [1508.06675] Consistent nonparametric estimation for heavy-tailed sparse graphs 2015-09-13T19:58:16+00:00 http://arxiv.org/abs/1508.06675 cshalizi to_read graph_limits network_data_analysis re:smoothing_adjacency_matrices cohn.henry chayes.jennifer borgs.christian heard_the_talk to_teach:graphons in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:472625b9cca4/ Recovery from selection bias using marginal structure in discrete models 2015-07-16T14:21:43+00:00 http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/evans.pdf cshalizi graphical_models identifiability statistics categorical_data partial_identification algebra heard_the_talk didelez.vanessa in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cd6ef048a15b/ Causal Reasoning for Events in Continuous Time: A Decision-Theoretic Approach 2015-07-16T12:18:16+00:00 http://www.homepages.ucl.ac.uk/~ucgtrbd/uai2015_causal/papers/didelez.pdf cshalizi time_series point_processes causality causal_inference graphical_models statistics didelez.vanessa stochastic_processes heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:90bcda1d9c9f/ Robust reconstruction of causal graphical models based on 2-point and 3-point conditional information 2015-07-15T14:27:20+00:00 http://auai.org/uai2015/proceedings/papers/293.pdf cshalizi to_read causal_discovery graphical_models information_theory statistics heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:410a728456ba/ Learning the Structure of Causal Models with Relational and Temporal Dependence 2015-07-15T14:21:43+00:00 http://auai.org/uai2015/proceedings/papers/86.pdf cshalizi causal_discovery relational_learning machine_learning statistics jensen.david heard_the_talk in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f7fd05fd054c/ Do-calculus when the true graph is unknown 2015-07-15T14:18:09+00:00 http://auai.org/uai2015/proceedings/papers/127.pdf cshalizi heard_the_talk to_read causal_inference causal_discovery graphical_models statistics eberhardt.frederick kith_and_kin re:ADAfaEPoV in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4e16c461151e/