Pinboard (cshalizi) https://pinboard.in/u:cshalizi/public/ recent bookmarks from cshalizi [2512.01819] Decision Tree Embedding by Leaf-Means 2025-12-06T14:30:34+00:00 https://arxiv.org/abs/2512.01819 cshalizi to:NB decision_trees neural_networks classifiers priebe.carey_e. https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a1ccf503ec38/ [2510.16174] COWs and their Hybrids: A Statistical View of Custom Orthogonal Weights 2025-10-24T19:41:22+00:00 https://arxiv.org/abs/2510.16174 cshalizi to:NB classifiers hypothesis_testing statistics particle_physics kith_and_kin wasserman.larry https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d79263d603f5/ Inside the AI Tool Used by DOGE to Review Veterans Affairs Contracts — ProPublica 2025-06-15T15:50:36+00:00 https://www.propublica.org/article/inside-ai-tool-doge-veterans-affairs-contracts-sahil-lavingia cshaliziutter_stupidity us_politics large_language_models_(so_called) programming classifiers to_teach:data-mining have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8dfe8878ce5a/ [2408.08823] Optimal Symmetries in Binary Classification 2024-12-11T19:53:45+00:00 https://arxiv.org/abs/2408.08823 cshalizi to:NB symmetry neural_networks classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:11063aae71d2/ [2203.05551] Cellular automata can classify data by inducing trajectory phase coexistence 2024-12-11T15:53:04+00:00 https://arxiv.org/abs/2203.05551 cshalizi to:NB classifiers cellular_automata https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c120322757d0/ [2211.13000] A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata 2024-07-23T14:56:27+00:00 https://arxiv.org/abs/2211.13000 cshalizi to:NB to_read network_data_analysis cellular_automata classifiers via:vaguery https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c05ac37b45b9/ [2402.07999] NetInfoF Framework: Measuring and Exploiting Network Usable Information 2024-03-12T01:33:46+00:00 https://arxiv.org/abs/2402.07999 cshalizi to:NB network_data_analysis classifiers entropy_estimation information_theory faloutsos.christos https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d6e3be34531e/ [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/ [2206.07801] Beyond Adult and COMPAS: Fairness in Multi-Class Prediction 2023-06-28T16:26:36+00:00 https://arxiv.org/abs/2206.07801 cshalizi in_NB classifiers algorithmic_fairness https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1ea2ba74e86b/ [2301.07015] Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection 2023-05-01T20:37:24+00:00 https://arxiv.org/abs/2301.07015 cshalizi to:NB classifiers networked_life deceiving_us_has_become_an_industrial_process decision_trees to_teach:data-mining philip_k_dick_and_the_fake_humans_rules_everything_around_me https://pinboard.in/ https://pinboard.in/u:cshalizi/b:26709234aea1/ Inside the Suspicion Machine | WIRED 2023-03-21T15:43:38+00:00 https://www.wired.com/story/welfare-state-algorithms/ cshalizi classifiers risk_assessment welfare_state algorithmic_fairness have_read to_teach:data-mining track_down_references bad_data_analysis https://pinboard.in/ https://pinboard.in/u:cshalizi/b:102b2cb016b2/ Inherent Tradeoffs in Learning Fair Representations 2022-07-19T14:03:30+00:00 https://jmlr.org/papers/v23/21-1427.html cshalizi in_NB algorithmic_fairness classifiers gordon.geoffrey to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination https://pinboard.in/ https://pinboard.in/u:cshalizi/b:8e8eb5d2fc4d/ Is a Classification Procedure Good Enough?—A Goodness-of-Fit Assessment Tool for Classification Learning: Journal of the American Statistical Association: Vol 0, No 0 2022-06-11T04:55:24+00:00 https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1979010 cshalizi to:NB classifiers goodness-of-fit https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7a692cbcec00/ [2112.00329] Non-splitting Neyman-Pearson Classifiers 2022-06-09T08:29:57+00:00 https://arxiv.org/abs/2112.00329 cshalizi to:NB classifiers neyman-pearson_classification https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fa5984dd2b33/ [2206.01295] Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification 2022-06-09T08:28:56+00:00 https://arxiv.org/abs/2206.01295 cshalizi to:NB prediction classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ebd80ca97d0b/ [2205.03009] Watching the watchers: bias and vulnerability in remote proctoring software 2022-05-23T15:01:27+00:00 https://arxiv.org/abs/2205.03009 cshalizi to:NB pattern_recognition classifiers algorithmic_fairness to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:697655fc032e/ Who Is Behind QAnon? Linguistic Detectives Find Fingerprints - The New York Times 2022-02-27T03:28:42+00:00 https://www.nytimes.com/2022/02/19/technology/qanon-messages-authors.html cshalizi qanon conspiracy_theories psychoceramics stylometrics text_mining natural_language_processing classifiers to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:cf3f4ef95f36/ Street–Level Algorithms: A Theory at the Gaps Between Policy and Decisions 2022-02-26T19:03:55+00:00 https://hci.stanford.edu/publications/2019/streetlevelalgorithms/streetlevelalgorithms-chi2019.pdf cshalizi via:henry_farrell classifiers algorithmic_fairness to_teach:data-mining decision-making bureaucracy have_read in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4aa7f6ce05aa/ [2003.08907] Overinterpretation reveals image classification model pathologies 2021-12-22T18:10:28+00:00 https://arxiv.org/abs/2003.08907 cshalizi classifiers your_favorite_deep_neural_network_sucks via:? adversarial_examples have_skimmed in_NB have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:628357aee15e/ Statistical Estimation from Dependent Data 2021-07-11T16:44:59+00:00 http://proceedings.mlr.press/v139/kandiros21a.html cshalizi to:NB learning_theory random_fields classifiers dependent_learning of_course_its_really_a_spin_glass statistics learning_under_dependence https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a1d41909f983/ Systematic misestimation of machine learning performance in neuroimaging studies of depression | Neuropsychopharmacology 2021-06-11T18:03:12+00:00 https://www.nature.com/articles/s41386-021-01020-7 cshalizi to:NB neural_data_analysis statistics classifiers to_teach:data-mining re:neutral_model_of_inquiry data_mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:a31d8ee83a33/ Phrenology, insurance claims, and digital gaydar - doxa 2021-06-04T03:34:23+00:00 https://doxa.substack.com/p/phrenology-insurance-claims-and-digital?token=eyJ1c2VyX2lkIjozMTk2MjUwOSwicG9zdF9pZCI6MzcxODg4MDYsIl8iOiI5RlR3eiIsImlhdCI6MTYyMjc3NzYxOSwiZXhwIjoxNjIyNzgxMjE5LCJpc3MiOiJwdWItMjM5NjUzIiwic3ViIjoicG9zdC1yZWFjdGlvbiJ9.r6AUl5BSCgCU4aMl6zL1Pt8xcC3cnNU5E7J6LDGlQbs cshaliziclassifiers algorithmic_fairness to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:903e4f804855/ [2011.02407] Debiasing classifiers: is reality at variance with expectation? 2021-06-01T13:36:59+00:00 https://arxiv.org/abs/2011.02407 cshalizi classifiers algorithmic_fairness in_NB statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ed5dccd4c10d/ [2105.08742] Uncertainty Aware Learning for High Energy Physics 2021-05-30T20:45:29+00:00 https://arxiv.org/abs/2105.08742 cshalizi to:NB classifiers statistics particle_physics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:41957b05e9e8/ [2105.11490] Hidden Markov and semi-Markov models: When and why are these models useful to classify states in time series data? 2021-05-26T18:30:10+00:00 https://arxiv.org/abs/2105.11490 cshalizi to:NB state-space_models time_series classifiers state_estimation misspecification to_teach:data_over_space_and_time statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:14bc8be5c08a/ 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/ [2105.07283] Calibrating sufficiently 2021-05-18T14:11:08+00:00 https://arxiv.org/abs/2105.07283 cshalizi to:NB classifiers prediction calibration sufficiency information_theory statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:fe18352f7b8e/ [2105.04648] Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations 2021-05-12T18:29:05+00:00 https://arxiv.org/abs/2105.04648 cshalizi prediction classifiers algorithmic_fairness smyth.padhraic to_teach:data-mining in_NB re:codename:one_law_for_the_lion_and_ox_is_oppression https://pinboard.in/ https://pinboard.in/u:cshalizi/b:9778e5e6bcab/ [2104.12553] Avoiding bias when inferring race using name-based approaches 2021-05-06T13:24:01+00:00 https://arxiv.org/abs/2104.12553 cshalizi to:NB text_mining classifiers sociology_of_science the_american_dilemma to_teach:statistics_of_inequality_and_discrimination color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c0dddef66827/ Statistical Evaluation of Medical Tests | Annual Review of Statistics and Its Application 2021-04-14T22:24:48+00:00 https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-040720-022432 cshalizito:NB to_teach:data-mining classifiers medicine statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7109aea5926e/ Results in statistical discriminant analysis: a review of the former Soviet Union literature - ScienceDirect 2021-03-10T15:49:35+00:00 https://www.sciencedirect.com/science/article/pii/S0047259X02000210 cshalizi to:NB history_of_statistics ussr via:rvenkat classifiers learning_theory to_teach:childs_garden_of_statistical_learning_theory re:paradigm_formation_in_statistical_learning https://pinboard.in/ https://pinboard.in/u:cshalizi/b:e51a3efdacdf/ [2003.01908] Denoised Smoothing: A Provable Defense for Pretrained Classifiers 2021-02-11T22:41:03+00:00 https://arxiv.org/abs/2003.01908 cshalizi classifiers adversarial_examples neural_networks kolter.j._zico to_read via:? in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:23b656d669b5/ [2101.11815] Interpolating Classifiers Make Few Mistakes 2021-02-04T15:33:47+00:00 https://arxiv.org/abs/2101.11815 cshalizi learning_theory recht.benjamin classifiers 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:d6b07112ccd5/ 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/ [2101.06309] Fundamental Tradeoffs in Distributionally Adversarial Training 2021-01-19T18:33:29+00:00 https://arxiv.org/abs/2101.06309 cshalizi to:NB statistics misspecification regression classifiers adversarial_examples robustness color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b9c81909cdee/ [2012.15863] Empirically Classifying Network Mechanisms 2021-01-03T19:53:41+00:00 https://arxiv.org/abs/2012.15863 cshalizi to:NB network_data_analysis network_formation classifiers to_teach:baby-nets https://pinboard.in/ https://pinboard.in/u:cshalizi/b:451433286efe/ Huawei tested AI software that could recognize Uighur minorities and alert police, report says - The Washington Post 2020-12-09T13:16:12+00:00 https://www.washingtonpost.com/technology/2020/12/08/huawei-tested-ai-software-that-could-recognize-uighur-minorities-alert-police-report-says/ cshalizi to_teach:data-mining xinjiang china:prc classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:953580a10026/ 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/ Barber : Is distribution-free inference possible for binary regression? 2020-11-16T16:22:04+00:00 https://projecteuclid.org/euclid.ejs/1601085759 cshalizi to:NB classifiers statistics confidence_sets prediction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:f65bda46c0b5/ Liu , Goldberg : Kernel machines with missing responses 2020-11-16T16:20:21+00:00 https://projecteuclid.org/euclid.ejs/1602900015 cshalizi to:NB missing_data kernel_methods statistics regression classifiers prediction https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b8bca80a71f4/ [2010.13993] Combining Label Propagation and Simple Models Out-performs Graph Neural Networks 2020-11-08T08:47:34+00:00 https://arxiv.org/abs/2010.13993 cshalizi network_data_analysis prediction classifiers smoothing your_favorite_deep_neural_network_sucks have_read to_teach:baby-nets in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7e9c5fb62793/ Social media-predicted personality traits and values can help match people to their ideal jobs | PNAS 2020-07-16T15:49:42+00:00 https://www.pnas.org/content/116/52/26459 cshalizi to:NB have_read bad_science bad_data_analysis classifiers text_mining personality_tests logistic_regression social_media psychology why_oh_why_cant_we_have_a_better_academic_publishing_system to_teach:data-mining forty_minutes_of_my_life_im_not_getting_back trapped_in_plutos_republic to:blog twitter re:career_advising_in_plutos_republic https://pinboard.in/ https://pinboard.in/u:cshalizi/b:780cca65f6d0/ Autopsy of a deep learning paper – Piekniewski's blog 2020-07-13T17:58:44+00:00 https://blog.piekniewski.info/2018/07/14/autopsy-dl-paper/ cshalizineural_networks classifiers your_favorite_deep_neural_network_sucks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:12413aefa499/ Wrongfully Accused by an Algorithm - The New York Times 2020-06-25T16:05:42+00:00 https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html?action=click&module=Top%20Stories&pgtype=Homepage cshaliziclassifiers data_mining police utter_stupidity to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b4f394d2fc25/ [2006.03895] The Criminality From Face Illusion 2020-06-19T17:30:25+00:00 https://arxiv.org/abs/2006.03895 cshalizi to:NB to_read prediction crime classifiers bad_data_analysis to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination via:yorksranter trapped_in_plutos_republic https://pinboard.in/ https://pinboard.in/u:cshalizi/b:34b1929c5c7c/ [1905.12516] Racial Bias in Hate Speech and Abusive Language Detection Datasets 2020-05-06T20:12:10+00:00 https://arxiv.org/abs/1905.12516 cshalizi algorithmic_fairness text_mining classifiers to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1ce9a1ebca05/ [2001.01987] Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring 2020-03-18T17:56:52+00:00 https://arxiv.org/abs/2001.01987 cshalizi to:NB neural_networks classifiers clustering k-means adversarial_examples via:arsyed https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1f1d75e91ff6/ A Tandem Evolutionary Algorithm for Identifying Causal Rules from Complex Data | Evolutionary Computation | MIT Press Journals 2020-03-02T14:53:54+00:00 https://www.mitpressjournals.org/doi/abs/10.1162/evco_a_00252 cshalizi to:NB causal_inference genetic_algorithms classifiers color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:33c9b7e64857/ 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/ The accuracy, fairness, and limits of predicting recidivism | Science Advances 2019-11-28T21:01:50+00:00 https://advances.sciencemag.org/content/4/1/eaao5580 cshalizi have_skimmed crime prediction algorithmic_fairness statistics classifiers to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ebd017b79dc3/ Algorithmic Risk Assessment in the Hands of Humans by Megan T. Stevenson, Jennifer L. Doleac :: SSRN 2019-11-21T03:32:13+00:00 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3489440 cshalizi algorithmic_fairness prediction classifiers crime to_teach:data-mining scores_and_classes to_teach:statistics_of_inequality_and_discrimination in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:553fdf367655/ [1911.00483] Explanation by Progressive Exaggeration 2019-11-11T15:27:15+00:00 https://arxiv.org/abs/1911.00483 cshalizi to:NB classifiers explanation https://pinboard.in/ https://pinboard.in/u:cshalizi/b:7501ccff54db/ [1902.02979] Fair Decisions Despite Imperfect Predictions 2019-10-29T15:01:36+00:00 https://arxiv.org/abs/1902.02979 cshalizi missing_data classifiers algorithmic_fairness statistics to_teach:data-mining to_teach:statistics_of_inequality_and_discrimination in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:3e6f8835dbd4/ [1910.12163] Understanding and Quantifying Adversarial Examples Existence in Linear Classification 2019-10-29T14:18:08+00:00 https://arxiv.org/abs/1910.12163 cshalizi adversarial_examples classifiers in_NB https://pinboard.in/ https://pinboard.in/u:cshalizi/b:266946ee11ba/ [1910.12756] Fast classification rates without standard margin assumptions 2019-10-29T02:24:09+00:00 https://arxiv.org/abs/1910.12756 cshalizi to:NB learning_theory classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:5060e7be5448/ [1910.11299] Fraud Detection in Networks: State-of-the-art 2019-10-25T14:40:13+00:00 https://arxiv.org/abs/1910.11299 cshalizi to:NB fraud classifiers network_data_analysis relational_learning statistics https://pinboard.in/ https://pinboard.in/u:cshalizi/b:b2f24aaae87e/ [1909.10831] Entropy from Machine Learning 2019-10-25T14:28:25+00:00 https://arxiv.org/abs/1909.10831 cshalizi to:NB classifiers entropy_estimation statistics color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:220e73253c27/ [1910.06772] Counterfactual diagnosis 2019-10-16T15:50:04+00:00 https://arxiv.org/abs/1910.06772 cshalizi to:NB causal_inference classifiers data_mining to_read to_teach:data-mining color_me_skeptical https://pinboard.in/ https://pinboard.in/u:cshalizi/b:595c9e6af36b/ [1909.12475] Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging 2019-10-01T17:19:57+00:00 https://arxiv.org/abs/1909.12475 cshalizi to:NB classifiers data_mining prediction bad_data_analysis statistics to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:48588e6ab9e2/ [1909.12434] Learning the Difference that Makes a Difference with Counterfactually-Augmented Data 2019-10-01T17:18:54+00:00 https://arxiv.org/abs/1909.12434 cshalizi to:NB classifiers statistics experimental_psychology of_a_sort https://pinboard.in/ https://pinboard.in/u:cshalizi/b:41d582ec8253/ [1909.03801] Aim for clinical utility, not just predictive accuracy 2019-09-26T18:39:39+00:00 https://arxiv.org/abs/1909.03801 cshalizi to:NB decision-making prediction classifiers data_mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:182a39f8e19b/ [1909.06788] Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels 2019-09-18T12:54:06+00:00 https://arxiv.org/abs/1909.06788 cshalizi to:NB kernel_methods high-dimensional_probability high-dimensional_statistics to_read statistics classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:c966754e5c49/ [1909.04791] Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization 2019-09-15T17:23:40+00:00 https://arxiv.org/abs/1909.04791 cshalizi to:NB classifiers machine_learning optimization computational_statistics statistics neural_networks https://pinboard.in/ https://pinboard.in/u:cshalizi/b:d7aabdc62b1e/ [1503.06410] What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes 2019-09-13T13:17:32+00:00 https://arxiv.org/abs/1503.06410 cshalizi information_retrieval classifiers NOT_to_teach:data-mining my_initial_skeptical_coloration_became_on_examination_a_permanent_stain https://pinboard.in/ https://pinboard.in/u:cshalizi/b:55d719338a6d/ [1908.08973] No evidence for critical slowing down prior to human epileptic seizures 2019-08-27T15:41:38+00:00 https://arxiv.org/abs/1908.08973 cshalizi to:NB time_series classifiers phase_transitions statistics epilepsy neuroscience https://pinboard.in/ https://pinboard.in/u:cshalizi/b:513f153be229/ Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition | Dermatology | JAMA Dermatology | JAMA Network 2019-08-20T14:46:42+00:00 https://jamanetwork.com/journals/jamadermatology/article-abstract/2740808 cshalizi to:NB classifiers to_teach:data-mining via:tslumley https://pinboard.in/ https://pinboard.in/u:cshalizi/b:1d52eaad8ebe/ [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/ [1908.06319] Locally Linear Embedding and fMRI feature selection in psychiatric classification 2019-08-20T14:19:42+00:00 https://arxiv.org/abs/1908.06319 cshalizi 80%) on eleven publicly-available datasets containing healthy controls and patients with either Schizophrenia, Attention-Deficit Hyperactivity Disorder (ADHD), or Autism Spectrum Disorder (ASD). Furthermore, unlike the original fMRI data before or after using Principal Component Analysis (PCA) for artefact reduction, the embedded fMRI furnished significantly better than chance classification (defined as the majority class proportion) on ten of eleven datasets. Interpretation: Locally Linear Embedding appears to be a useful feature extraction procedure that retains important information about patterns of brain activity distinguishing among psychiatric cohorts." --- Last tag is because I plan to teach LLE and this might make a good example or assignment, if I like how it was actually done. --- ETA: It's... not horrible (though the writing is bad and far too pretentious), but not very insightful, and too complicated to make a good teaching example.]]> to:NB locally_linear_embedding classifiers fmri dimension_reduction to_teach:data-mining have_read https://pinboard.in/ https://pinboard.in/u:cshalizi/b:ac687c5e5458/ [1908.03000] Feature selection of neural networks is skewed towards the less abstract cue 2019-08-09T13:40:03+00:00 https://arxiv.org/abs/1908.03000 cshalizi to:NB neural_networks classifiers https://pinboard.in/ https://pinboard.in/u:cshalizi/b:4c351f450ae7/ [1908.02591] Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics 2019-08-08T12:59:09+00:00 https://arxiv.org/abs/1908.02591 cshalizi to:NB bitcoin network_data_analysis classifiers statistics data_mining crime to_teach:data-mining https://pinboard.in/ https://pinboard.in/u:cshalizi/b:82c164a2a4b9/