Pinboard (cshalizi)
https://pinboard.in/u:cshalizi/public/
recent bookmarks from cshaliziModel Selection via the VC Dimension2019-06-14T11:30:59+00:00
http://jmlr.org/papers/v20/17-669.html
cshalizito:NB learning_theory vc-dimension statistics model_selection to_teach:childs_garden_of_statistical_learning_theoryhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:3a1968525449/Model Selection via the VC Dimension2018-03-11T19:20:28+00:00
https://www.researchgate.net/profile/Bertrand_Clarke2/publication/322950779_Model_Selection_via_VC_Dimension/links/5a793a9d0f7e9b41dbd44e77/Model-Selection-via-VC-Dimension.pdf
cshalizito:NB learning_theory model_selection statistics regression classifiers vc-dimension to_read to_teach:childs_garden_of_statistical_learning_theoryhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:ddaa40d58791/Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher - ScienceDirect2017-04-10T00:58:53+00:00
http://www.sciencedirect.com/science/article/pii/S0022000097915075
cshalizito:NB vc-dimensionhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:378c518340fc/Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers | SpringerLink2017-04-06T18:58:10+00:00
https://link.springer.com/article/10.1007/BF00993408
cshalizito:NB to_read re:hyperbolic_networks vc-dimension computational_complexityhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:def37a93f650/Arcones , Gine : Limit Theorems for $U$-Processes2015-12-09T00:27:53+00:00
https://projecteuclid.org/euclid.aop/1176989128
cshaliziu-statistics empirical_processes deviation_inequalities vc-dimension central_limit_theorem re:smoothing_adjacency_matrices in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:fbf2713f12b2/Measures of Complexity - Festschrift for Alexey Chervonenkis | Vladimir Vovk | Springer2015-10-26T03:51:34+00:00
http://www.springer.com/us/book/9783319218519
cshalizito:NB learning_theory empirical_processes vc-dimension books:noted to_teach:childs_garden_of_statistical_learning_theoryhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:7a0909ffb330/From Uniform Laws of Large Numbers to Uniform Ergodic Theorems2015-08-27T00:24:46+00:00
http://www.maths.manchester.ac.uk/~goran/lectures.pdf
cshaliziin_NB ergodic_theory vc-dimension learning_theory stochastic_processes empirical_processes have_read books:recommended to_teach:childs_garden_of_statistical_learning_theoryhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:f5f9b778314c/[1506.05900] Representation Learning for Clustering: A Statistical Framework2015-07-14T09:50:06+00:00
http://arxiv.org/abs/1506.05900
cshalizito:NB machine_learning representation learning_theory clustering vc-dimensionhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:6432afb4e8cc/[1412.6612] The Vapnik-Chervonenkis Dimension of Norms on $mathbb{R}^d$2015-01-19T23:52:03+00:00
http://arxiv.org/abs/1412.6612
cshalizito:NB learning_theory vc-dimension to_teach:childs_garden_of_statistical_learning_theoryhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:3b85b7ba37a9/Probability in High Dimension2014-07-09T13:26:22+00:00
https://www.princeton.edu/~rvan/ORF570.pdf
cshaliziconcentration_of_measure empirical_processes probability high-dimensional_probability learning_theory vc-dimension van_handel.ramon via:arsyed re:almost_none in_NB to_teach:childs_garden_of_statistical_learning_theoryhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:f6d6eb8bb7ee/[1401.7388] Bounding Embeddings of VC Classes into Maximum Classes2014-02-03T20:26:21+00:00
http://arxiv.org/abs/1401.7388
cshalizilearning_theory vc-dimension in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:6334f20f8478/[1310.5796] Relative Deviation Learning Bounds and Generalization with Unbounded Loss Functions2013-10-23T14:15:38+00:00
http://arxiv.org/abs/1310.5796
cshalizilearning_theory vc-dimension empirical_processes have_read in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:4e3b59d92bd3/[1309.2626] Some new maximum VC classes2013-09-11T18:24:13+00:00
http://arxiv.org/abs/1309.2626
cshalizito:NB learning_theory vc-dimensionhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:5f50877796a6/Structural Risk Minimization over Data-Dependent Hierarchies2013-04-30T16:20:12+00:00
http://users.cecs.anu.edu.au/~williams/papers/P85.pdf
cshalizilearning_theory structural_risk_minimization classifiers vc-dimension re:your_favorite_dsge_sucks have_read to:blog in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:a5e762a44ed5/[1303.5976] On Learnability, Complexity and Stability2013-03-26T03:15:44+00:00
http://arxiv.org/abs/1303.5976
cshalizilearning_theory machine_learning stability_of_learning vc-dimension in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:be39d72ef75b/On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers2012-06-12T22:05:38+00:00
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6172585
cshalizilearning_theory stability_of_learning cross-validation vc-dimension in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:713ee6523cf9/[1203.0193] Vapnik-Chervonenkis Dimension of Axis-Parallel Cuts2012-03-03T16:25:49+00:00
http://arxiv.org/abs/1203.0193
cshalizilearning_theory vc-dimension classifiers in_NBhttps://pinboard.in/https://pinboard.in/u:cshalizi/b:8d3f1ba04187/[1111.3404] Estimated VC dimension for risk bounds2011-11-16T02:19:23+00:00
http://arxiv.org/abs/1111.3404
cshaliziself-promotion learning_theory vc-dimension machine_learning re:your_favorite_dsge_suckshttps://pinboard.in/https://pinboard.in/u:cshalizi/b:9eaaf6b4725a/[1109.4347] VC dimension of ellipsoids2011-09-21T01:22:54+00:00
http://arxiv.org/abs/1109.4347
cshalizilearning_theory in_NB vc-dimensionhttps://pinboard.in/u:cshalizi/b:3e020be58fe1/[1104.2097] PAC learnability versus VC dimension: a footnote to a basic result of statistical learning2011-04-18T01:32:15+00:00
http://arxiv.org/abs/1104.2097
cshalizilearning_theory vc-dimension in_NB set_theory measure_theoryhttps://pinboard.in/u:cshalizi/b:5dc139740a5a/Adams, Nobel: Uniform convergence of Vapnik–Chervonenkis classes under ergodic sampling2010-07-08T14:01:58+00:00
http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aop/1278593952
cshaliziergodic_theory learning_theory stochastic_processes vc-dimension have_read re:XV_for_mixing re:your_favorite_dsge_suckshttps://pinboard.in/u:cshalizi/b:cc75398c017c/[1006.5090] PAC learnability of a concept class under non-atomic measures: a problem by Vidyasagar2010-06-29T01:13:19+00:00
http://arxiv.org/abs/1006.5090
cshalizilearning_theory vc-dimension measure_theoryhttps://pinboard.in/u:cshalizi/b:4d481121ab4d/[1002.2044] On the Stability of Empirical Risk Minimization in the Presence of Multiple Risk Minimizers2010-02-11T06:01:07+00:00
http://arxiv.org/abs/1002.2044
cshalizilearning_theory stability_of_learning vc-dimensionhttps://pinboard.in/u:cshalizi/b:8bc975be2a89/van der Vaart, Wellner: A note on bounds for VC dimensions2010-02-02T14:49:03+00:00
http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.imsc/1265119264
cshalizilearning_theory vc-dimension van_der_vaart.aad wellner.jonhttps://pinboard.in/u:cshalizi/b:534812de1e1d/MIT OpenCourseWare | Mathematics | 18.465 Topics in Statistics: Statistical Learning Theory, Spring 2007 | Home2009-10-06T12:49:21+00:00
http://ocw.mit.edu/OcwWeb/Mathematics/18-465Spring-2007/CourseHome/
cshalizistatistics learning_theory machine_learning concentration_of_measure empirical_processes vc-dimension via:mraginskyhttps://pinboard.in/u:cshalizi/b:96fca2106a5c/A Note on the Richness of the Convex Hulls of VC Classes2009-03-24T04:52:21+00:00
http://www.math.washington.edu/~ejpecp/ECP/viewarticle.php?id=1685&layout=abstract
cshalizi0, there exists a function f in F such that the measure of the symmetric difference of B and the set where f is positive is less than ε. The question was motivated by the investigation of the theoretical properties of certain algorithms in machine learning." --- I see it, but I don't believe it! (The proof would seem to extend to arbitrary complete separable metric spaces, not just R^d.)
]]>learning_theory density_estimation statistics have_read vc-dimension analysis measure_theoryhttps://pinboard.in/u:cshalizi/b:8fe9e7f43418/