Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin
暂无分享,去创建一个
Daniel M. Kane | Ilias Diakonikolas | Pasin Manurangsi | Ilias Diakonikolas | D. Kane | Pasin Manurangsi
[1] Daniel M. Kane,et al. Robust Estimators in High Dimensions without the Computational Intractability , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).
[2] Ilias Diakonikolas,et al. Efficient Algorithms and Lower Bounds for Robust Linear Regression , 2018, SODA.
[3] Irit Dinur,et al. The PCP theorem by gap amplification , 2006, STOC.
[4] Amit Daniely,et al. Complexity theoretic limitations on learning halfspaces , 2015, STOC.
[5] Ilias Diakonikolas,et al. Degree-𝑑 chow parameters robustly determine degree-𝑑 PTFs (and algorithmic applications) , 2018, Electron. Colloquium Comput. Complex..
[6] Rocco A. Servedio,et al. Learning intersections of halfspaces with a margin , 2004, J. Comput. Syst. Sci..
[7] Ohad Shamir,et al. Learning Kernel-Based Halfspaces with the Zero-One Loss , 2010, COLT 2010.
[8] Madhur Tulsiani,et al. Regularity, Boosting, and Efficiently Simulating Every High-Entropy Distribution , 2009, 2009 24th Annual IEEE Conference on Computational Complexity.
[9] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[10] Guy Kindler,et al. Polynomially Low Error PCPs with polyloglog n Queries via Modular Composition , 2015, STOC.
[11] David A. McAllester. Simplified PAC-Bayesian Margin Bounds , 2003, COLT.
[12] Ran Raz,et al. Two Query PCP with Sub-Constant Error , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[13] Rocco A. Servedio,et al. Smooth boosting and learning with malicious noise , 2003 .
[14] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[15] Russell Impagliazzo,et al. Which Problems Have Strongly Exponential Complexity? , 2001, J. Comput. Syst. Sci..
[16] Preetum Nakkiran,et al. Adversarial Robustness May Be at Odds With Simplicity , 2019, ArXiv.
[17] Carsten Lund,et al. Efficient probabilistic checkable proofs and applications to approximation , 1994, STOC '94.
[18] Ming Li,et al. Learning in the presence of malicious errors , 1993, STOC '88.
[19] Luc Devroye,et al. Combinatorial methods in density estimation , 2001, Springer series in statistics.
[20] Rishi Saket,et al. Hardness of learning noisy halfspaces using polynomial thresholds , 2017, Electron. Colloquium Comput. Complex..
[21] Jerry Li,et al. Sever: A Robust Meta-Algorithm for Stochastic Optimization , 2018, ICML.
[22] Daniel M. Kane,et al. Learning geometric concepts with nasty noise , 2017, STOC.
[23] Rocco A. Servedio,et al. Hardness results for agnostically learning low-degree polynomial threshold functions , 2011, SODA '11.
[24] Russell Impagliazzo,et al. On the Complexity of k-SAT , 2001, J. Comput. Syst. Sci..
[25] Santosh S. Vempala,et al. Agnostic Estimation of Mean and Covariance , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).
[26] Vinod Vaikuntanathan,et al. Computational Limitations in Robust Classification and Win-Win Results , 2019, IACR Cryptol. ePrint Arch..
[27] Nathan Linial,et al. The Complexity of Learning Halfspaces using Generalized Linear Methods , 2012, COLT.
[28] Prasad Raghavendra,et al. Hardness of Learning Halfspaces with Noise , 2006, FOCS.
[29] Rocco A. Servedio,et al. Nearly Optimal Solutions for the Chow Parameters Problem and Low-Weight Approximation of Halfspaces , 2012, J. ACM.
[30] Jerry Li,et al. Robustly Learning a Gaussian: Getting Optimal Error, Efficiently , 2017, SODA.
[31] Michael R. Fellows,et al. Fixed-Parameter Tractability and Completeness II: On Completeness for W[1] , 1995, Theor. Comput. Sci..
[32] Rocco A. Servedio,et al. Learning Halfspaces with Malicious Noise , 2009, ICALP.
[33] Shai Shalev-Shwartz,et al. Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs , 2012, NIPS.
[34] Ilya P. Razenshteyn,et al. Adversarial examples from computational constraints , 2018, ICML.
[35] Jacques Stern,et al. The hardness of approximate optima in lattices, codes, and systems of linear equations , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.
[36] R. Schapire,et al. Toward Efficient Agnostic Learning , 1994 .
[37] Michael R. Fellows,et al. Fundamentals of Parameterized Complexity , 2013 .
[38] Rocco A. Servedio,et al. Learning large-margin halfspaces with more malicious noise , 2011, NIPS.
[39] Nathan Srebro,et al. VC Classes are Adversarially Robustly Learnable, but Only Improperly , 2019, COLT.
[40] Shai Shalev-Shwartz,et al. Agnostically Learning Halfspaces with Margin Errors , 2009 .
[41] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[42] Maria-Florina Balcan,et al. The Power of Localization for Efficiently Learning Linear Separators with Noise , 2013, J. ACM.
[43] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[44] Ryan O'Donnell,et al. The Chow Parameters Problem , 2011, SIAM J. Comput..
[45] W. B. Johnson,et al. Extensions of Lipschitz mappings into Hilbert space , 1984 .
[46] Dániel Marx,et al. Lower bounds based on the Exponential Time Hypothesis , 2011, Bull. EATCS.
[47] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[48] Carsten Lund,et al. Proof verification and hardness of approximation problems , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.
[49] C. K. Chow,et al. On the characterization of threshold functions , 1961, SWCT.
[50] Paul W. Goldberg,et al. A Bound on the Precision Required to Estimate a Boolean Perceptron from Its Average Satisfying Assignment , 2006, SIAM J. Discret. Math..
[51] Rocco A. Servedio,et al. Improved Approximation of Linear Threshold Functions , 2009, 2009 24th Annual IEEE Conference on Computational Complexity.
[52] Vitaly Feldman,et al. New Results for Learning Noisy Parities and Halfspaces , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[53] Sanjeev Arora,et al. Probabilistic checking of proofs; a new characterization of NP , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.
[54] Hans Ulrich Simon,et al. Efficient Learning of Linear Perceptrons , 2000, NIPS.
[55] Jerry Li,et al. Being Robust (in High Dimensions) Can Be Practical , 2017, ICML.
[56] Pravesh Kothari,et al. Efficient Algorithms for Outlier-Robust Regression , 2018, COLT.
[57] Leslie G. Valiant,et al. Learning Disjunction of Conjunctions , 1985, IJCAI.
[58] Carsten Lund,et al. Efficient probabilistically checkable proofs and applications to approximations , 1993, STOC.
[59] Carsten Lund,et al. Proof verification and the hardness of approximation problems , 1998, JACM.