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ORCIDDaniel M. Kane
Daniel Kane
Person information
- affiliation: University of California, San Diego, La Jolla, CA, USA
- affiliation (2011 - 2014): Stanford University, CA, USA
- affiliation (PhD 2011): Harvard University, Cambridge, MA, USA
- affiliation (former): Massachusetts Institute of Technology, Cambridge, MA, USA
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2020 – today
- 2023
[c111]Daniel Beaglehole, Max Hopkins, Daniel Kane, Sihan Liu, Shachar Lovett:
Sampling Equilibria: Fast No-Regret Learning in Structured Games. SODA 2023: 3817-3855- [c110]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. SOSA 2023: 348-352 - [c109]Ilias Diakonikolas, Christos Tzamos, Daniel M. Kane:
A Strongly Polynomial Algorithm for Approximate Forster Transforms and Its Application to Halfspace Learning. STOC 2023: 1741-1754 - [i132]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Do PAC-Learners Learn the Marginal Distribution? CoRR abs/2302.06285 (2023) - [i131]Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. CoRR abs/2302.06512 (2023) - [i130]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan, Csaba Szepesvári, Gellért Weisz:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. CoRR abs/2302.12940 (2023) - [i129]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Efficient Testable Learning of Halfspaces with Adversarial Label Noise. CoRR abs/2303.05485 (2023) - [i128]Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia, Thanasis Pittas:
A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm. CoRR abs/2305.00966 (2023) - [i127]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. CoRR abs/2305.02544 (2023) - 2022
- [j21]Venkata Gandikota
, Daniel Kane, Raj Kumar Maity, Arya Mazumdar:
vqSGD: Vector Quantized Stochastic Gradient Descent. IEEE Trans. Inf. Theory 68(7): 4573-4587 (2022) - [c108]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Hardness of Learning a Single Neuron with Adversarial Label Noise. AISTATS 2022: 8199-8213 - [c107]Alaa Maalouf, Murad Tukan, Eric Price, Daniel M. Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. AISTATS 2022: 10622-10639 - [c106]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gap in Reinforcement Learning. COLT 2022: 1282-1302 - [c105]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. COLT 2022: 3015-3069 - [c104]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. COLT 2022: 3936-3978 - [c103]Ilias Diakonikolas, Daniel Kane:
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. COLT 2022: 4258-4282 - [c102]Ilias Diakonikolas, Daniel Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. COLT 2022: 4535-4547 - [c101]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. COLT 2022: 4703-4763 - [c100]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. ICML 2022: 5061-5117 - [c99]Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. NeurIPS 2022 - [c98]Clément L. Canonne, Ilias Diakonikolas, Daniel Kane, Sihan Liu:
Nearly-Tight Bounds for Testing Histogram Distributions. NeurIPS 2022 - [c97]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. NeurIPS 2022 - [c96]Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. NeurIPS 2022 - [c95]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. NeurIPS 2022 - [c94]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. NeurIPS 2022 - [c93]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning general halfspaces with general Massart noise under the Gaussian distribution. STOC 2022: 874-885 - [c92]Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala:
Robustly learning mixtures of k arbitrary Gaussians. STOC 2022: 1234-1247 - [c91]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
Clustering mixture models in almost-linear time via list-decodable mean estimation. STOC 2022: 1262-1275 - [i126]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gaps in Reinforcement Learning. CoRR abs/2202.05444 (2022) - [i125]Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. CoRR abs/2203.03009 (2022) - [i124]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. CoRR abs/2204.12399 (2022) - [i123]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. CoRR abs/2206.03441 (2022) - [i122]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. CoRR abs/2206.04589 (2022) - [i121]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. CoRR abs/2206.05245 (2022) - [i120]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Near-Optimal Bounds for Testing Histogram Distributions. CoRR abs/2207.06596 (2022) - [i119]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2207.14266 (2022) - [i118]Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. CoRR abs/2210.09949 (2022) - [i117]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. CoRR abs/2210.13706 (2022) - [i116]Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. CoRR abs/2211.16333 (2022) - [i115]Ilias Diakonikolas, Christos Tzamos, Daniel M. Kane:
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. CoRR abs/2212.03008 (2022) - [i114]Daniel M. Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. CoRR abs/2212.11221 (2022) - [i113]Ilias Diakonikolas, Christos Tzamos, Daniel Kane:
A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. Electron. Colloquium Comput. Complex. TR22 (2022) - 2021
- [j20]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustness meets algorithms. Commun. ACM 64(5): 107-115 (2021) - [j19]Daniel M. Kane, Scott Duke Kominers:
Prisoners, Rooms, and Light Switches. Electron. J. Comb. 28(1): 1 (2021) - [c90]Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar:
vqSGD: Vector Quantized Stochastic Gradient Descent. AISTATS 2021: 2197-2205 - [c89]Ilias Diakonikolas, Daniel M. Kane:
The Sample Complexity of Robust Covariance Testing. COLT 2021: 1511-1521 - [c88]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. COLT 2021: 1522-1551 - [c87]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model. COLT 2021: 1552-1584 - [c86]Ilias Diakonikolas, Russell Impagliazzo, Daniel M. Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. COLT 2021: 1585-1644 - [c85]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. COLT 2021: 1645-1682 - [c84]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. COLT 2021: 2358-2387 - [c83]Yuval Dagan, Yuval Filmus, Daniel Kane, Shay Moran:
The Entropy of Lies: Playing Twenty Questions with a Liar. ITCS 2021: 1:1-1:16 - [c82]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart:
Statistical Query Lower Bounds for List-Decodable Linear Regression. NeurIPS 2021: 3191-3204 - [c81]Ilias Diakonikolas, Daniel Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. NeurIPS 2021: 7732-7744 - [c80]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. NeurIPS 2021: 10195-10208 - [c79]Daniel M. Kane:
Robust Learning of Mixtures of Gaussians. SODA 2021: 1246-1258 - [c78]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Efficiently learning halfspaces with Tsybakov noise. STOC 2021: 88-101 - [c77]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price:
Optimal testing of discrete distributions with high probability. STOC 2021: 542-555 - [i112]Daniel Kane, Andreas Fackler, Adam Gagol, Damian Straszak:
Highway: Efficient Consensus with Flexible Finality. CoRR abs/2101.02159 (2021) - [i111]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. CoRR abs/2102.02171 (2021) - [i110]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals. CoRR abs/2102.04401 (2021) - [i109]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. CoRR abs/2102.05047 (2021) - [i108]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. CoRR abs/2102.05629 (2021) - [i107]Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan, Daniel Kane:
Fooling Gaussian PTFs via Local Hyperconcentration. CoRR abs/2103.07809 (2021) - [i106]Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. CoRR abs/2106.07779 (2021) - [i105]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation. CoRR abs/2106.08537 (2021) - [i104]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart:
Statistical Query Lower Bounds for List-Decodable Linear Regression. CoRR abs/2106.09689 (2021) - [i103]Ilias Diakonikolas, Daniel M. Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. CoRR abs/2107.05582 (2021) - [i102]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Threshold Phenomena in Learning Halfspaces with Massart Noise. CoRR abs/2108.08767 (2021) - [i101]Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. CoRR abs/2109.11515 (2021) - [i100]Daniel M. Kane, Shahed Sharif, Alice Silverberg:
Quantum Money from Quaternion Algebras. CoRR abs/2109.12643 (2021) - [i99]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. CoRR abs/2111.04746 (2021) - [i98]Ilias Diakonikolas, Daniel M. Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. CoRR abs/2112.09104 (2021) - [i97]Daniel M. Kane, Shahed Sharif, Alice Silverberg:
Quantum Money from Quaternion Algebras. IACR Cryptol. ePrint Arch. 2021: 1294 (2021) - 2020
- [j18]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. IEEE Trans. Inf. Theory 66(5): 3132-3170 (2020) - [c76]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Nikos Zarifis:
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. COLT 2020: 1514-1539 - [c75]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. COLT 2020: 1957-2006 - [c74]Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari:
Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. FOCS 2020: 149-159 - [c73]Ilias Diakonikolas, Daniel M. Kane:
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. FOCS 2020: 184-195 - [c72]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. FOCS 2020: 1034-1044 - [c71]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. NeurIPS 2020 - [c70]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. NeurIPS 2020 - [c69]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. NeurIPS 2020 - [c68]Ilias Diakonikolas, Daniel Kane, Nikos Zarifis:
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. NeurIPS 2020 - [c67]Max Hopkins, Daniel Kane, Shachar Lovett:
The Power of Comparisons for Actively Learning Linear Classifiers. NeurIPS 2020 - [p1]Ilias Diakonikolas, Daniel M. Kane:
Robust High-Dimensional Statistics. Beyond the Worst-Case Analysis of Algorithms 2020: 382-402 - [i96]Max Hopkins, Daniel Kane, Shachar Lovett, Gaurav Mahajan:
Noise-tolerant, Reliable Active Classification with Comparison Queries. CoRR abs/2001.05497 (2020) - [i95]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Point Location and Active Learning: Learning Halfspaces Almost Optimally. CoRR abs/2004.11380 (2020) - [i94]Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar:
Robustly Learning any Clusterable Mixture of Gaussians. CoRR abs/2005.06417 (2020) - [i93]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. CoRR abs/2006.10715 (2020) - [i92]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Nikos Zarifis:
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. CoRR abs/2006.12476 (2020) - [i91]Ilias Diakonikolas, Daniel M. Kane, Nikos Zarifis:
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. CoRR abs/2006.16200 (2020) - [i90]Daniel M. Kane:
Robust Learning of Mixtures of Gaussians. CoRR abs/2007.05912 (2020) - [i89]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. CoRR abs/2007.15220 (2020) - [i88]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. CoRR abs/2007.15618 (2020) - [i87]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price:
Optimal Testing of Discrete Distributions with High Probability. CoRR abs/2009.06540 (2020) - [i86]Daniel M. Kane, Scott Duke Kominers:
Prisoners, Rooms, and Lightswitches. CoRR abs/2009.08575 (2020) - [i85]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise. CoRR abs/2010.01705 (2020) - [i84]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. CoRR abs/2011.09973 (2020) - [i83]Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala:
Robustly Learning Mixtures of k Arbitrary Gaussians. CoRR abs/2012.02119 (2020) - [i82]Ilias Diakonikolas, Daniel M. Kane:
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. CoRR abs/2012.07774 (2020) - [i81]Ilias Diakonikolas, Daniel M. Kane:
Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2012.09720 (2020) - [i80]Ilias Diakonikolas, Daniel M. Kane:
The Sample Complexity of Robust Covariance Testing. CoRR abs/2012.15802 (2020) - [i79]Ilias Diakonikolas, Themis Gouleakis, Daniel Kane, John Peebles, Eric Price:
Optimal Testing of Discrete Distributions with High Probability. Electron. Colloquium Comput. Complex. TR20 (2020)
2010 – 2019
- 2019
- [j17]Daniel M. Kane, Carlo Sanna, Jeffrey O. Shallit:
Waring's Theorem for Binary Powers. Comb. 39(6): 1335-1350 (2019) - [j16]Daniel M. Kane, Shachar Lovett, Shay Moran:
Near-optimal Linear Decision Trees for k-SUM and Related Problems. J. ACM 66(3): 16:1-16:18 (2019) - [j15]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. 48(2): 742-864 (2019) - [j14]Daniel Kane, Shachar Lovett, Sankeerth Rao:
The Independence Number of the Birkhoff Polytope Graph, and Applications to Maximally Recoverable Codes. SIAM J. Comput. 48(4): 1425-1435 (2019) - [j13]Ilgweon Kang, Fang Qiao, Dongwon Park, Daniel Kane, Evangeline F. Y. Young, Chung-Kuan Cheng, Ronald L. Graham:
Three-dimensional Floorplan Representations by Using Corner Links and Partial Order. ACM Trans. Design Autom. Electr. Syst. 24(1): 13:1-13:33 (2019) - [c66]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. COLT 2019: 318-341 - [c65]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao:
Communication and Memory Efficient Testing of Discrete Distributions. COLT 2019: 1070-1106 - [c64]Ilias Diakonikolas, Daniel M. Kane, John Peebles:
Testing Identity of Multidimensional Histograms. COLT 2019: 1107-1131 - [c63]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. COLT 2019: 1449-1469 - [c62]Daniel Kane, Roi Livni, Shay Moran, Amir Yehudayoff:
On Communication Complexity of Classification Problems. COLT 2019: 1903-1943 - [c61]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML 2019: 1596-1606 - [c60]Daniel M. Kane, Richard Ryan Williams:
The Orthogonal Vectors Conjecture for Branching Programs and Formulas. ITCS 2019: 48:1-48:15 - [c59]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi:
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. NeurIPS 2019: 10473-10484 - [c58]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. NeurIPS 2019: 10688-10699 - [c57]Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld:
Private Testing of Distributions via Sample Permutations. NeurIPS 2019: 10877-10888 - [c56]Ilias Diakonikolas, Daniel M. Kane:
Degree-푑 chow parameters robustly determine degree-푑 PTFs (and algorithmic applications). STOC 2019: 804-815 - [i78]Surbhi Goel, Daniel M. Kane, Adam R. Klivans:
Learning Ising Models with Independent Failures. CoRR abs/1902.04728 (2019) - [i77]Olivier Bousquet, Daniel Kane, Shay Moran:
The Optimal Approximation Factor in Density Estimation. CoRR abs/1902.05876 (2019) - [i76]