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Daniel M. Kane
Daniel Kane 0001
Person information
- affiliation: University of California, San Diego, Department of Computer Science and Engineering, La Jolla, CA, USA
- affiliation (2011-2014): Stanford University, Department of Mathematics, Stanford, CA, USA
- affiliation (PhD 2011): Harvard University, Department of Mathematics, Cambridge, MA, USA
- affiliation (former): Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA, USA
Other persons with the same name
- Daniel Kane 0002 — Utah State University, Logan, UT, USA
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2020 – today
- 2024
- [j22]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. TheoretiCS 3 (2024) - [c135]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis:
Testable Learning of General Halfspaces with Adversarial Label Noise. COLT 2024: 1308-1335 - [c134]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. COLT 2024: 1336-1363 - [c133]Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. COLT 2024: 1364-1378 - [c132]Yuqian Cheng, Daniel M. Kane, Zhicheng Zheng:
New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions. COLT 2024: 2768-2794 - [c131]Max Hopkins, Russell Impagliazzo, Daniel M. Kane, Sihan Liu, Christopher Ye:
Replicability in High Dimensional Statistics. FOCS 2024: 1-8 - [c130]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostically Learning Multi-Index Models with Queries. FOCS 2024: 1931-1952 - [c129]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. ICML 2024 - [c128]Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu:
Online Robust Mean Estimation. SODA 2024: 3197-3235 - [c127]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFs. STOC 2024: 152-159 - [c126]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Testing Closeness of Multivariate Distributions via Ramsey Theory. STOC 2024: 340-347 - [c125]Daniel M. Kane, Anthony Ostuni, Kewen Wu:
Locality Bounds for Sampling Hamming Slices. STOC 2024: 1279-1286 - [i155]Daniel M. Kane, Anthony Ostuni, Kewen Wu:
Locality Bounds for Sampling Hamming Slices. CoRR abs/2402.14278 (2024) - [i154]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. CoRR abs/2403.02300 (2024) - [i153]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. CoRR abs/2403.04744 (2024) - [i152]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. CoRR abs/2403.10416 (2024) - [i151]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Super Non-singular Decompositions of Polynomials and their Application to Robustly Learning Low-degree PTFs. CoRR abs/2404.00529 (2024) - [i150]Max Hopkins, Russell Impagliazzo, Daniel Kane, Sihan Liu, Christopher Ye:
Replicability in High Dimensional Statistics. CoRR abs/2406.02628 (2024) - [i149]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis:
Efficient Testable Learning of General Halfspaces with Adversarial Label Noise. CoRR abs/2408.17165 (2024) - [i148]Daniel M. Kane, Anthony Ostuni, Kewen Wu:
Locally Sampleable Uniform Symmetric Distributions. CoRR abs/2411.08183 (2024) - [i147]Ilias Diakonikolas, Daniel M. Kane:
Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models. CoRR abs/2411.15669 (2024) - [i146]Daniel Kane, Anthony Ostuni, Kewen Wu:
Locally Sampleable Uniform Symmetric Distributions. Electron. Colloquium Comput. Complex. TR24 (2024) - [i145]Daniel M. Kane, Anthony Ostuni, Kewen Wu:
Locality Bounds for Sampling Hamming Slices. Electron. Colloquium Comput. Complex. TR24 (2024) - 2023
- [c124]Sihan Liu, Gaurav Mahajan, Daniel Kane, Shachar Lovett, Gellért Weisz, Csaba Szepesvári:
Exponential Hardness of Reinforcement Learning with Linear Function Approximation. COLT 2023: 1588-1617 - [c123]Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. COLT 2023: 2211-2239 - [c122]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians. COLT 2023: 2319-2349 - [c121]Daniel Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. COLT 2023: 3014-3028 - [c120]Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru Zhang:
Statistical and Computational Limits for Tensor-on-Tensor Association Detection. COLT 2023: 5260-5310 - [c119]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. ICML 2023: 7886-7921 - [c118]Ilias Diakonikolas, Daniel Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. ICML 2023: 7922-7938 - [c117]Ilias Diakonikolas, Jelena Diakonikolas, Daniel Kane, Puqian Wang, Nikos Zarifis:
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. NeurIPS 2023 - [c116]Ilias Diakonikolas, Daniel Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Efficient Testable Learning of Halfspaces with Adversarial Label Noise. NeurIPS 2023 - [c115]Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia, Thanasis Pittas:
A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm. NeurIPS 2023 - [c114]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. NeurIPS 2023 - [c113]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. NeurIPS 2023 - [c112]Ilias Diakonikolas, Daniel Kane, Yuxin Sun:
SQ Lower Bounds for Learning Mixtures of Linear Classifiers. NeurIPS 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 - [i144]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Do PAC-Learners Learn the Marginal Distribution? CoRR abs/2302.06285 (2023) - [i143]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) - [i142]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) - [i141]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) - [i140]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) - [i139]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. CoRR abs/2305.02544 (2023) - [i138]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
SQ Lower Bounds for Learning Bounded Covariance GMMs. CoRR abs/2306.13057 (2023) - [i137]Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. CoRR abs/2306.16352 (2023) - [i136]Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis:
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. CoRR abs/2307.08438 (2023) - [i135]Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials. CoRR abs/2307.12840 (2023) - [i134]Yuqian Cheng, Daniel M. Kane, Zhicheng Zheng:
New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions. CoRR abs/2308.00089 (2023) - [i133]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
SQ Lower Bounds for Learning Mixtures of Linear Classifiers. CoRR abs/2310.11876 (2023) - [i132]Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu:
Online Robust Mean Estimation. CoRR abs/2310.15932 (2023) - [i131]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Testing Closeness of Multivariate Distributions via Ramsey Theory. CoRR abs/2311.13154 (2023) - [i130]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. CoRR abs/2312.01547 (2023) - [i129]Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas:
Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation. CoRR abs/2312.11769 (2023) - [i128]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostically Learning Multi-index Models with Queries. CoRR abs/2312.16616 (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 - [i127]Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gaps in Reinforcement Learning. CoRR abs/2202.05444 (2022) - [i126]Alaa Maalouf, Murad Tukan, Eric Price, Daniel Kane, Dan Feldman:
Coresets for Data Discretization and Sine Wave Fitting. CoRR abs/2203.03009 (2022) - [i125]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. CoRR abs/2204.12399 (2022) - [i124]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. CoRR abs/2206.03441 (2022) - [i123]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) - [i122]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) - [i121]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Near-Optimal Bounds for Testing Histogram Distributions. CoRR abs/2207.06596 (2022) - [i120]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2207.14266 (2022) - [i119]Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. CoRR abs/2210.09949 (2022) - [i118]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. CoRR abs/2210.13706 (2022) - [i117]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) - [i116]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) - [i115]Daniel M. Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. CoRR abs/2212.11221 (2022) - [i114]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 - [i113]Daniel Kane, Andreas Fackler, Adam Gagol, Damian Straszak:
Highway: Efficient Consensus with Flexible Finality. CoRR abs/2101.02159 (2021) - [i112]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. CoRR abs/2102.02171 (2021) - [i111]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) - [i110]Max Hopkins, Daniel Kane, Shachar Lovett, Michal Moshkovitz:
Bounded Memory Active Learning through Enriched Queries. CoRR abs/2102.05047 (2021) - [i109]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. CoRR abs/2102.05629 (2021) - [i108]Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan, Daniel Kane:
Fooling Gaussian PTFs via Local Hyperconcentration. CoRR abs/2103.07809 (2021) - [i107]Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. CoRR abs/2106.07779 (2021) - [i106]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) - [i105]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) - [i104]Ilias Diakonikolas, Daniel M. Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. CoRR abs/2107.05582 (2021) - [i103]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Threshold Phenomena in Learning Halfspaces with Massart Noise. CoRR abs/2108.08767 (2021) - [i102]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) - [i101]Daniel M. Kane, Shahed Sharif, Alice Silverberg:
Quantum Money from Quaternion Algebras. CoRR abs/2109.12643 (2021) - [i100]Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. CoRR abs/2111.04746 (2021) - [i99]Ilias Diakonikolas, Daniel M. Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. CoRR abs/2112.09104 (2021) - [i98]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]