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ORCIDIlias Diakonikolas
Person information
- affiliation (2019-present): University of Wisconsin, Madison, WI, USA
- affiliation (2016-2019): University Southern California, CA, USA
- affiliation (2012-2015): University of Edinburgh, UK
- affiliation (2010-2012): University of California, Berkeley, CA, USA
- affiliation (2004-2010): Columbia University, New York City, USA
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2020 – today
- 2024
[c147]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis:
Testable Learning of General Halfspaces with Adversarial Label Noise. COLT 2024: 1308-1335- [c146]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. COLT 2024: 1336-1363 - [c145]Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. COLT 2024: 1364-1378 - [c144]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? ICLR 2024 - [c143]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. ICML 2024 - [c142]Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos:
Fast Co-Training under Weak Dependence via Stream-Based Active Learning. ICML 2024 - [c141]Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas:
Robustly Learning Single-Index Models via Alignment Sharpness. ICML 2024 - [c140]Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu:
Online Robust Mean Estimation. SODA 2024: 3197-3235 - [c139]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 - [c138]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Testing Closeness of Multivariate Distributions via Ramsey Theory. STOC 2024: 340-347 - [i158]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? CoRR abs/2402.03502 (2024) - [i157]Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas:
Robustly Learning Single-Index Models via Alignment Sharpness. CoRR abs/2402.17756 (2024) - [i156]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. CoRR abs/2403.02300 (2024) - [i155]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. CoRR abs/2403.04744 (2024) - [i154]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) - [i153]Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright:
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. CoRR abs/2403.10547 (2024) - [i152]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) - [i151]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Online Learning of Halfspaces with Massart Noise. CoRR abs/2405.12958 (2024) - 2023
- [c137]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 - [c136]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 - [c135]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Self-Directed Linear Classification. COLT 2023: 2919-2947 - [c134]Daniel Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. COLT 2023: 3014-3028 - [c133]Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru Zhang:
Statistical and Computational Limits for Tensor-on-Tensor Association Detection. COLT 2023: 5260-5310 - [c132]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. COLT 2023: 5453-5475 - [c131]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. ICML 2023: 7886-7921 - [c130]Ilias Diakonikolas, Daniel Kane, Lisheng Ren:
Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. ICML 2023: 7922-7938 - [c129]Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas:
Robustly Learning a Single Neuron via Sharpness. ICML 2023: 36541-36577 - [c128]Ilias Diakonikolas, Jelena Diakonikolas, Daniel Kane, Puqian Wang, Nikos Zarifis:
Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. NeurIPS 2023 - [c127]Ilias Diakonikolas, Daniel Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis:
Efficient Testable Learning of Halfspaces with Adversarial Label Noise. NeurIPS 2023 - [c126]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 - [c125]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas:
Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. NeurIPS 2023 - [c124]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
First Order Stochastic Optimization with Oblivious Noise. NeurIPS 2023 - [c123]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. NeurIPS 2023 - [c122]Ilias Diakonikolas, Daniel Kane, Yuxin Sun:
SQ Lower Bounds for Learning Mixtures of Linear Classifiers. NeurIPS 2023 - [c121]Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright:
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. NeurIPS 2023 - [c120]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. SOSA 2023: 348-352 - [c119]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 - [i150]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) - [i149]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) - [i148]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) - [i147]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. CoRR abs/2305.02544 (2023) - [i146]Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas:
Robustly Learning a Single Neuron via Sharpness. CoRR abs/2306.07892 (2023) - [i145]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
SQ Lower Bounds for Learning Bounded Covariance GMMs. CoRR abs/2306.13057 (2023) - [i144]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) - [i143]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) - [i142]Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials. CoRR abs/2307.12840 (2023) - [i141]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Self-Directed Linear Classification. CoRR abs/2308.03142 (2023) - [i140]Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos:
Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. CoRR abs/2309.11657 (2023) - [i139]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
SQ Lower Bounds for Learning Mixtures of Linear Classifiers. CoRR abs/2310.11876 (2023) - [i138]Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu:
Online Robust Mean Estimation. CoRR abs/2310.15932 (2023) - [i137]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Testing Closeness of Multivariate Distributions via Ramsey Theory. CoRR abs/2311.13154 (2023) - [i136]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) - [i135]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) - [i134]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]Xi Chen, Ilias Diakonikolas, Anthi Orfanou, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis:
On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms for a Unit-Demand Buyer. SIAM J. Comput. 51(3): 492-548 (2022) - [c118]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Hardness of Learning a Single Neuron with Adversarial Label Noise. AISTATS 2022: 8199-8213 - [c117]Ilias Diakonikolas, Chrystalla Pavlou, John Peebles, Alistair Stewart:
Efficient Approximation Algorithms for the Inverse Semivalue Problem. AAMAS 2022: 354-362 - [c116]Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. COLT 2022: 3936-3978 - [c115]Ilias Diakonikolas, Daniel Kane:
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. COLT 2022: 4258-4282 - [c114]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. COLT 2022: 4313-4361 - [c113]Ilias Diakonikolas, Daniel Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. COLT 2022: 4535-4547 - [c112]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. COLT 2022: 4703-4763 - [c111]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. ICML 2022: 5061-5117 - [c110]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. ICML 2022: 5118-5141 - [c109]Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. NeurIPS 2022 - [c108]Clément L. Canonne, Ilias Diakonikolas, Daniel Kane, Sihan Liu:
Nearly-Tight Bounds for Testing Histogram Distributions. NeurIPS 2022 - [c107]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. NeurIPS 2022 - [c106]Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Ankit Pensia:
Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. NeurIPS 2022 - [c105]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. NeurIPS 2022 - [c104]Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. NeurIPS 2022 - [c103]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 - [c102]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 - [c101]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 - [i133]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas:
Streaming Algorithms for High-Dimensional Robust Statistics. CoRR abs/2204.12399 (2022) - [i132]Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. CoRR abs/2206.03441 (2022) - [i131]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) - [i130]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) - [i129]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. CoRR abs/2206.08918 (2022) - [i128]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Sihan Liu:
Near-Optimal Bounds for Testing Histogram Distributions. CoRR abs/2207.06596 (2022) - [i127]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi, Lisheng Ren:
Cryptographic Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2207.14266 (2022) - [i126]Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren, Yuxin Sun:
SQ Lower Bounds for Learning Single Neurons with Massart Noise. CoRR abs/2210.09949 (2022) - [i125]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Gaussian Mean Testing Made Simple. CoRR abs/2210.13706 (2022) - [i124]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) - [i123]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) - [i122]Daniel M. Kane, Ilias Diakonikolas:
A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. CoRR abs/2212.11221 (2022) - [i121]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) - [c100]Ilias Diakonikolas, Daniel M. Kane:
The Sample Complexity of Robust Covariance Testing. COLT 2021: 1511-1521 - [c99]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. COLT 2021: 1522-1551 - [c98]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 - [c97]Ilias Diakonikolas, Russell Impagliazzo, Daniel M. Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. COLT 2021: 1585-1644 - [c96]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. COLT 2021: 1645-1682 - [c95]Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld:
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. ICDE 2021: 1703-1714 - [c94]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis:
Learning Online Algorithms with Distributional Advice. ICML 2021: 2687-2696 - [c93]Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas, Alistair Stewart:
Statistical Query Lower Bounds for List-Decodable Linear Regression. NeurIPS 2021: 3191-3204 - [c92]Ilias Diakonikolas, Daniel Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. NeurIPS 2021: 7732-7744 - [c91]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. NeurIPS 2021: 10195-10208 - [c90]Ilias Diakonikolas, Jongho Park, Christos Tzamos:
ReLU Regression with Massart Noise. NeurIPS 2021: 25891-25903 - [c89]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Efficiently learning halfspaces with Tsybakov noise. STOC 2021: 88-101 - [c88]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price:
Optimal testing of discrete distributions with high probability. STOC 2021: 542-555 - [i120]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun:
Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. CoRR abs/2102.02171 (2021) - [i119]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) - [i118]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Agnostic Proper Learning of Halfspaces under Gaussian Marginals. CoRR abs/2102.05629 (2021) - [i117]Ilias Diakonikolas, Russell Impagliazzo, Daniel Kane, Rex Lei, Jessica Sorrell, Christos Tzamos:
Boosting in the Presence of Massart Noise. CoRR abs/2106.07779 (2021) - [i116]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) - [i115]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) - [i114]Ilias Diakonikolas, Daniel M. Kane, Christos Tzamos:
Forster Decomposition and Learning Halfspaces with Noise. CoRR abs/2107.05582 (2021) - [i113]Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Threshold Phenomena in Learning Halfspaces with Massart Noise. CoRR abs/2108.08767 (2021) - [i112]Ilias Diakonikolas, Jongho Park, Christos Tzamos:
ReLU Regression with Massart Noise. CoRR abs/2109.04623 (2021) - [i111]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) - [i110]Ilias Diakonikolas, Daniel M. Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. CoRR abs/2112.09104 (2021) - 2020
- [j19]David S. Johnson, Lee Breslau, Ilias Diakonikolas, Nick Duffield, Yu Gu, MohammadTaghi Hajiaghayi, Howard J. Karloff, Mauricio G. C. Resende, Subhabrata Sen:
Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs. Oper. Res. 68(3): 896-926 (2020) - [j18]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. IEEE Trans. Inf. Theory 66(5): 3132-3170 (2020) - [c87]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi:
Approximation Schemes for ReLU Regression. COLT 2020: 1452-1485 - [c86]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning Halfspaces with Massart Noise Under Structured Distributions. COLT 2020: 1486-1513 - [c85]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 - [c84]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 - [c83]Ilias Diakonikolas, Daniel M. Kane:
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. FOCS 2020: 184-195 - [c82]Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi:
High-dimensional Robust Mean Estimation via Gradient Descent. ICML 2020: 1768-1778 - [c81]Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. ICML 2020: 7010-7021 - [c80]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. NeurIPS 2020 - [c79]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. NeurIPS 2020 - [c78]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. NeurIPS 2020 - [c77]