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Ilias 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
- [c144]Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis:
Testable Learning of General Halfspaces with Adversarial Label Noise. COLT 2024: 1308-1335 - [c143]Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis:
Statistical Query Lower Bounds for Learning Truncated Gaussians. COLT 2024: 1336-1363 - [c142]Ilias Diakonikolas, Daniel M. Kane:
Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. COLT 2024: 1364-1378 - [c141]Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li:
How Does Unlabeled Data Provably Help Out-of-Distribution Detection? ICLR 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, Jong Ho 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, Jong Ho 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, Jong Ho 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, Jong Ho 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]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. NeurIPS 2020 - [c76]Ilias Diakonikolas, Daniel Kane, Nikos Zarifis:
Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. NeurIPS 2020 - [p2]Ilias Diakonikolas, Daniel M. Kane:
Robust High-Dimensional Statistics. Beyond the Worst-Case Analysis of Algorithms 2020: 382-402 - [i109]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning Halfspaces with Massart Noise Under Structured Distributions. CoRR abs/2002.05632 (2020) - [i108]Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov:
Efficient Algorithms for Multidimensional Segmented Regression. CoRR abs/2003.11086 (2020) - [i107]Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi:
High-Dimensional Robust Mean Estimation via Gradient Descent. CoRR abs/2005.01378 (2020) - [i106]Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar:
Robustly Learning any Clusterable Mixture of Gaussians. CoRR abs/2005.06417 (2020) - [i105]Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro:
Efficiently Learning Adversarially Robust Halfspaces with Noise. CoRR abs/2005.07652 (2020) - [i104]Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi:
Approximation Schemes for ReLU Regression. CoRR abs/2005.12844 (2020) - [i103]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning Halfspaces with Tsybakov Noise. CoRR abs/2006.06467 (2020) - [i102]Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. CoRR abs/2006.06742 (2020) - [i101]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard:
List-Decodable Mean Estimation via Iterative Multi-Filtering. CoRR abs/2006.10715 (2020) - [i100]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) - [i99]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) - [i98]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. CoRR abs/2007.15220 (2020) - [i97]Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia:
Outlier Robust Mean Estimation with Subgaussian Rates via Stability. CoRR abs/2007.15618 (2020) - [i96]Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld:
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. CoRR abs/2008.03891 (2020) - [i95]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price:
Optimal Testing of Discrete Distributions with High Probability. CoRR abs/2009.06540 (2020) - [i94]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) - [i93]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. CoRR abs/2011.09973 (2020) - [i92]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) - [i91]Ilias Diakonikolas, Daniel M. Kane:
Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. CoRR abs/2012.07774 (2020) - [i90]Ilias Diakonikolas, Daniel M. Kane:
Hardness of Learning Halfspaces with Massart Noise. CoRR abs/2012.09720 (2020) - [i89]Ilias Diakonikolas, Daniel M. Kane:
The Sample Complexity of Robust Covariance Testing. CoRR abs/2012.15802 (2020) - [i88]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]Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price:
Collision-Based Testers are Optimal for Uniformity and Closeness. Chic. J. Theor. Comput. Sci. 2019 (2019) - [j16]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) - [c75]Ilias Diakonikolas, Chrystalla Pavlou:
On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games. AAAI 2019: 1869-1876 - [c74]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. COLT 2019: 727-757 - [c73]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao:
Communication and Memory Efficient Testing of Discrete Distributions. COLT 2019: 1070-1106 - [c72]Ilias Diakonikolas, Daniel M. Kane, John Peebles:
Testing Identity of Multidimensional Histograms. COLT 2019: 1107-1131 - [c71]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML 2019: 1596-1606 - [c70]Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos:
Distribution-Independent PAC Learning of Halfspaces with Massart Noise. NeurIPS 2019: 4751-4762 - [c69]Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang:
Equipping Experts/Bandits with Long-term Memory. NeurIPS 2019: 5927-5937 - [c68]Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant:
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. NeurIPS 2019: 7721-7733 - [c67]Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi:
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. NeurIPS 2019: 10473-10484 - [c66]Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. NeurIPS 2019: 10688-10699 - [c65]Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld:
Private Testing of Distributions via Sample Permutations. NeurIPS 2019: 10877-10888 - [c64]Ilias Diakonikolas, Weihao Kong, Alistair Stewart:
Efficient Algorithms and Lower Bounds for Robust Linear Regression. SODA 2019: 2745-2754 - [c63]Yu Cheng
, Ilias Diakonikolas, Rong Ge:
High-Dimensional Robust Mean Estimation in Nearly-Linear Time. SODA 2019: 2755-2771 - [c62]Ilias Diakonikolas, Daniel M. Kane:
Degree-푑 chow parameters robustly determine degree-푑 PTFs (and algorithmic applications). STOC 2019: 804-815 - [i87]Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang:
Equipping Experts/Bandits with Long-term Memory. CoRR abs/1905.12950 (2019) - [i86]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. CoRR abs/1906.04661 (2019) - [i85]Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao:
Communication and Memory Efficient Testing of Discrete Distributions. CoRR abs/1906.04709 (2019) - [i84]Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos:
Distribution-Independent PAC Learning of Halfspaces with Massart Noise. CoRR abs/1906.10075 (2019) - [i83]Brian Axelrod, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart, Gregory Valiant:
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. CoRR abs/1907.08306 (2019) - [i82]Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi:
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. CoRR abs/1908.11335 (2019) - [i81]Ilias Diakonikolas, Daniel M. Kane:
Recent Advances in Algorithmic High-Dimensional Robust Statistics. CoRR abs/1911.05911 (2019) - [i80]Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane, Eric Price, Alistair Stewart:
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. CoRR abs/1911.08085 (2019) - 2018
- [j15]Xi Chen, Ilias Diakonikolas, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis:
The complexity of optimal multidimensional pricing for a unit-demand buyer. Games Econ. Behav. 110: 139-164 (2018) - [j14]Clément L. Canonne
, Ilias Diakonikolas, Themis Gouleakis, Ronitt Rubinfeld:
Testing Shape Restrictions of Discrete Distributions. Theory Comput. Syst. 62(1): 4-62 (2018) - [c61]Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. COLT 2018: 819-842 - [c60]Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart:
Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities. COLT 2018: 1234-1262 - [c59]Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price:
Sample-Optimal Identity Testing with High Probability. ICALP 2018: 41:1-41:14 - [c58]Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld:
Differentially Private Identity and Equivalence Testing of Discrete Distributions. ICML 2018: 169-178 - [c57]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Conditional Independence of Discrete Distributions. ITA 2018: 1-57 - [c56]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. NeurIPS 2018: 6204-6213 - [c55]Alistair Stewart, Ilias Diakonikolas, Clément L. Canonne:
Testing for Families of Distributions via the Fourier Transform. NeurIPS 2018: 10084-10095 - [c54]Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart:
Robust Learning of Fixed-Structure Bayesian Networks. NeurIPS 2018: 10304-10316 - [c53]Ilias Diakonikolas, Gautam Kamath
, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA 2018: 2683-2702 - [c52]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing conditional independence of discrete distributions. STOC 2018: 735-748 - [c51]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
List-decodable robust mean estimation and learning mixtures of spherical gaussians. STOC 2018: 1047-1060 - [c50]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning geometric concepts with nasty noise. STOC 2018: 1061-1073 - [e1]Ilias Diakonikolas, David Kempe, Monika Henzinger:
Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2018, Los Angeles, CA, USA, June 25-29, 2018. ACM 2018 [contents] - [i79]Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. CoRR abs/1802.08513 (2018) - [i78]Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart:
Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities. CoRR abs/1802.10575 (2018) - [i77]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. CoRR abs/1803.02815 (2018) - [i76]Ilias Diakonikolas, Daniel M. Kane, John Peebles:
Testing Identity of Multidimensional Histograms. CoRR abs/1804.03636 (2018) - [i75]Ilias Diakonikolas, Weihao Kong, Alistair Stewart:
Efficient Algorithms and Lower Bounds for Robust Linear Regression. CoRR abs/1806.00040 (2018) - [i74]Ilias Diakonikolas, Daniel M. Kane:
Degree-d Chow Parameters Robustly Determine Degree-d PTFs (and Algorithmic Applications). CoRR abs/1811.03491 (2018) - [i73]Yu Cheng, Ilias Diakonikolas, Rong Ge:
High-Dimensional Robust Mean Estimation in Nearly-Linear Time. CoRR abs/1811.09380 (2018) - [i72]Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart:
A Polynomial Time Algorithm for Maximum Likelihood Estimation of Multivariate Log-concave Densities. CoRR abs/1812.05524 (2018) - [i71]Ilias Diakonikolas, Chrystalla Pavlou:
On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games. CoRR abs/1812.11712 (2018) - [i70]Ilias Diakonikolas, Daniel Kane:
Degree-$d$ Chow Parameters Robustly Determine Degree-$d$ PTFs (and Algorithmic Applications). Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [j13]Anindya De, Ilias Diakonikolas, Rocco A. Servedio
:
The Inverse Shapley value problem. Games Econ. Behav. 105: 122-147 (2017) - [c49]Clément L. Canonne
, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. COLT 2017: 370-448 - [c48]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning Multivariate Log-concave Distributions. COLT 2017: 711-727 - [c47]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures. FOCS 2017: 73-84 - [c46]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Near-Optimal Closeness Testing of Discrete Histogram Distributions. ICALP 2017: 8:1-8:15 - [c45]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. ICML 2017: 999-1008 - [c44]Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt:
Communication-Efficient Distributed Learning of Discrete Distributions. NIPS 2017: 6391-6401 - [c43]Yu Cheng, Ilias Diakonikolas, Alistair Stewart:
Playing Anonymous Games using Simple Strategies. SODA 2017: 616-631 - [c42]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Sample-Optimal Density Estimation in Nearly-Linear Time. SODA 2017: 1278-1289 - [i69]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. CoRR abs/1703.00893 (2017) - [i68]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Near-Optimal Closeness Testing of Discrete Histogram Distributions. CoRR abs/1703.01913 (2017) - [i67]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. CoRR abs/1704.03866 (2017) - [i66]Clément L. Canonne, Ilias Diakonikolas, Alistair Stewart:
Fourier-Based Testing for Families of Distributions. CoRR abs/1706.05738 (2017) - [i65]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning Geometric Concepts with Nasty Noise. CoRR abs/1707.01242 (2017) - [i64]Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld:
Differentially Private Identity and Closeness Testing of Discrete Distributions. CoRR abs/1707.05497 (2017) - [i63]Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price:
Sample-Optimal Identity Testing with High Probability. CoRR abs/1708.02728 (2017) - [i62]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. CoRR abs/1709.02087 (2017) - [i61]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
List-Decodable Robust Mean Estimation and Learning Mixtures of Spherical Gaussians. CoRR abs/1711.07211 (2017) - [i60]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Conditional Independence of Discrete Distributions. CoRR abs/1711.11560 (2017) - [i59]Clément L. Canonne, Ilias Diakonikolas, Alistair Stewart:
Fourier-Based Testing for Families of Distributions. Electron. Colloquium Comput. Complex. TR17 (2017) - [i58]Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price:
Sample-Optimal Identity Testing with High Probability. Electron. Colloquium Comput. Complex. TR17 (2017) - [i57]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Sharp Bounds for Generalized Uniformity Testing. Electron. Colloquium Comput. Complex. TR17 (2017) - 2016
- [j12]Constantinos Daskalakis, Ilias Diakonikolas, Mihalis Yannakakis:
How Good is the Chord Algorithm? SIAM J. Comput. 45(3): 811-858 (2016) - [j11]Anindya De, Ilias Diakonikolas, Rocco A. Servedio
:
A Robust Khintchine Inequality, and Algorithms for Computing Optimal Constants in Fourier Analysis and High-Dimensional Geometry. SIAM J. Discret. Math. 30(2): 1058-1094 (2016) - [c41]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables. COLT 2016: 831-849 - [c40]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time. COLT 2016: 850-878 - [c39]Ilias Diakonikolas, Gautam Kamath
, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High Dimensions without the Computational Intractability. FOCS 2016: 655-664 - [c38]Ilias Diakonikolas, Daniel M. Kane:
A New Approach for Testing Properties of Discrete Distributions. FOCS 2016: 685-694 - [c37]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. ICML 2016: 2878-2886 - [c36]Clément L. Canonne, Ilias Diakonikolas, Themis Gouleakis, Ronitt Rubinfeld:
Testing Shape Restrictions of Discrete Distributions. STACS 2016: 25:1-25:14 - [c35]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
The fourier transform of poisson multinomial distributions and its algorithmic applications. STOC 2016: 1060-1073 - [p1]Ilias Diakonikolas:
Learning Structured Distributions. Handbook of Big Data 2016: 267-283 - [i56]Ilias Diakonikolas, Daniel M. Kane:
A New Approach for Testing Properties of Discrete Distributions. CoRR abs/1601.05557 (2016) - [i55]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Zheng Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High Dimensions without the Computational Intractability. CoRR abs/1604.06443 (2016) - [i54]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Learning Multivariate Log-concave Distributions. CoRR abs/1605.08188 (2016) - [i53]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Efficient Robust Proper Learning of Log-concave Distributions. CoRR abs/1606.03077 (2016) - [i52]Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Robust Learning of Fixed-Structure Bayesian Networks. CoRR abs/1606.07384 (2016) - [i51]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. CoRR abs/1607.03990 (2016) - [i50]Yu Cheng, Ilias Diakonikolas, Alistair Stewart:
Playing Anonymous Games using Simple Strategies. CoRR abs/1608.07336 (2016) - [i49]David S. Johnson, Lee Breslau, Ilias Diakonikolas, Nick G. 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. CoRR abs/1611.01210 (2016) - [i48]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures. CoRR abs/1611.03473 (2016) - [i47]Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price:
Collision-based Testers are Optimal for Uniformity and Closeness. CoRR abs/1611.03579 (2016) - [i46]Clément L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Testing Bayesian Networks. CoRR abs/1612.03156 (2016) - [i45]Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price:
Collision-based Testers are Optimal for Uniformity and Closeness. Electron. Colloquium Comput. Complex. TR16 (2016) - [i44]Ilias Diakonikolas, Daniel M. Kane:
A New Approach for Testing Properties of Discrete Distributions. Electron. Colloquium Comput. Complex. TR16 (2016) - [i43]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures. Electron. Colloquium Comput. Complex. TR16 (2016) - 2015
- [j10]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
:
Learning Poisson Binomial Distributions. Algorithmica 72(1): 316-357 (2015) - [j9]Ilias Diakonikolas, Ragesh Jaiswal, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
Noise Stable Halfspaces are Close to Very Small Juntas. Chic. J. Theor. Comput. Sci. 2015 (2015) - [c34]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin
:
Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. FOCS 2015: 1183-1202 - [c33]Xi Chen, Ilias Diakonikolas, Anthi Orfanou, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis:
On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms. FOCS 2015: 1464-1479 - [c32]Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt:
Differentially Private Learning of Structured Discrete Distributions. NIPS 2015: 2566-2574 - [c31]Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Zheng Li, Ludwig Schmidt:
Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. PODS 2015: 249-263 - [c30]Anindya De, Ilias Diakonikolas, Rocco A. Servedio
:
Learning from satisfying assignments. SODA 2015: 478-497 - [c29]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin
:
Testing Identity of Structured Distributions. SODA 2015: 1841-1854 - [i42]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables. CoRR abs/1505.00662 (2015) - [i41]Jayadev Acharya, Ilias Diakonikolas, Jerry Zheng Li, Ludwig Schmidt:
Sample-Optimal Density Estimation in Nearly-Linear Time. CoRR abs/1506.00671 (2015) - [i40]Clément L. Canonne, Ilias Diakonikolas, Themis Gouleakis, Ronitt Rubinfeld:
Testing Shape Restrictions of Discrete Distributions. CoRR abs/1507.03558 (2015) - [i39]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. CoRR abs/1508.05538 (2015) - [i38]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
The Fourier Transform of Poisson Multinomial Distributions and its Algorithmic Applications. CoRR abs/1511.03592 (2015) - [i37]Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart:
Properly Learning Poisson Binomial Distributions in Almost Polynomial Time. CoRR abs/1511.04066 (2015) - 2014
- [j8]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio
:
Nearly Optimal Solutions for the Chow Parameters Problem and Low-Weight Approximation of Halfspaces. J. ACM 61(2): 11:1-11:36 (2014) - [j7]Ilias Diakonikolas, Prasad Raghavendra, Rocco A. Servedio
, Li-Yang Tan:
Average Sensitivity and Noise Sensitivity of Polynomial Threshold Functions. SIAM J. Comput. 43(1): 231-253 (2014) - [j6]Ilias Diakonikolas, Rocco A. Servedio
, Li-Yang Tan, Andrew Wan:
A Regularity Lemma and Low-Weight Approximators for Low-Degree Polynomial Threshold Functions. Theory Comput. 10: 27-53 (2014) - [j5]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
:
Learning k-Modal Distributions via Testing. Theory Comput. 10: 535-570 (2014) - [c28]Anindya De, Ilias Diakonikolas, Rocco A. Servedio
:
Deterministic Approximate Counting for Juntas of Degree-2 Polynomial Threshold Functions. CCC 2014: 229-240 - [c27]Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun:
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms. NIPS 2014: 1844-1852 - [c26]Constantinos Daskalakis, Anindya De, Ilias Diakonikolas, Ankur Moitra, Rocco A. Servedio
:
A Polynomial-time Approximation Scheme for Fault-tolerant Distributed Storage. SODA 2014: 628-644 - [c25]Siu On Chan, Ilias Diakonikolas, Paul Valiant, Gregory Valiant:
Optimal Algorithms for Testing Closeness of Discrete Distributions. SODA 2014: 1193-1203 - [c24]Xi Chen, Ilias Diakonikolas, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis:
The Complexity of Optimal Multidimensional Pricing. SODA 2014: 1319-1328 - [c23]Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio
, Xiaorui Sun:
Efficient density estimation via piecewise polynomial approximation. STOC 2014: 604-613 - [i36]Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin:
Testing Identity of Structured Distributions. CoRR abs/1410.2266 (2014) - [i35]Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun:
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms. CoRR abs/1411.0169 (2014) - 2013
- [j4]Ilias Diakonikolas, Rocco A. Servedio
:
Improved Approximation of Linear Threshold Functions. Comput. Complex. 22(3): 623-677 (2013) - [c22]Constantinos Daskalakis, Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio
, Li-Yang Tan:
Learning Sums of Independent Integer Random Variables. FOCS 2013: 217-226 - [c21]Anindya De, Ilias Diakonikolas, Rocco A. Servedio
:
A Robust Khintchine Inequality, and Algorithms for Computing Optimal Constants in Fourier Analysis and High-Dimensional Geometry. ICALP (1) 2013: 376-387 - [c20]Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio
, Xiaorui Sun:
Learning mixtures of structured distributions over discrete domains. SODA 2013: 1380-1394 - [c19]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
, Gregory Valiant, Paul Valiant:
Testing k-Modal Distributions: Optimal Algorithms via Reductions. SODA 2013: 1833-1852 - [i34]Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun:
Efficient Density Estimation via Piecewise Polynomial Approximation. CoRR abs/1305.3207 (2013) - [i33]Constantinos Daskalakis, Anindya De, Ilias Diakonikolas, Ankur Moitra, Rocco A. Servedio:
A Polynomial-time Approximation Scheme for Fault-tolerant Distributed Storage. CoRR abs/1307.3621 (2013) - [i32]Siu On Chan, Ilias Diakonikolas, Gregory Valiant, Paul Valiant:
Optimal Algorithms for Testing Closeness of Discrete Distributions. CoRR abs/1308.3946 (2013) - [i31]Constantinos Daskalakis, Ilias Diakonikolas, Mihalis Yannakakis:
How good is the Chord algorithm? CoRR abs/1309.7084 (2013) - [i30]Xi Chen, Ilias Diakonikolas, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis:
The Complexity of Optimal Multidimensional Pricing. CoRR abs/1311.2138 (2013) - [i29]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
Deterministic Approximate Counting for Degree-$2$ Polynomial Threshold Functions. CoRR abs/1311.7105 (2013) - [i28]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
Deterministic Approximate Counting for Juntas of Degree-$2$ Polynomial Threshold Functions. CoRR abs/1311.7115 (2013) - [i27]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
Deterministic Approximate Counting for Juntas of Degree-2 Polynomial Threshold Functions. Electron. Colloquium Comput. Complex. TR13 (2013) - [i26]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
Deterministic Approximate Counting for Degree-2 Polynomial Threshold Functions. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [c18]Anindya De, Ilias Diakonikolas, Rocco A. Servedio
:
The Inverse Shapley Value Problem. ICALP (1) 2012: 266-277 - [c17]Ilias Diakonikolas, Christos H. Papadimitriou, George Pierrakos, Yaron Singer:
Efficiency-Revenue Trade-Offs in Auctions. ICALP (2) 2012: 488-499 - [c16]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
:
Learning k-modal distributions via testing. SODA 2012: 1371-1385 - [c15]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
:
Learning poisson binomial distributions. STOC 2012: 709-728 - [c14]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio
:
Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces. STOC 2012: 729-746 - [i25]Ilias Diakonikolas, Ragesh Jaiswal, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
On the Distribution of the Fourier Spectrum of Halfspaces. CoRR abs/1202.6680 (2012) - [i24]Ilias Diakonikolas, Christos H. Papadimitriou, George Pierrakos, Yaron Singer:
Efficiency-Revenue Trade-offs in Auctions. CoRR abs/1205.3077 (2012) - [i23]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio:
Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces. CoRR abs/1206.0985 (2012) - [i22]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
A robust Khintchine inequality, and algorithms for computing optimal constants in Fourier analysis and high-dimensional geometry. CoRR abs/1207.2229 (2012) - [i21]Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun:
Learning mixtures of structured distributions over discrete domains. CoRR abs/1210.0864 (2012) - [i20]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
Inverse problems in approximate uniform generation. CoRR abs/1211.1722 (2012) - [i19]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
The Inverse Shapley Value Problem. CoRR abs/1212.5132 (2012) - [i18]Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio:
Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces. Electron. Colloquium Comput. Complex. TR12 (2012) - [i17]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
Inverse Problems in Approximate Uniform Generation. Electron. Colloquium Comput. Complex. TR12 (2012) - [i16]Anindya De, Ilias Diakonikolas, Rocco A. Servedio:
The Inverse Shapley Value Problem. Electron. Colloquium Comput. Complex. TR12 (2012) - 2011
- [b1]Ilias Diakonikolas:
Approximation of Multiobjective Optimization Problems. Columbia University, USA, 2011 - [j3]Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Rocco A. Servedio, Andrew Wan:
Efficiently Testing Sparse GF(2) Polynomials. Algorithmica 61(3): 580-605 (2011) - [c13]Lee Breslau, Ilias Diakonikolas, Nick G. Duffield, Yu Gu, Mohammad Taghi Hajiaghayi, David S. Johnson, Howard J. Karloff, Mauricio G. C. Resende, Subhabrata Sen:
Disjoint-Path Facility Location: Theory and Practice. ALENEX 2011: 60-74 - [c12]Hung-Yi Liu, Ilias Diakonikolas, Michele Petracca, Luca P. Carloni
:
Supervised design space exploration by compositional approximation of Pareto sets. DAC 2011: 399-404 - [c11]Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio
, Yi Wu:
Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions. SODA 2011: 1590-1606 - [i15]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio:
Learning transformed product distributions. CoRR abs/1103.0598 (2011) - [i14]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio:
Learning $k$-Modal Distributions via Testing. CoRR abs/1107.2700 (2011) - [i13]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio:
Learning Poisson Binomial Distributions. CoRR abs/1107.2702 (2011) - [i12]Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio, Gregory Valiant, Paul Valiant:
Testing $k$-Modal Distributions: Optimal Algorithms via Reductions. CoRR abs/1112.5659 (2011) - 2010
- [j2]Ilias Diakonikolas, Parikshit Gopalan, Ragesh Jaiswal, Rocco A. Servedio, Emanuele Viola:
Bounded Independence Fools Halfspaces. SIAM J. Comput. 39(8): 3441-3462 (2010) - [c10]Ilias Diakonikolas, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
A Regularity Lemma, and Low-Weight Approximators, for Low-Degree Polynomial Threshold Functions. CCC 2010: 211-222 - [c9]Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson:
Bounded Independence Fools Degree-2 Threshold Functions. FOCS 2010: 11-20 - [c8]Constantinos Daskalakis, Ilias Diakonikolas, Mihalis Yannakakis:
How Good is the Chord Algorithm?. SODA 2010: 978-991 - [c7]Ilias Diakonikolas, Prahladh Harsha
, Adam R. Klivans, Raghu Meka, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan:
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. STOC 2010: 533-542 - [i11]Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio, Yi Wu:
Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions. CoRR abs/1010.3484 (2010)
2000 – 2009
- 2009
- [j1]Ilias Diakonikolas, Mihalis Yannakakis:
Small Approximate Pareto Sets for Biobjective Shortest Paths and Other Problems. SIAM J. Comput. 39(4): 1340-1371 (2009) - [c6]Ilias Diakonikolas, Rocco A. Servedio
:
Improved Approximation of Linear Threshold Functions. CCC 2009: 161-172 - [c5]Ilias Diakonikolas, Parikshit Gopalan, Ragesh Jaiswal, Rocco A. Servedio, Emanuele Viola:
Bounded Independence Fools Halfspaces. FOCS 2009: 171-180 - [i10]Ilias Diakonikolas, Parikshit Gopalan, Ragesh Jaiswal, Rocco A. Servedio, Emanuele Viola:
Bounded Independence Fools Halfspaces. CoRR abs/0902.3757 (2009) - [i9]Ilias Diakonikolas, Rocco A. Servedio, Li-Yang Tan, Andrew Wan:
A regularity lemma, and low-weight approximators, for low-degree polynomial threshold functions. CoRR abs/0909.4727 (2009) - [i8]Ilias Diakonikolas, Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan:
Average sensitivity and noise sensitivity of polynomial threshold functions. CoRR abs/0909.5011 (2009) - [i7]Ilias Diakonikolas, Rocco A. Servedio:
Improved Approximation of Linear Threshold Functions. CoRR abs/0910.3719 (2009) - [i6]Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson:
Bounded Independence Fools Degree-2 Threshold Functions. CoRR abs/0911.3389 (2009) - [i5]Ilias Diakonikolas, Parikshit Gopalan, Ragesh Jaiswal, Rocco A. Servedio, Emanuele Viola:
Bounded Independence Fools Halfspaces. Electron. Colloquium Comput. Complex. TR09 (2009) - [i4]Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson:
Bounded Independence Fools Degree-2 Threshold Functions. Electron. Colloquium Comput. Complex. TR09 (2009) - 2008
- [c4]Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Rocco A. Servedio, Andrew Wan:
Efficiently Testing Sparse GF(2) Polynomials. ICALP (1) 2008: 502-514 - [c3]Ilias Diakonikolas, Mihalis Yannakakis:
Succinct approximate convex pareto curves. SODA 2008: 74-83 - [i3]Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Rocco A. Servedio, Andrew Wan:
Efficiently Testing Sparse GF(2) Polynomials. CoRR abs/0805.1765 (2008) - [i2]Ilias Diakonikolas, Mihalis Yannakakis:
Small Approximate Pareto Sets for Bi-objective Shortest Paths and Other Problems. CoRR abs/0805.2646 (2008) - 2007
- [c2]Ilias Diakonikolas, Mihalis Yannakakis:
Small Approximate Pareto Sets for Bi-objective Shortest Paths and Other Problems. APPROX-RANDOM 2007: 74-88 - [c1]Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Krzysztof Onak, Ronitt Rubinfeld, Rocco A. Servedio, Andrew Wan:
Testing for Concise Representations. FOCS 2007: 549-558 - [i1]Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Krzysztof Onak, Ronitt Rubinfeld, Rocco A. Servedio, Andrew Wan:
Testing for Concise Representations. Electron. Colloquium Comput. Complex. TR07 (2007)
Coauthor Index
aka: Jerry Zheng Li
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