Small Approximate Pareto Sets for Bi-objective Shortest Paths and Other Problems. - Ilias Diakonikolas; Mihalis Yannakakis (APPROX-RANDOM, 2007)

Testing for Concise Representations. - Ilias Diakonikolas; Homin K. Lee; Kevin Matulef; Krzysztof Onak; Ronitt Rubinfeld; Rocco A. Servedio; Andrew Wan (Electron. Colloquium Comput. Complex., 2007)

Efficiently Testing Sparse GF(2) Polynomials. - Ilias Diakonikolas; Homin K. Lee; Kevin Matulef; Rocco A. Servedio; Andrew Wan (ICALP (1), 2008)

Succinct approximate convex pareto curves. - Ilias Diakonikolas; Mihalis Yannakakis (SODA, 2008)

Bounded Independence Fools Degree-2 Threshold Functions. - Ilias Diakonikolas; Daniel M. Kane; Jelani Nelson (Electron. Colloquium Comput. Complex., 2009)

Small Approximate Pareto Sets for Biobjective Shortest Paths and Other Problems. - Ilias Diakonikolas; Mihalis Yannakakis (SIAM J. Comput., 2009)

Bounding the average sensitivity and noise sensitivity of polynomial threshold functions. - Ilias Diakonikolas; Prahladh Harsha; Adam R. Klivans; Raghu Meka; Prasad Raghavendra; Rocco A. Servedio; Li-Yang Tan (STOC, 2010)

Bounded Independence Fools Halfspaces. - Ilias Diakonikolas; Parikshit Gopalan; Ragesh Jaiswal; Rocco A. Servedio; Emanuele Viola (SIAM J. Comput., 2010)

Disjoint-Path Facility Location: Theory and Practice. - Lee Breslau; Ilias Diakonikolas; Nick G. Duffield; Yu Gu 004; Mohammad Taghi Hajiaghayi; David S. Johnson 001; Howard J. Karloff; Mauricio G. C. Resende; Subhabrata Sen (ALENEX, 2011)

Supervised design space exploration by compositional approximation of Pareto sets. - Hung-Yi Liu; Ilias Diakonikolas; Michele Petracca; Luca P. Carloni (DAC, 2011)

Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions. - Ilias Diakonikolas; Ryan O'Donnell; Rocco A. Servedio; Yi Wu 002 (SODA, 2011)

Efficiently Testing Sparse GF(2) Polynomials. - Ilias Diakonikolas; Homin K. Lee; Kevin Matulef; Rocco A. Servedio; Andrew Wan (Algorithmica, 2011)

Learning transformed product distributions - Constantinos Daskalakis; Ilias Diakonikolas; Rocco A. Servedio (CoRR, 2011)

Learning $k$-Modal Distributions via Testing - Constantinos Daskalakis; Ilias Diakonikolas; Rocco A. Servedio (CoRR, 2011)

Testing $k$-Modal Distributions: Optimal Algorithms via Reductions - Constantinos Daskalakis; Ilias Diakonikolas; Rocco A. Servedio; Gregory Valiant; Paul Valiant (CoRR, 2011)

Approximation of Multiobjective Optimization Problems. - Ilias Diakonikolas (2011)

Efficiency-Revenue Trade-Offs in Auctions. - Ilias Diakonikolas; Christos H. Papadimitriou; George Pierrakos; Yaron Singer (ICALP (2), 2012)

On the Distribution of the Fourier Spectrum of Halfspaces - Ilias Diakonikolas; Ragesh Jaiswal; Rocco A. Servedio; Li-Yang Tan; Andrew Wan (CoRR, 2012)

Nearly optimal solutions for the Chow Parameters Problem and low-weight approximation of halfspaces. - Anindya De; Ilias Diakonikolas; Vitaly Feldman; Rocco A. Servedio (Electron. Colloquium Comput. Complex., 2012)

Inverse Problems in Approximate Uniform Generation. - Anindya De; Ilias Diakonikolas; Rocco A. Servedio (Electron. Colloquium Comput. Complex., 2012)

The Inverse Shapley Value Problem. - Anindya De; Ilias Diakonikolas; Rocco A. Servedio (Electron. Colloquium Comput. Complex., 2012)

Learning Sums of Independent Integer Random Variables. - Constantinos Daskalakis; Ilias Diakonikolas; Ryan O'Donnell; Rocco A. Servedio; Li-Yang Tan (FOCS, 2013)

Learning mixtures of structured distributions over discrete domains. - Siu On Chan; Ilias Diakonikolas; Rocco A. Servedio; Xiaorui Sun (SODA, 2013)

Testing k-Modal Distributions: Optimal Algorithms via Reductions. - Constantinos Daskalakis; Ilias Diakonikolas; Rocco A. Servedio; Gregory Valiant; Paul Valiant (SODA, 2013)

Improved Approximation of Linear Threshold Functions. - Ilias Diakonikolas; Rocco A. Servedio (Comput. Complex., 2013)

Deterministic Approximate Counting for Juntas of Degree-2 Polynomial Threshold Functions. - Anindya De; Ilias Diakonikolas; Rocco A. Servedio (Electron. Colloquium Comput. Complex., 2013)

Deterministic Approximate Counting for Degree-2 Polynomial Threshold Functions. - Anindya De; Ilias Diakonikolas; Rocco A. Servedio (Electron. Colloquium Comput. Complex., 2013)

Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms. - Siu On Chan; Ilias Diakonikolas; Rocco A. Servedio; Xiaorui Sun (NIPS, 2014)

Optimal Algorithms for Testing Closeness of Discrete Distributions. - Siu On Chan; Ilias Diakonikolas; Paul Valiant; Gregory Valiant (SODA, 2014)

The Complexity of Optimal Multidimensional Pricing. - Xi Chen 001; Ilias Diakonikolas; Dimitris Paparas; Xiaorui Sun; Mihalis Yannakakis (SODA, 2014)

A Polynomial-time Approximation Scheme for Fault-tolerant Distributed Storage. - Constantinos Daskalakis; Anindya De; Ilias Diakonikolas; Ankur Moitra; Rocco A. Servedio (SODA, 2014)

Efficient density estimation via piecewise polynomial approximation. - Siu On Chan; Ilias Diakonikolas; Rocco A. Servedio; Xiaorui Sun (STOC, 2014)

Average Sensitivity and Noise Sensitivity of Polynomial Threshold Functions. - Ilias Diakonikolas; Prasad Raghavendra; Rocco A. Servedio; Li-Yang Tan (SIAM J. Comput., 2014)

Learning k-Modal Distributions via Testing. - Constantinos Daskalakis; Ilias Diakonikolas; Rocco A. Servedio (Theory Comput., 2014)

A Regularity Lemma and Low-Weight Approximators for Low-Degree Polynomial Threshold Functions. - Ilias Diakonikolas; Rocco A. Servedio; Li-Yang Tan; Andrew Wan (Theory Comput., 2014)

On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms. - Xi Chen 001; Ilias Diakonikolas; Anthi Orfanou; Dimitris Paparas; Xiaorui Sun; Mihalis Yannakakis (FOCS, 2015)

Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. - Ilias Diakonikolas; Daniel M. Kane; Vladimir Nikishkin (FOCS, 2015)

Differentially Private Learning of Structured Discrete Distributions. - Ilias Diakonikolas; Moritz Hardt; Ludwig Schmidt (NIPS, 2015)

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. - Jayadev Acharya; Ilias Diakonikolas; Chinmay Hegde; Jerry Zheng Li; Ludwig Schmidt (PODS, 2015)

Learning from satisfying assignments. - Anindya De; Ilias Diakonikolas; Rocco A. Servedio (SODA, 2015)

Testing Identity of Structured Distributions. - Ilias Diakonikolas; Daniel M. Kane; Vladimir Nikishkin (SODA, 2015)

Learning Poisson Binomial Distributions. - Constantinos Daskalakis; Ilias Diakonikolas; Rocco A. Servedio (Algorithmica, 2015)

Noise Stable Halfspaces are Close to Very Small Juntas. - Ilias Diakonikolas; Ragesh Jaiswal; Rocco A. Servedio; Li-Yang Tan; Andrew Wan (Chic. J. Theor. Comput. Sci., 2015)

Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (CoRR, 2015)

Learning Structured Distributions. - Ilias Diakonikolas (Handbook of Big Data, 2016)

Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (COLT, 2016)

Properly Learning Poisson Binomial Distributions in Almost Polynomial Time. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (COLT, 2016)

Fast Algorithms for Segmented Regression. - Jayadev Acharya; Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (ICML, 2016)

The fourier transform of poisson multinomial distributions and its algorithmic applications. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2016)

Efficient Robust Proper Learning of Log-concave Distributions. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (CoRR, 2016)

A New Approach for Testing Properties of Discrete Distributions. - Ilias Diakonikolas; Daniel M. Kane (Electron. Colloquium Comput. Complex., 2016)

Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (Electron. Colloquium Comput. Complex., 2016)

How Good is the Chord Algorithm? - Constantinos Daskalakis; Ilias Diakonikolas; Mihalis Yannakakis (SIAM J. Comput., 2016)

A Robust Khintchine Inequality, and Algorithms for Computing Optimal Constants in Fourier Analysis and High-Dimensional Geometry. - Anindya De; Ilias Diakonikolas; Rocco A. Servedio (SIAM J. Discret. Math., 2016)

Learning Multivariate Log-concave Distributions. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (COLT, 2017)

Near-Optimal Closeness Testing of Discrete Histogram Distributions. - Ilias Diakonikolas; Daniel M. Kane; Vladimir Nikishkin (ICALP, 2017)

Being Robust (in High Dimensions) Can Be Practical. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (ICML, 2017)

Communication-Efficient Distributed Learning of Discrete Distributions. - Ilias Diakonikolas; Elena Grigorescu; Jerry Li 001; Abhiram Natarajan; Krzysztof Onak; Ludwig Schmidt (NIPS, 2017)

Sample-Optimal Density Estimation in Nearly-Linear Time. - Jayadev Acharya; Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (SODA, 2017)

Playing Anonymous Games using Simple Strategies. - Yu Cheng 002; Ilias Diakonikolas; Alistair Stewart (SODA, 2017)

Differentially Private Identity and Closeness Testing of Discrete Distributions. - Maryam Aliakbarpour; Ilias Diakonikolas; Ronitt Rubinfeld (CoRR, 2017)

Fourier-Based Testing for Families of Distributions. - Clément L. Canonne; Ilias Diakonikolas; Alistair Stewart (Electron. Colloquium Comput. Complex., 2017)

Sample-Optimal Identity Testing with High Probability. - Ilias Diakonikolas; Themis Gouleakis; John Peebles; Eric Price 001 (Electron. Colloquium Comput. Complex., 2017)

Sharp Bounds for Generalized Uniformity Testing. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (Electron. Colloquium Comput. Complex., 2017)

Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities. - Timothy Carpenter; Ilias Diakonikolas; Anastasios Sidiropoulos; Alistair Stewart (COLT, 2018)

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. - Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (COLT, 2018)

Differentially Private Identity and Equivalence Testing of Discrete Distributions. - Maryam Aliakbarpour; Ilias Diakonikolas; Ronitt Rubinfeld (ICML, 2018)

Robust Learning of Fixed-Structure Bayesian Networks. - Yu Cheng 002; Ilias Diakonikolas; Daniel Kane 001; Alistair Stewart (NeurIPS, 2018)

Testing for Families of Distributions via the Fourier Transform. - Alistair Stewart; Ilias Diakonikolas; Clément L. Canonne (NeurIPS, 2018)

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (SODA, 2018)

Testing conditional independence of discrete distributions. - Clément L. Canonne; Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2018)

List-decodable robust mean estimation and learning mixtures of spherical gaussians. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2018)

Learning geometric concepts with nasty noise. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2018)

A Polynomial Time Algorithm for Maximum Likelihood Estimation of Multivariate Log-concave Densities. - Ilias Diakonikolas; Anastasios Sidiropoulos; Alistair Stewart (CoRR, 2018)

Degree-$d$ Chow Parameters Robustly Determine Degree-$d$ PTFs (and Algorithmic Applications). - Ilias Diakonikolas; Daniel Kane 001 (Electron. Colloquium Comput. Complex., 2018)

The complexity of optimal multidimensional pricing for a unit-demand buyer. - Xi Chen 001; Ilias Diakonikolas; Dimitris Paparas; Xiaorui Sun; Mihalis Yannakakis (Games Econ. Behav., 2018)

Testing Shape Restrictions of Discrete Distributions. - Clément L. Canonne; Ilias Diakonikolas; Themis Gouleakis; Ronitt Rubinfeld (Theory Comput. Syst., 2018)

On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games. - Ilias Diakonikolas; Chrystalla Pavlou (AAAI, 2019)

Faster Algorithms for High-Dimensional Robust Covariance Estimation. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; David P. Woodruff (COLT, 2019)

Communication and Memory Efficient Testing of Discrete Distributions. - Ilias Diakonikolas; Themis Gouleakis; Daniel M. Kane; Sankeerth Rao (COLT, 2019)

Testing Identity of Multidimensional Histograms. - Ilias Diakonikolas; Daniel M. Kane; John Peebles (COLT, 2019)

Sever: A Robust Meta-Algorithm for Stochastic Optimization. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Jacob Steinhardt; Alistair Stewart (ICML, 2019)

Private Testing of Distributions via Sample Permutations. - Maryam Aliakbarpour; Ilias Diakonikolas; Daniel Kane 001; Ronitt Rubinfeld (NeurIPS, 2019)

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. - Brian Axelrod; Ilias Diakonikolas; Alistair Stewart; Anastasios Sidiropoulos; Gregory Valiant (NeurIPS, 2019)

Distribution-Independent PAC Learning of Halfspaces with Massart Noise. - Ilias Diakonikolas; Themis Gouleakis; Christos Tzamos (NeurIPS, 2019)

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. - Ilias Diakonikolas; Daniel Kane 001; Sushrut Karmalkar; Eric Price 001; Alistair Stewart (NeurIPS, 2019)

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi (NeurIPS, 2019)

Equipping Experts/Bandits with Long-term Memory. - Kai Zheng 007; Haipeng Luo; Ilias Diakonikolas; Liwei Wang 001 (NeurIPS, 2019)

High-Dimensional Robust Mean Estimation in Nearly-Linear Time. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001 (SODA, 2019)

Efficient Algorithms and Lower Bounds for Robust Linear Regression. - Ilias Diakonikolas; Weihao Kong; Alistair Stewart (SODA, 2019)

Degree-푑 chow parameters robustly determine degree-푑 PTFs (and algorithmic applications). - Ilias Diakonikolas; Daniel M. Kane (STOC, 2019)

Collision-Based Testers are Optimal for Uniformity and Closeness. - Ilias Diakonikolas; Themis Gouleakis; John Peebles; Eric Price 001 (Chic. J. Theor. Comput. Sci., 2019)

Recent Advances in Algorithmic High-Dimensional Robust Statistics. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2019)

Robust Estimators in High-Dimensions Without the Computational Intractability. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Ankur Moitra; Alistair Stewart (SIAM J. Comput., 2019)

Robust High-Dimensional Statistics. - Ilias Diakonikolas; Daniel M. Kane (Beyond the Worst-Case Analysis of Algorithms, 2020)

Approximation Schemes for ReLU Regression. - Ilias Diakonikolas; Surbhi Goel; Sushrut Karmalkar; Adam R. Klivans; Mahdi Soltanolkotabi (COLT, 2020)

Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Nikos Zarifis (COLT, 2020)

Learning Halfspaces with Massart Noise Under Structured Distributions. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2020)

Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. - Ainesh Bakshi; Ilias Diakonikolas; Samuel B. Hopkins 001; Daniel Kane 001; Sushrut Karmalkar; Pravesh K. Kothari (FOCS, 2020)

Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. - Ilias Diakonikolas; Daniel M. Kane (FOCS, 2020)

High-dimensional Robust Mean Estimation via Gradient Descent. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Mahdi Soltanolkotabi (ICML, 2020)

Efficiently Learning Adversarially Robust Halfspaces with Noise. - Omar Montasser; Surbhi Goel; Ilias Diakonikolas; Nathan Srebro (ICML, 2020)

List-Decodable Mean Estimation via Iterative Multi-Filtering. - Ilias Diakonikolas; Daniel Kane 001; Daniel Kongsgaard (NeurIPS, 2020)

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise. - Ilias Diakonikolas; Daniel M. Kane; Pasin Manurangsi (NeurIPS, 2020)

Outlier Robust Mean Estimation with Subgaussian Rates via Stability. - Ilias Diakonikolas; Daniel M. Kane; Ankit Pensia (NeurIPS, 2020)

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (NeurIPS, 2020)

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. - Ilias Diakonikolas; Daniel Kane 001; Nikos Zarifis (NeurIPS, 2020)

Efficient Algorithms for Multidimensional Segmented Regression. - Ilias Diakonikolas; Jerry Li 001; Anastasia Voloshinov (CoRR, 2020)

Robustly Learning any Clusterable Mixture of Gaussians. - Ilias Diakonikolas; Samuel B. Hopkins 001; Daniel Kane 001; Sushrut Karmalkar (CoRR, 2020)

Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

Robustly Learning Mixtures of k Arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (CoRR, 2020)

Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2020)

Optimal Testing of Discrete Distributions with High Probability. - Ilias Diakonikolas; Themis Gouleakis; Daniel Kane 001; John Peebles; Eric Price 001 (Electron. Colloquium Comput. Complex., 2020)

Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs. - David S. Johnson 001; Lee Breslau; Ilias Diakonikolas; Nick Duffield; Yu Gu 004; MohammadTaghi Hajiaghayi; Howard J. Karloff; Mauricio G. C. Resende; Subhabrata Sen (Oper. Res., 2020)

Testing Bayesian Networks. - Clément L. Canonne; Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (IEEE Trans. Inf. Theory, 2020)

Boosting in the Presence of Massart Noise. - Ilias Diakonikolas; Russell Impagliazzo; Daniel M. Kane; Rex Lei; Jessica Sorrell; Christos Tzamos (COLT, 2021)

The Sample Complexity of Robust Covariance Testing. - Ilias Diakonikolas; Daniel M. Kane (COLT, 2021)

Agnostic Proper Learning of Halfspaces under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2021)

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (COLT, 2021)

Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart; Yuxin Sun (COLT, 2021)

Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. - Stephen Macke; Maryam Aliakbarpour; Ilias Diakonikolas; Aditya G. Parameswaran; Ronitt Rubinfeld (ICDE, 2021)

Learning Online Algorithms with Distributional Advice. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Ali Vakilian; Nikos Zarifis (ICML, 2021)

List-Decodable Mean Estimation in Nearly-PCA Time. - Ilias Diakonikolas; Daniel Kane 001; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (NeurIPS, 2021)

Statistical Query Lower Bounds for List-Decodable Linear Regression. - Ilias Diakonikolas; Daniel Kane 001; Ankit Pensia; Thanasis Pittas; Alistair Stewart (NeurIPS, 2021)

Forster Decomposition and Learning Halfspaces with Noise. - Ilias Diakonikolas; Daniel Kane 001; Christos Tzamos (NeurIPS, 2021)

ReLU Regression with Massart Noise. - Ilias Diakonikolas; Jongho Park 004; Christos Tzamos (NeurIPS, 2021)

Efficiently learning halfspaces with Tsybakov noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2021)

Robustness meets algorithms. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (Commun. ACM, 2021)

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (CoRR, 2021)

Threshold Phenomena in Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2021)

Hardness of Learning a Single Neuron with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi; Lisheng Ren (AISTATS, 2022)

Efficient Approximation Algorithms for the Inverse Semivalue Problem. - Ilias Diakonikolas; Chrystalla Pavlou; John Peebles; Alistair Stewart (AAMAS, 2022)

Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001 (COLT, 2022)

Non-Gaussian Component Analysis via Lattice Basis Reduction. - Ilias Diakonikolas; Daniel Kane 001 (COLT, 2022)

Robust Sparse Mean Estimation via Sum of Squares. - Ilias Diakonikolas; Daniel M. Kane; Sushrut Karmalkar; Ankit Pensia; Thanasis Pittas (COLT, 2022)

Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. - Ilias Diakonikolas; Daniel M. Kane; Yuxin Sun (COLT, 2022)

Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2022)

Streaming Algorithms for High-Dimensional Robust Statistics. - Ilias Diakonikolas; Daniel M. Kane; Ankit Pensia; Thanasis Pittas (ICML, 2022)

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (ICML, 2022)

Outlier-Robust Sparse Estimation via Non-Convex Optimization. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Shivam Gupta 002; Daniel Kane 001; Mahdi Soltanolkotabi (NeurIPS, 2022)

Nearly-Tight Bounds for Testing Histogram Distributions. - Clément L. Canonne; Ilias Diakonikolas; Daniel Kane 001; Sihan Liu (NeurIPS, 2022)

List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. - Ilias Diakonikolas; Daniel Kane 001; Sushrut Karmalkar; Ankit Pensia; Thanasis Pittas (NeurIPS, 2022)

Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. - Ilias Diakonikolas; Daniel Kane 001; Jasper C. H. Lee; Ankit Pensia (NeurIPS, 2022)

Cryptographic Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi; Lisheng Ren (NeurIPS, 2022)

SQ Lower Bounds for Learning Single Neurons with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001; Lisheng Ren; Yuxin Sun (NeurIPS, 2022)

Robustly learning mixtures of k arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (STOC, 2022)

Clustering mixture models in almost-linear time via list-decodable mean estimation. - Ilias Diakonikolas; Daniel M. Kane; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (STOC, 2022)

Learning general halfspaces with general Massart noise under the Gaussian distribution. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2022)

Near-Optimal Bounds for Testing Histogram Distributions. - Clément L. Canonne; Ilias Diakonikolas; Daniel M. Kane; Sihan Liu (CoRR, 2022)

A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. - Ilias Diakonikolas; Christos Tzamos; Daniel Kane 001 (Electron. Colloquium Comput. Complex., 2022)

On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms for a Unit-Demand Buyer. - Xi Chen 001; Ilias Diakonikolas; Anthi Orfanou; Dimitris Paparas; Xiaorui Sun; Mihalis Yannakakis (SIAM J. Comput., 2022)

Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel M. Kane; Puqian Wang; Nikos Zarifis (COLT, 2023)

Statistical and Computational Limits for Tensor-on-Tensor Association Detection. - Ilias Diakonikolas; Daniel M. Kane; Yuetian Luo; Anru Zhang (COLT, 2023)

Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. - Ilias Diakonikolas; Sushrut Karmalkar; Jongho Park 004; Christos Tzamos (COLT, 2023)

SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (COLT, 2023)

Self-Directed Linear Classification. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (COLT, 2023)

A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. - Daniel Kane 001; Ilias Diakonikolas (COLT, 2023)

Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. - Ilias Diakonikolas; Daniel Kane 001; Ankit Pensia; Thanasis Pittas (ICML, 2023)

Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. - Ilias Diakonikolas; Daniel Kane 001; Lisheng Ren (ICML, 2023)

Robustly Learning a Single Neuron via Sharpness. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (ICML, 2023)

Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel Kane 001; Puqian Wang; Nikos Zarifis (NeurIPS, 2023)

Efficient Testable Learning of Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (NeurIPS, 2023)

A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm. - Ilias Diakonikolas; Daniel Kane 001; Jasper C. H. Lee; Ankit Pensia; Thanasis Pittas (NeurIPS, 2023)

Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. - Ilias Diakonikolas; Daniel Kane 001; Ankit Pensia; Thanasis Pittas (NeurIPS, 2023)

First Order Stochastic Optimization with Oblivious Noise. - Ilias Diakonikolas; Sushrut Karmalkar; Jongho Park 004; Christos Tzamos (NeurIPS, 2023)

SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. - Ilias Diakonikolas; Daniel Kane 001; Lisheng Ren; Yuxin Sun (NeurIPS, 2023)

SQ Lower Bounds for Learning Mixtures of Linear Classifiers. - Ilias Diakonikolas; Daniel Kane 001; Yuxin Sun (NeurIPS, 2023)

Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. - Shuyao Li 001; Yu Cheng 002; Ilias Diakonikolas; Jelena Diakonikolas; Rong Ge 001; Stephen J. Wright 001 (NeurIPS, 2023)

Gaussian Mean Testing Made Simple. - Ilias Diakonikolas; Daniel M. Kane; Ankit Pensia (SOSA, 2023)

SQ Lower Bounds for Learning Bounded Covariance GMMs. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (CoRR, 2023)

Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2023)

Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. - Ilias Diakonikolas; Daniel M. Kane (COLT, 2024)

Testable Learning of General Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Nikos Zarifis (COLT, 2024)

Statistical Query Lower Bounds for Learning Truncated Gaussians. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (COLT, 2024)

Agnostically Learning Multi-Index Models with Queries. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (FOCS, 2024)

Sum-of-Squares Lower Bounds for Non-Gaussian Component Analysis. - Ilias Diakonikolas; Sushrut Karmalkar; Shuo Pang 002; Aaron Potechin (FOCS, 2024)

How Does Unlabeled Data Provably Help Out-of-Distribution Detection? - Xuefeng Du; Zhen Fang 001; Ilias Diakonikolas; Yixuan Li 001 (ICLR, 2024)

Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. - Ilias Diakonikolas; Daniel Kane 001; Sushrut Karmalkar; Ankit Pensia; Thanasis Pittas (ICML, 2024)

Fast Co-Training under Weak Dependence via Stream-Based Active Learning. - Ilias Diakonikolas; Mingchen Ma; Lisheng Ren; Christos Tzamos (ICML, 2024)

Robustly Learning Single-Index Models via Alignment Sharpness. - Nikos Zarifis; Puqian Wang; Ilias Diakonikolas; Jelena Diakonikolas (ICML, 2024)

Active Learning of General Halfspaces: Label Queries vs Membership Queries. - Ilias Diakonikolas; Daniel M. Kane; Mingchen Ma (NeurIPS, 2024)

Reliable Learning of Halfspaces under Gaussian Marginals. - Ilias Diakonikolas; Lisheng Ren; Nikos Zarifis (NeurIPS, 2024)

A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise. - Ilias Diakonikolas; Nikos Zarifis (NeurIPS, 2024)

Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise. - Shuyao Li 001; Sushrut Karmalkar; Ilias Diakonikolas; Jelena Diakonikolas (NeurIPS, 2024)

Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (NeurIPS, 2024)

Online Robust Mean Estimation. - Daniel M. Kane; Ilias Diakonikolas; Hanshen Xiao; Sihan Liu (SODA, 2024)

Super Non-singular Decompositions of Polynomials and Their Application to Robustly Learning Low-Degree PTFs. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (STOC, 2024)

Testing Closeness of Multivariate Distributions via Ramsey Theory. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu (STOC, 2024)

Online Learning of Halfspaces with Massart Noise. - Ilias Diakonikolas; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2024)

Efficient Testable Learning of General Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Nikos Zarifis (CoRR, 2024)

Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2024)

Faster Algorithms for Agnostically Learning Disjunctions and their Implications. - Ilias Diakonikolas; Daniel M. Kane; Lisheng Ren (COLT, 2025)

Learning Intersections of Two Margin Halfspaces under Factorizable Distributions. - Ilias Diakonikolas; Mingchen Ma; Lisheng Ren; Christos Tzamos (COLT, 2025)

Robustly Learning Monotone Generalized Linear Models via Data Augmentation. - Nikos Zarifis; Puqian Wang; Ilias Diakonikolas; Jelena Diakonikolas (COLT, 2025)

Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation. - Ilias Diakonikolas; Daniel M. Kane; Jasper C. H. Lee; Thanasis Pittas (SODA, 2025)

SoS Certifiability of Subgaussian Distributions and Its Algorithmic Applications. - Ilias Diakonikolas; Samuel B. Hopkins 001; Ankit Pensia; Stefan Tiegel (STOC, 2025)

SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More. - Ilias Diakonikolas; Samuel B. Hopkins 001; Ankit Pensia; Stefan Tiegel (STOC, 2025)

Entangled Mean Estimation in High Dimensions. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Thanasis Pittas (STOC, 2025)

Robust Learning of Multi-index Models via Iterative Subspace Approximation. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Nikos Zarifis (CoRR, 2025)

Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise. - Ilias Diakonikolas; Mingchen Ma; Lisheng Ren; Christos Tzamos (CoRR, 2025)

Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Thanasis Pittas (CoRR, 2025)

Batch List-Decodable Linear Regression via Higher Moments. - Ilias Diakonikolas; Daniel M. Kane; Sushrut Karmalkar; Sihan Liu; Thanasis Pittas (CoRR, 2025)

On Learning Parallel Pancakes with Mostly Uniform Weights. - Ilias Diakonikolas; Daniel M. Kane; Sushrut Karmalkar; Jasper C. H. Lee; Thanasis Pittas (CoRR, 2025)

Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Lisheng Ren (CoRR, 2025)

On Fine-Grained Distinct Element Estimation. - Ilias Diakonikolas; Daniel M. Kane; Jasper C. H. Lee; Thanasis Pittas; David P. Woodruff; Samson Zhou (CoRR, 2025)

Replicable Distribution Testing. - Ilias Diakonikolas; Jingyi Gao; Daniel M. Kane; Sihan Liu; Christopher Ye 001 (CoRR, 2025)

Distributionally Robust Optimization with Adversarial Data Contamination. - Shuyao Li 001; Ilias Diakonikolas; Jelena Diakonikolas (CoRR, 2025)

Robustly Learning Monotone Single-Index Models. - Puqian Wang; Nikos Zarifis; Ilias Diakonikolas; Jelena Diakonikolas (CoRR, 2025)