Diakonikolas, Ilias

103 publications

NeurIPS 2025 Algorithms and SQ Lower Bounds for Robustly Learning Real-Valued Multi-Index Models Ilias Diakonikolas, Giannis Iakovidis, Daniel Kane, Lisheng Ren

ICML 2025 Batch List-Decodable Linear Regression via Higher Moments Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Sihan Liu, Thanasis Pittas

ICML 2025 Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination Ilias Diakonikolas, Giannis Iakovidis, Daniel Kane, Thanasis Pittas

COLT 2025 Faster Algorithms for Agnostically Learning Disjunctions and Their Implications Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren

NeurIPS 2025 Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination Ilias Diakonikolas, Chao Gao, Daniel Kane, John Lafferty, Ankit Pensia

COLT 2025 Learning Intersections of Two Margin Halfspaces Under Factorizable Distributions Ilias Diakonikolas, Ma Mingchen, Ren Lisheng, Tzamos Christos

ICML 2025 On Fine-Grained Distinct Element Estimation Ilias Diakonikolas, Daniel Kane, Jasper C.H. Lee, Thanasis Pittas, David Woodruff, Samson Zhou

ICML 2025 On Learning Parallel Pancakes with Mostly Uniform Weights Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Jasper C.H. Lee, Thanasis Pittas

ICML 2025 Online Linear Classification with Massart Noise Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

NeurIPS 2025 Replicable Distribution Testing Ilias Diakonikolas, Jingyi Gao, Daniel Kane, Sihan Liu, Christopher Ye

NeurIPS 2025 Robust Regression of General ReLUs with Queries Ilias Diakonikolas, Daniel Kane, Mingchen Ma

COLT 2025 Robustly Learning Monotone Generalized Linear Models via Data Augmentation Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas

NeurIPS 2025 Robustly Learning Monotone Single-Index Models Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

ICML 2025 Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos

NeurIPS 2024 A Near-Optimal Algorithm for Learning Margin Halfspaces with Massart Noise Ilias Diakonikolas, Nikos Zarifis

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

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

ICML 2024 Fast Co-Training Under Weak Dependence via Stream-Based Active Learning Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos

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

NeurIPS 2024 Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas, Jelena Diakonikolas

NeurIPS 2024 Reliable Learning of Halfspaces Under Gaussian Marginals Ilias Diakonikolas, Lisheng Ren, Nikos Zarifis

ICML 2024 Robust Sparse Estimation for Gaussians with Optimal Error Under Huber Contamination Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas

ICML 2024 Robustly Learning Single-Index Models via Alignment Sharpness Nikos Zarifis, Puqian Wang, 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

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

COLT 2024 Testable Learning of General Halfspaces with Adversarial Label Noise Ilias Diakonikolas, Daniel Kane, Sihan Liu, Nikos Zarifis

COLT 2023 A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points Daniel Kane, Ilias Diakonikolas

NeurIPS 2023 A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm Ilias Diakonikolas, Daniel Kane, Jasper Lee, Ankit Pensia, Thanasis Pittas

COLT 2023 Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions Ilias Diakonikolas, Sushrut Karmalkar, Jong Ho Park, Christos Tzamos

NeurIPS 2023 Efficient Testable Learning of Halfspaces with Adversarial Label Noise Ilias Diakonikolas, Daniel Kane, Vasilis Kontonis, Sihan Liu, Nikos Zarifis

NeurIPS 2023 First Order Stochastic Optimization with Oblivious Noise Ilias Diakonikolas, Sushrut Karmalkar, Jong Ho Park, Christos Tzamos

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

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

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

ICML 2023 Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression Under Gaussian Marginals Ilias Diakonikolas, Daniel Kane, Lisheng Ren

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

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

ICML 2023 Robustly Learning a Single Neuron via Sharpness Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas

NeurIPS 2023 SQ Lower Bounds for Learning Mixtures of Linear Classifiers Ilias Diakonikolas, Daniel Kane, Yuxin Sun

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

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

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

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

AISTATS 2022 Hardness of Learning a Single Neuron with Adversarial Label Noise Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren

NeurIPS 2022 Cryptographic Hardness of Learning Halfspaces with Massart Noise Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren

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

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

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

COLT 2022 Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise Ilias Diakonikolas, Daniel Kane

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

COLT 2022 Non-Gaussian Component Analysis via Lattice Basis Reduction Ilias Diakonikolas, Daniel Kane

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

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

NeurIPS 2022 Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions Ilias Diakonikolas, Daniel Kane, Jasper Lee, Ankit Pensia

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

NeurIPS 2022 SQ Lower Bounds for Learning Single Neurons with Massart Noise Ilias Diakonikolas, Daniel Kane, Lisheng Ren, Yuxin Sun

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

COLT 2021 Agnostic Proper Learning of Halfspaces Under Gaussian Marginals Ilias Diakonikolas, Daniel M Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

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

NeurIPS 2021 Forster Decomposition and Learning Halfspaces with Noise Ilias Diakonikolas, Daniel Kane, Christos Tzamos

ICML 2021 Learning Online Algorithms with Distributional Advice Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis

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

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

NeurIPS 2021 ReLU Regression with Massart Noise Ilias Diakonikolas, Jong Ho Park, Christos Tzamos

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

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 The Sample Complexity of Robust Covariance Testing Ilias Diakonikolas, Daniel M. Kane

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 Approximation Schemes for ReLU Regression Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi

ICML 2020 Efficiently Learning Adversarially Robust Halfspaces with Noise Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro

ICML 2020 High-Dimensional Robust Mean Estimation via Gradient Descent Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi

COLT 2020 Learning Halfspaces with Massart Noise Under Structured Distributions Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

NeurIPS 2020 List-Decodable Mean Estimation via Iterative Multi-Filtering Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard

NeurIPS 2020 Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs Under Gaussian Marginals Ilias Diakonikolas, Daniel Kane, Nikos Zarifis

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

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

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

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

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

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

NeurIPS 2019 Equipping Experts/Bandits with Long-Term Memory Kai Zheng, Haipeng Luo, Ilias Diakonikolas, Liwei Wang

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

NeurIPS 2019 Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi

AAAI 2019 On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games Ilias Diakonikolas, Chrystalla Pavlou

NeurIPS 2019 Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ecprice, Alistair Stewart

NeurIPS 2019 Private Testing of Distributions via Sample Permutations Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld

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

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

ICML 2018 Differentially Private Identity and Equivalence Testing of Discrete Distributions Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld

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

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

NeurIPS 2018 Robust Learning of Fixed-Structure Bayesian Networks Yu Cheng, Ilias Diakonikolas, Daniel Kane, Alistair Stewart

NeurIPS 2018 Sharp Bounds for Generalized Uniformity Testing Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart

NeurIPS 2018 Testing for Families of Distributions via the Fourier Transform Clément L Canonne, Ilias Diakonikolas, Alistair Stewart

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

NeurIPS 2017 Communication-Efficient Distributed Learning of Discrete Distributions Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt

COLT 2017 Learning Multivariate Log-Concave Distributions Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart

COLT 2017 Testing Bayesian Networks Clement L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart

ICML 2016 Fast Algorithms for Segmented Regression Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt

COLT 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

NeurIPS 2015 Differentially Private Learning of Structured Discrete Distributions Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt

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