Ilias Diakonikolas

A Quasi-Polynomial Time Mean Estimator Under Mean-Shift Contamination with Unknown Covariance COLT 2026 High-Dimensional Gaussian Mean Estimation under Realizable Contamination COLT 2026 Linear Regression under Missing or Corrupted Coordinates COLT 2026 Batch List-Decodable Linear Regression via Higher Moments ICML 2025 Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination ICML 2025 Faster Algorithms for Agnostically Learning Disjunctions and their Implications COLT 2025 Learning Intersections of Two Margin Halfspaces under Factorizable Distributions COLT 2025 On Fine-Grained Distinct Element Estimation ICML 2025 On Learning Parallel Pancakes with Mostly Uniform Weights ICML 2025 Online Linear Classification with Massart Noise ICML 2025 Robustly Learning Monotone Generalized Linear Models via Data Augmentation COLT 2025 Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise ICML 2025 Active Learning of General Halfspaces: Label Queries vs Membership Queries NeurIPS 2024 A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise NeurIPS 2024 Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials COLT 2024 Fast Co-Training under Weak Dependence via Stream-Based Active Learning ICML 2024 How Does Unlabeled Data Provably Help Out-of-Distribution Detection? ICLR 2024 Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise NeurIPS 2024 Reliable Learning of Halfspaces under Gaussian Marginals NeurIPS 2024 Robustly Learning Single-Index Models via Alignment Sharpness ICML 2024 Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination ICML 2024 Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models NeurIPS 2024 Statistical Query Lower Bounds for Learning Truncated Gaussians COLT 2024 Testable Learning of General Halfspaces with Adversarial Label Noise COLT 2024 A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points COLT 2023 A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm NeurIPS 2023 Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions COLT 2023 Efficient Testable Learning of Halfspaces with Adversarial Label Noise NeurIPS 2023 First Order Stochastic Optimization with Oblivious Noise NeurIPS 2023 Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise COLT 2023 Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA ICML 2023 Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression NeurIPS 2023 Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise NeurIPS 2023 Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals ICML 2023 Robustly Learning a Single Neuron via Sharpness ICML 2023 Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing NeurIPS 2023 Self-Directed Linear Classification COLT 2023 SQ Lower Bounds for Learning Mixtures of Linear Classifiers NeurIPS 2023 SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians COLT 2023 SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions NeurIPS 2023 Statistical and Computational Limits for Tensor-on-Tensor Association Detection COLT 2023 Cryptographic Hardness of Learning Halfspaces with Massart Noise NeurIPS 2022 Hardness of Learning a Single Neuron with Adversarial Label Noise AISTATS 2022 Learning a Single Neuron with Adversarial Label Noise via Gradient Descent COLT 2022 Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent ICML 2022 List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering NeurIPS 2022 Nearly-Tight Bounds for Testing Histogram Distributions NeurIPS 2022 Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise COLT 2022 Non-Gaussian Component Analysis via Lattice Basis Reduction COLT 2022 Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models COLT 2022 Outlier-Robust Sparse Estimation via Non-Convex Optimization NeurIPS 2022 Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions NeurIPS 2022 Robust Sparse Mean Estimation via Sum of Squares COLT 2022 SQ Lower Bounds for Learning Single Neurons with Massart Noise NeurIPS 2022 Streaming Algorithms for High-Dimensional Robust Statistics ICML 2022 Agnostic Proper Learning of Halfspaces under Gaussian Marginals COLT 2021 Boosting in the Presence of Massart Noise COLT 2021 Forster Decomposition and Learning Halfspaces with Noise NeurIPS 2021 Learning Online Algorithms with Distributional Advice ICML 2021 List-Decodable Mean Estimation in Nearly-PCA Time NeurIPS 2021 Outlier-Robust Learning of Ising Models Under Dobrushin’s Condition COLT 2021 ReLU Regression with Massart Noise NeurIPS 2021 Statistical Query Lower Bounds for List-Decodable Linear Regression NeurIPS 2021 The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model COLT 2021 The Sample Complexity of Robust Covariance Testing COLT 2021 Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks COLT 2020 Approximation Schemes for ReLU Regression COLT 2020 Efficiently Learning Adversarially Robust Halfspaces with Noise ICML 2020 High-dimensional Robust Mean Estimation via Gradient Descent ICML 2020 Learning Halfspaces with Massart Noise Under Structured Distributions COLT 2020 List-Decodable Mean Estimation via Iterative Multi-Filtering NeurIPS 2020 Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals NeurIPS 2020 Non-Convex SGD Learns Halfspaces with Adversarial Label Noise NeurIPS 2020 Outlier Robust Mean Estimation with Subgaussian Rates via Stability NeurIPS 2020 The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise NeurIPS 2020 A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families NeurIPS 2019 Communication and Memory Efficient Testing of Discrete Distributions COLT 2019 Distribution-Independent PAC Learning of Halfspaces with Massart Noise NeurIPS 2019 Equipping Experts/Bandits with Long-term Memory NeurIPS 2019 Faster Algorithms for High-Dimensional Robust Covariance Estimation COLT 2019 Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin NeurIPS 2019 On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games AAAI 2019 Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering NeurIPS 2019 Private Testing of Distributions via Sample Permutations NeurIPS 2019 Sever: A Robust Meta-Algorithm for Stochastic Optimization ICML 2019 Testing Identity of Multidimensional Histograms COLT 2019 Differentially Private Identity and Equivalence Testing of Discrete Distributions ICML 2018 Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms COLT 2018 Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities COLT 2018 Robust Learning of Fixed-Structure Bayesian Networks NeurIPS 2018 Sharp Bounds for Generalized Uniformity Testing NeurIPS 2018 Testing for Families of Distributions via the Fourier Transform NeurIPS 2018 Being Robust (in High Dimensions) Can Be Practical ICML 2017 Communication-Efficient Distributed Learning of Discrete Distributions NeurIPS 2017 Learning Multivariate Log-concave Distributions COLT 2017 Testing Bayesian Networks COLT 2017 Fast Algorithms for Segmented Regression ICML 2016 Differentially Private Learning of Structured Discrete Distributions NeurIPS 2015 Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms NeurIPS 2014