← Authors

Ilias Diakonikolas

96 papers · 2014–2025 · 6 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (6) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (7) πŸƒ Academic Marathon (11) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🏠 Conference Loyalist (40) πŸ† Keyword Champion (10) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (35) 🧬 Topic Evolution 🀝 Dynamic Duo (34) πŸ”₯ Unstoppable (12) ⚑ Prolific Year (10) ❓ The Questioner πŸ’Ž Century Club (96) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (53) πŸš€ Conference Pioneer

Conferences

NIPS (40) COLT (32) ICML (21) AAAI (1) AISTATS (1) ICLR (1)

Top co-authors

Daniel Kane (34) Daniel M. Kane (23) Nikos Zarifis (23) Christos Tzamos (16) Thanasis Pittas (15) Alistair Stewart (12) Sushrut Karmalkar (10) Vasilis Kontonis (10) Ankit Pensia (10) Lisheng Ren (9)

Research topics

Statistics (3) Differential Privacy (2) Probability (1)

Keywords

sample complexity (19) pac learning (13) statistical query (12) adversarial label noise (10) robust statistics (10) gaussian distribution (10) massart noise (9) halfspace learning (9) learning theory (8) mean estimation (8) agnostic learning (7) outlier detection (7) total variation distance (6) high-dimensional statistics (6) robust learning (6) distribution testing (6) single neuron (5) hypothesis testing (5) lower bound (5) statistical query lower bound (5)

Papers

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