| 2026 | COLT | Linear Regression under Missing or Corrupted Coordinates. | Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas |
| 2026 | COLT | A Quasi-Polynomial Time Mean Estimator Under Mean-Shift Contamination with Unknown Covariance. | Ilias Diakonikolas, Jingyi Gao, Giannis Iakovidis, Daniel M. Kane, Sihan Liu, Thanasis Pittas |
| 2026 | COLT | High-Dimensional Gaussian Mean Estimation under Realizable Contamination. | Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas |
| 2025 | COLT | Faster Algorithms for Agnostically Learning Disjunctions and their Implications. | Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren |
| 2025 | COLT | Learning Intersections of Two Margin Halfspaces under Factorizable Distributions. | Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos |
| 2025 | COLT | Robustly Learning Monotone Generalized Linear Models via Data Augmentation. | Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas |
| 2025 | FOCS | Robust Learning of Multi-index Models via Iterative Subspace Approximation. | Ilias Diakonikolas, Giannis Iakovidis, Daniel M. Kane, Nikos Zarifis |
| 2025 | FOCS | Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models. | Ilias Diakonikolas, Daniel M. Kane |
| 2025 | FOCS | PTF Testing Lower Bounds for Non-Gaussian Component Analysis. | Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Thanasis Pittas |
| 2025 | ICML | On Learning Parallel Pancakes with Mostly Uniform Weights. | Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Jasper C. H. Lee, Thanasis Pittas |
| 2025 | ICML | Batch List-Decodable Linear Regression via Higher Moments. | Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Sihan Liu, Thanasis Pittas |
| 2025 | ICML | On Fine-Grained Distinct Element Estimation. | Ilias Diakonikolas, Daniel Kane, Jasper C. H. Lee, Thanasis Pittas, David P. Woodruff, Samson Zhou |
| 2025 | ICML | Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination. | Ilias Diakonikolas, Giannis Iakovidis, Daniel Kane, Thanasis Pittas |
| 2025 | ICML | Online Linear Classification with Massart Noise. | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2025 | ICML | Statistical Query Hardness of Multiclass Linear Classification with Random Classification Noise. | Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos |
| 2025 | SODA | Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation. | Ilias Diakonikolas, Daniel M. Kane, Jasper C. H. Lee, Thanasis Pittas |
| 2025 | STOC | SoS Certifiability of Subgaussian Distributions and Its Algorithmic Applications. | Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia, Stefan Tiegel |
| 2025 | STOC | SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More. | Ilias Diakonikolas, Samuel B. Hopkins, Ankit Pensia, Stefan Tiegel |
| 2025 | STOC | Entangled Mean Estimation in High Dimensions. | Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Thanasis Pittas |
| 2024 | COLT | Efficiently Learning One-Hidden-Layer ReLU Networks via SchurPolynomials. | Ilias Diakonikolas, Daniel M. Kane |
| 2024 | COLT | Testable Learning of General Halfspaces with Adversarial Label Noise. | Ilias Diakonikolas, Daniel M. Kane, Sihan Liu, Nikos Zarifis |
| 2024 | COLT | Statistical Query Lower Bounds for Learning Truncated Gaussians. | Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis |
| 2024 | FOCS | Agnostically Learning Multi-Index Models with Queries. | Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2024 | FOCS | Sum-of-Squares Lower Bounds for Non-Gaussian Component Analysis. | Ilias Diakonikolas, Sushrut Karmalkar, Shuo Pang, Aaron Potechin |
| 2024 | ICLR | How Does Unlabeled Data Provably Help Out-of-Distribution Detection? | Xuefeng Du, Zhen Fang, Ilias Diakonikolas, Yixuan Li |
| 2024 | ICML | Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. | Ilias Diakonikolas, Daniel Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas |
| 2024 | ICML | Fast Co-Training under Weak Dependence via Stream-Based Active Learning. | Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos |
| 2024 | ICML | Robustly Learning Single-Index Models via Alignment Sharpness. | Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas |
| 2024 | SODA | Online Robust Mean Estimation. | Daniel M. Kane, Ilias Diakonikolas, Hanshen Xiao, Sihan Liu |
| 2024 | STOC | 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 |
| 2024 | STOC | Testing Closeness of Multivariate Distributions via Ramsey Theory. | Ilias Diakonikolas, Daniel M. Kane, Sihan Liu |
| 2023 | COLT | Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise. | Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane, Puqian Wang, Nikos Zarifis |
| 2023 | COLT | Statistical and Computational Limits for Tensor-on-Tensor Association Detection. | Ilias Diakonikolas, Daniel M. Kane, Yuetian Luo, Anru Zhang |
| 2023 | COLT | Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions. | Ilias Diakonikolas, Sushrut Karmalkar, Jongho Park, Christos Tzamos |
| 2023 | COLT | SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians. | Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis |
| 2023 | COLT | Self-Directed Linear Classification. | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2023 | COLT | A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. | Daniel Kane, Ilias Diakonikolas |
| 2023 | ICML | Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. | Ilias Diakonikolas, Daniel Kane, Ankit Pensia, Thanasis Pittas |
| 2023 | ICML | Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. | Ilias Diakonikolas, Daniel Kane, Lisheng Ren |
| 2023 | ICML | Robustly Learning a Single Neuron via Sharpness. | Puqian Wang, Nikos Zarifis, Ilias Diakonikolas, Jelena Diakonikolas |
| 2023 | STOC | A Strongly Polynomial Algorithm for Approximate Forster Transforms and Its Application to Halfspace Learning. | Ilias Diakonikolas, Christos Tzamos, Daniel M. Kane |
| 2022 | AISTATS | Hardness of Learning a Single Neuron with Adversarial Label Noise. | Ilias Diakonikolas, Daniel Kane, Pasin Manurangsi, Lisheng Ren |
| 2022 | COLT | Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. | Ilias Diakonikolas, Daniel Kane |
| 2022 | COLT | Non-Gaussian Component Analysis via Lattice Basis Reduction. | Ilias Diakonikolas, Daniel Kane |
| 2022 | COLT | Robust Sparse Mean Estimation via Sum of Squares. | Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas |
| 2022 | COLT | Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. | Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun |
| 2022 | COLT | Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2022 | ICML | Streaming Algorithms for High-Dimensional Robust Statistics. | Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia, Thanasis Pittas |
| 2022 | ICML | Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent. | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2022 | STOC | Robustly learning mixtures of | Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel M. Kane, Pravesh K. Kothari, Santosh S. Vempala |
| 2022 | STOC | Clustering mixture models in almost-linear time via list-decodable mean estimation. | Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian |
| 2022 | STOC | Learning general halfspaces with general Massart noise under the Gaussian distribution. | Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2021 | COLT | Boosting in the Presence of Massart Noise. | Ilias Diakonikolas, Russell Impagliazzo, Daniel M. Kane, Rex Lei, Jessica Sorrell, Christos Tzamos |
| 2021 | COLT | The Sample Complexity of Robust Covariance Testing. | Ilias Diakonikolas, Daniel M. Kane |
| 2021 | COLT | Agnostic Proper Learning of Halfspaces under Gaussian Marginals. | Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2021 | COLT | The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals in the SQ Model. | Ilias Diakonikolas, Daniel M. Kane, Thanasis Pittas, Nikos Zarifis |
| 2021 | COLT | Outlier-Robust Learning of Ising Models Under Dobrushin's Condition. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart, Yuxin Sun |
| 2021 | ICDE | Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. | Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld |
| 2021 | ICML | Learning Online Algorithms with Distributional Advice. | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Ali Vakilian, Nikos Zarifis |
| 2021 | STOC | Optimal testing of discrete distributions with high probability. | Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, John Peebles, Eric Price |
| 2021 | STOC | Efficiently learning halfspaces with Tsybakov noise. | Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2020 | COLT | Approximation Schemes for ReLU Regression. | Ilias Diakonikolas, Surbhi Goel, Sushrut Karmalkar, Adam R. Klivans, Mahdi Soltanolkotabi |
| 2020 | COLT | Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks. | Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Nikos Zarifis |
| 2020 | COLT | Learning Halfspaces with Massart Noise Under Structured Distributions. | Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis |
| 2020 | FOCS | Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. | Ainesh Bakshi, Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, Sushrut Karmalkar, Pravesh K. Kothari |
| 2020 | FOCS | Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. | Ilias Diakonikolas, Daniel M. Kane |
| 2020 | ICML | High-dimensional Robust Mean Estimation via Gradient Descent. | Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi |
| 2020 | ICML | Efficiently Learning Adversarially Robust Halfspaces with Noise. | Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro |
| 2019 | AAAI | On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games. | Ilias Diakonikolas, Chrystalla Pavlou |
| 2019 | COLT | Faster Algorithms for High-Dimensional Robust Covariance Estimation. | Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff |
| 2019 | COLT | Communication and Memory Efficient Testing of Discrete Distributions. | Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao |
| 2019 | COLT | Testing Identity of Multidimensional Histograms. | Ilias Diakonikolas, Daniel M. Kane, John Peebles |
| 2019 | ICML | Sever: A Robust Meta-Algorithm for Stochastic Optimization. | Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart |
| 2019 | SODA | High-Dimensional Robust Mean Estimation in Nearly-Linear Time. | Yu Cheng, Ilias Diakonikolas, Rong Ge |
| 2019 | SODA | Efficient Algorithms and Lower Bounds for Robust Linear Regression. | Ilias Diakonikolas, Weihao Kong, Alistair Stewart |
| 2019 | STOC | Degree-푑 chow parameters robustly determine degree-푑 PTFs (and algorithmic applications). | Ilias Diakonikolas, Daniel M. Kane |
| 2018 | COLT | Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities. | Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart |
| 2018 | COLT | Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. | Ilias Diakonikolas, Jerry Li, Ludwig Schmidt |
| 2018 | ICALP | Sample-Optimal Identity Testing with High Probability. | Ilias Diakonikolas, Themis Gouleakis, John Peebles, Eric Price |
| 2018 | ICML | Differentially Private Identity and Equivalence Testing of Discrete Distributions. | Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld |
| 2018 | ITA | Testing Conditional Independence of Discrete Distributions. | Clment L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2018 | SODA | Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2018 | STOC | Testing conditional independence of discrete distributions. | Clment L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2018 | STOC | List-decodable robust mean estimation and learning mixtures of spherical gaussians. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2018 | STOC | Learning geometric concepts with nasty noise. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2017 | COLT | Testing Bayesian Networks. | Clment L. Canonne, Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2017 | COLT | Learning Multivariate Log-concave Distributions. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2017 | FOCS | Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2017 | ICALP | Near-Optimal Closeness Testing of Discrete Histogram Distributions. | Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin |
| 2017 | ICML | Being Robust (in High Dimensions) Can Be Practical. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2017 | SODA | Sample-Optimal Density Estimation in Nearly-Linear Time. | Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt |
| 2017 | SODA | Playing Anonymous Games using Simple Strategies. | Yu Cheng, Ilias Diakonikolas, Alistair Stewart |
| 2016 | COLT | Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2016 | COLT | Properly Learning Poisson Binomial Distributions in Almost Polynomial Time. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2016 | FOCS | A New Approach for Testing Properties of Discrete Distributions. | Ilias Diakonikolas, Daniel M. Kane |
| 2016 | FOCS | Robust Estimators in High Dimensions without the Computational Intractability. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2016 | ICML | Fast Algorithms for Segmented Regression. | Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt |
| 2016 | STOC | The fourier transform of poisson multinomial distributions and its algorithmic applications. | Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart |
| 2016 | STACS | Testing Shape Restrictions of Discrete Distributions. | Clment L. Canonne, Ilias Diakonikolas, Themis Gouleakis, Ronitt Rubinfeld |
| 2015 | FOCS | On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms. | Xi Chen, Ilias Diakonikolas, Anthi Orfanou, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis |
| 2015 | FOCS | Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. | Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin |
| 2015 | PODS | Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. | Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Zheng Li, Ludwig Schmidt |
| 2015 | SODA | Learning from satisfying assignments. | Anindya De, Ilias Diakonikolas, Rocco A. Servedio |
| 2015 | SODA | Testing Identity of Structured Distributions. | Ilias Diakonikolas, Daniel M. Kane, Vladimir Nikishkin |
| 2014 | SODA | Optimal Algorithms for Testing Closeness of Discrete Distributions. | Siu On Chan, Ilias Diakonikolas, Paul Valiant, Gregory Valiant |
| 2014 | SODA | The Complexity of Optimal Multidimensional Pricing. | Xi Chen, Ilias Diakonikolas, Dimitris Paparas, Xiaorui Sun, Mihalis Yannakakis |
| 2014 | SODA | A Polynomial-time Approximation Scheme for Fault-tolerant Distributed Storage. | Constantinos Daskalakis, Anindya De, Ilias Diakonikolas, Ankur Moitra, Rocco A. Servedio |
| 2014 | STOC | Efficient density estimation via piecewise polynomial approximation. | Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun |
| 2013 | FOCS | Learning Sums of Independent Integer Random Variables. | Constantinos Daskalakis, Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan |
| 2013 | ICALP | A Robust Khintchine Inequality, and Algorithms for Computing Optimal Constants in Fourier Analysis and High-Dimensional Geometry. | Anindya De, Ilias Diakonikolas, Rocco A. Servedio |
| 2013 | SODA | Learning mixtures of structured distributions over discrete domains. | Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun |
| 2013 | SODA | Testing | Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio, Gregory Valiant, Paul Valiant |
| 2012 | ICALP | The Inverse Shapley Value Problem. | Anindya De, Ilias Diakonikolas, Rocco A. Servedio |
| 2012 | ICALP | Efficiency-Revenue Trade-Offs in Auctions. | Ilias Diakonikolas, Christos H. Papadimitriou, George Pierrakos, Yaron Singer |
| 2012 | SODA | Learning | Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio |
| 2012 | STOC | Learning poisson binomial distributions. | Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio |
| 2012 | STOC | Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces. | Anindya De, Ilias Diakonikolas, Vitaly Feldman, Rocco A. Servedio |
| 2011 | ALENEX | Disjoint-Path Facility Location: Theory and Practice. | Lee Breslau, Ilias Diakonikolas, Nick G. Duffield, Yu Gu, Mohammad Taghi Hajiaghayi, David S. Johnson, Howard J. Karloff, Mauricio G. C. Resende, Subhabrata Sen |
| 2011 | DAC | Supervised design space exploration by compositional approximation of Pareto sets. | Hung-Yi Liu, Ilias Diakonikolas, Michele Petracca, Luca P. Carloni |
| 2011 | SODA | Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions. | Ilias Diakonikolas, Ryan O'Donnell, Rocco A. Servedio, Yi Wu |
| 2010 | FOCS | Bounded Independence Fools Degree-2 Threshold Functions. | Ilias Diakonikolas, Daniel M. Kane, Jelani Nelson |
| 2010 | SODA | How Good is the Chord Algorithm?. | Constantinos Daskalakis, Ilias Diakonikolas, Mihalis Yannakakis |
| 2010 | STOC | 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 |
| 2009 | FOCS | Bounded Independence Fools Halfspaces. | Ilias Diakonikolas, Parikshit Gopalan, Ragesh Jaiswal, Rocco A. Servedio, Emanuele Viola |
| 2008 | ICALP | Efficiently Testing Sparse GF(2) Polynomials. | Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Rocco A. Servedio, Andrew Wan |
| 2008 | SODA | Succinct approximate convex pareto curves. | Ilias Diakonikolas, Mihalis Yannakakis |
| 2007 | FOCS | Testing for Concise Representations. | Ilias Diakonikolas, Homin K. Lee, Kevin Matulef, Krzysztof Onak, Ronitt Rubinfeld, Rocco A. Servedio, Andrew Wan |