Ilias Diakonikolas

发表

Being Robust (in High Dimensions) Can Be Practical pdf

Jerry Li, Daniel M. Kane, Ankur Moitra, 2017, ICML.

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently pdf

Jerry Li, Daniel M. Kane, Ankur Moitra, 2017, SODA.

List-decodable robust mean estimation and learning mixtures of spherical gaussians

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2017, STOC.

Recent Advances in Algorithmic High-Dimensional Robust Statistics pdf

Daniel M. Kane, Ilias Diakonikolas, Ilias Diakonikolas, 2019, ArXiv.

Robust Estimators in High Dimensions without the Computational Intractability

Daniel M. Kane, Ankur Moitra, Ilias Diakonikolas, 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

High-Dimensional Robust Mean Estimation in Nearly-Linear Time pdf

Yu Cheng, Ilias Diakonikolas, Rong Ge, 2018, SODA.

Efficient Algorithms and Lower Bounds for Robust Linear Regression

Ilias Diakonikolas, Alistair Stewart, Weihao Kong, 2018, SODA.

Nearly Optimal Solutions for the Chow Parameters Problem and Low-Weight Approximation of Halfspaces

Rocco A. Servedio, Anindya De, Vitaly Feldman, 2012, J. ACM.

Statistical Query Lower Bounds for Robust Estimation of High-Dimensional Gaussians and Gaussian Mixtures

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2016, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).

Learning geometric concepts with nasty noise

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2017, STOC.

Robust Estimators in High Dimensions without the Computational Intractability

Daniel M. Kane, Ankur Moitra, Ilias Diakonikolas, 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

Faster Algorithms for High-Dimensional Robust Covariance Estimation pdf

David P. Woodruff, Ilias Diakonikolas, Rong Ge, 2019, COLT.

Differentially Private Learning of Structured Discrete Distributions

Ilias Diakonikolas, Ludwig Schmidt, Moritz Hardt, 2015, NIPS.

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms

Chinmay Hegde, Ilias Diakonikolas, Ludwig Schmidt, 2015, PODS.

Testing for Concise Representations

Ilias Diakonikolas, R. Servedio, R. Rubinfeld, 2007, FOCS 2007.

Learning from satisfying assignments

Rocco A. Servedio, Anindya De, Ilias Diakonikolas, 2015, SODA.

The Complexity of Optimal Multidimensional Pricing

Xi Chen, Mihalis Yannakakis, Ilias Diakonikolas, 2013, SODA.

A Regularity Lemma and Low-Weight Approximators for Low-Degree Polynomial Threshold Functions

Rocco A. Servedio, Li-Yang Tan, Ilias Diakonikolas, 2014, Theory Comput..

N ov 2 01 8 Degreed Chow Parameters Robustly Determine Degreed PTFs ( and Algorithmic Applications )

Ilias Diakonikolas, D. Kane, 2018 .

Streaming Algorithms for High-Dimensional Robust Statistics

Ilias Diakonikolas, D. Kane, Ankit Pensia, 2022, 2204.12399.

Robustly learning mixtures of k arbitrary Gaussians

S. Vempala, Ilias Diakonikolas, D. Kane, 2020, STOC.

Communication-Efficient Distributed Learning of Discrete Probability Distributions

Ilias Diakonikolas, 2017 .

Complexity of Efficiency-Revenue Trade-offs in Bayesian Auctions

C. Papadimitriou, Ilias Diakonikolas, Yaron Singer, 2011 .

Sever: A Robust Meta-Algorithm for Stochastic Optimization

Jerry Li, Daniel M. Kane, Ilias Diakonikolas, 2018, ICML.

Table of Contents

Ilias Diakonikolas, D. Kane, Shai Evra, 2020, JAAD Case Reports.

Efficient Robust Proper Learning of Log-concave Distributions pdf

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2016, ArXiv.

Succinct Approximate Convex Pareto Curves ( Extended Abstract )

M. Yannakakis, Ilias Diakonikolas, 2022 .

Small Approximate Pareto Sets for Bi-objective Shortest Paths and Other Problems pdf

Mihalis Yannakakis, Ilias Diakonikolas, M. Yannakakis, 2007, APPROX-RANDOM.

D S ] 2 1 Ja n 20 16 Testing Shape Restrictions of Discrete Distributions

Ilias Diakonikolas, R. Rubinfeld, C. Canonne, 2018 .

25 : 2 Testing Shape Restrictions of Discrete Distributions restricted inference

Ilias Diakonikolas, R. Rubinfeld, C. Canonne, 2016 .

Threshold Phenomena in Learning Halfspaces with Massart Noise pdf

Daniel M. Kane, Christos Tzamos, Ilias Diakonikolas, 2021, ArXiv.

Proceedings of the nineteenth annual ACM-SIAM Symposium on Discrete Algorithms (SODA '08)

Mihalis Yannakakis, Ilias Diakonikolas, M. Yannakakis, 2008 .

Efficient Approximation Algorithms for the Inverse Semivalue Problem

Ilias Diakonikolas, Alistair Stewart, Chrystalla Pavlou, 2022, AAMAS.

Robust Sparse Mean Estimation via Sum of Squares

Ilias Diakonikolas, D. Kane, Ankit Pensia, 2022, COLT.

Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models

Ilias Diakonikolas, D. Kane, Yuxin Sun, 2022, COLT.

List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering

Ilias Diakonikolas, D. Kane, Ankit Pensia, 2022, ArXiv.

Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces

V. Feldman, Ilias Diakonikolas, R. Servedio, 2012, STOC '12.

Robust High-Dimensional Statistics

Daniel M. Kane, Ilias Diakonikolas, Ilias Diakonikolas, 2020, Beyond the Worst-Case Analysis of Algorithms.

Playing Anonymous Games using Simple Strategies

Ilias Diakonikolas, Alistair Stewart, Yu Cheng, 2016, SODA.

Cryptographic Hardness of Learning Halfspaces with Massart Noise

Ilias Diakonikolas, D. Kane, Pasin Manurangsi, 2022, NeurIPS.

Hardness of Learning a Single Neuron with Adversarial Label Noise

Ilias Diakonikolas, D. Kane, Pasin Manurangsi, 2022, AISTATS.

Outlier-Robust Sparse Estimation via Non-Convex Optimization

Rong Ge, M. Soltanolkotabi, Ilias Diakonikolas, 2021, NeurIPS.

A Strongly Polynomial Algorithm for Approximate Forster Transforms and Its Application to Halfspace Learning

Ilias Diakonikolas, D. Kane, Christos Tzamos, 2022, Electron. Colloquium Comput. Complex..

Learning General Halfspaces with Adversarial Label Noise via Online Gradient Descent

Ilias Diakonikolas, Christos Tzamos, Vasilis Kontonis, 2022, ICML.

Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions

Jasper C. H. Lee, Ilias Diakonikolas, D. Kane, 2022, NeurIPS.

Near-Optimal Bounds for Testing Histogram Distributions

Ilias Diakonikolas, D. Kane, C. Canonne, 2022, ArXiv.

Edinburgh Learning k-Modal Distributions via Testing

Ilias Diakonikolas, R. Servedio, 2017 .

SQ Lower Bounds for Learning Single Neurons with Massart Noise

Ilias Diakonikolas, D. Kane, Yuxin Sun, 2022, NeurIPS.

Learning a Single Neuron with Adversarial Label Noise via Gradient Descent

Ilias Diakonikolas, Christos Tzamos, Vasilis Kontonis, 2022, COLT.

Non-Gaussian Component Analysis via Lattice Basis Reduction pdf

Daniel M. Kane, Ilias Diakonikolas, Ilias Diakonikolas, 2021, COLT.

Advances in Neural Information Processing Systems 28

Ilias Diakonikolas, Moritz Hardt, Ludwig Schmidt, 2014 .

A Regularity Lemma, and Low-Weight Approximators, for Low-Degree Polynomial Threshold Functions

Rocco A. Servedio, Li-Yang Tan, Ilias Diakonikolas, 2009, 2010 IEEE 25th Annual Conference on Computational Complexity.

Deterministic Approximate Counting for Juntas of Degree-2 Polynomial Threshold Functions

Rocco A. Servedio, Anindya De, Ilias Diakonikolas, 2013, 2014 IEEE 29th Conference on Computational Complexity (CCC).

A Polynomial-time Approximation Scheme for Fault-tolerant Distributed Storage pdf

Rocco A. Servedio, Anindya De, Ankur Moitra, 2013, SODA.

Deterministic Approximate Counting for Degree-2 Polynomial Threshold Functions pdf

Rocco A. Servedio, Anindya De, Ilias Diakonikolas, 2013, Electron. Colloquium Comput. Complex..

Inverse Problems in Approximate Uniform Generation

Rocco A. Servedio, Anindya De, Ilias Diakonikolas, 2012, Electron. Colloquium Comput. Complex..

The Inverse Shapley value problem

Rocco A. Servedio, Anindya De, Ilias Diakonikolas, 2012, Games Econ. Behav..

Improved Approximation of Linear Threshold Functions

Rocco A. Servedio, Ilias Diakonikolas, Ilias Diakonikolas, 2009, 2009 24th Annual IEEE Conference on Computational Complexity.

Bounded Independence Fools Halfspaces

Rocco A. Servedio, Emanuele Viola, Ilias Diakonikolas, 2009, 2009 50th Annual IEEE Symposium on Foundations of Computer Science.

Optimal testing of discrete distributions with high probability pdf

Daniel M. Kane, Ilias Diakonikolas, John Peebles, 2020, Electron. Colloquium Comput. Complex..

Degree-𝑑 chow parameters robustly determine degree-𝑑 PTFs (and algorithmic applications)

Ilias Diakonikolas, Daniel Kane, Ilias Diakonikolas, 2018, Electron. Colloquium Comput. Complex..

Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces

V. Feldman, Ilias Diakonikolas, R. Servedio, 2012, STOC '12.

Sample-Optimal Identity Testing with High Probability

Ilias Diakonikolas, John Peebles, Themis Gouleakis, 2017, Electron. Colloquium Comput. Complex..

Testing k-Modal Distributions: Optimal Algorithms via Reductions pdf

Rocco A. Servedio, Ilias Diakonikolas, Constantinos Daskalakis, 2011, SODA.

A Robust Khintchine Inequality, and Algorithms for Computing Optimal Constants in Fourier Analysis and High-Dimensional Geometry pdf

Rocco A. Servedio, Anindya De, Ilias Diakonikolas, 2012, SIAM J. Discret. Math..

Noise Stable Halfspaces are Close to Very Small Juntas

Rocco A. Servedio, Li-Yang Tan, Ilias Diakonikolas, 2015, Chic. J. Theor. Comput. Sci..

On the Distribution of the Fourier Spectrum of Halfspaces pdf

Rocco A. Servedio, Li-Yang Tan, Ilias Diakonikolas, 2012, ArXiv.

Average Sensitivity and Noise Sensitivity of Polynomial Threshold Functions

Prasad Raghavendra, Rocco A. Servedio, Li-Yang Tan, 2009, SIAM J. Comput..

List-Decodable Mean Estimation via Iterative Multi-Filtering pdf

Daniel M. Kane, Ilias Diakonikolas, Daniel Kongsgaard, 2020, NeurIPS.

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise pdf

Daniel M. Kane, Ilias Diakonikolas, Pasin Manurangsi, 2020, NeurIPS.

Sample-Optimal Density Estimation in Nearly-Linear Time

Ilias Diakonikolas, Ludwig Schmidt, Jayadev Acharya, 2015, SODA.

Fast Algorithms for Segmented Regression

Jerry Li, Ilias Diakonikolas, Ludwig Schmidt, 2016, ICML.

Learning Structured Distributions

Ilias Diakonikolas, Ilias Diakonikolas, 2016, Handbook of Big Data.

Efficient Algorithms for Multidimensional Segmented Regression pdf

Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov, 2020, ArXiv.

Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees

Ronitt Rubinfeld, Aditya G. Parameswaran, Ilias Diakonikolas, 2020, 2021 IEEE 37th International Conference on Data Engineering (ICDE).

Testing Shape Restrictions of Discrete Distributions

Ronitt Rubinfeld, Ilias Diakonikolas, Clément L. Canonne, 2015, Theory of Computing Systems.

How good is the Chord algorithm?

Mihalis Yannakakis, Ilias Diakonikolas, Constantinos Daskalakis, 2010, SODA '10.

Small Approximate Pareto Sets for Biobjective Shortest Paths and Other Problems

Mihalis Yannakakis, Ilias Diakonikolas, M. Yannakakis, 2009, SIAM J. Comput..

Succinct approximate convex pareto curves

Mihalis Yannakakis, Ilias Diakonikolas, M. Yannakakis, 2008, SODA '08.

Streaming Algorithms for High-Dimensional Robust Statistics

Ilias Diakonikolas, D. Kane, Ankit Pensia, 2022, ICML.

Communication and Memory Efficient Testing of Discrete Distributions

Daniel M. Kane, Ilias Diakonikolas, Themis Gouleakis, 2019, COLT.

Efficiently Learning Adversarially Robust Halfspaces with Noise

Ilias Diakonikolas, Nathan Srebro, Omar Montasser, 2020, ICML.

Efficiency-Revenue Trade-Offs in Auctions pdf

Christos H. Papadimitriou, George Pierrakos, Ilias Diakonikolas, 2012, ICALP.

Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs

Yu Gu, Mauricio G. C. Resende, David S. Johnson, 2016, Oper. Res..

Disjoint-Path Facility Location: Theory and Practice

Yu Gu, Mauricio G. C. Resende, David S. Johnson, 2011, ALENEX.

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering

Ilias Diakonikolas, Alistair Stewart, Eric Price, 2019, NeurIPS.

A New Approach for Testing Properties of Discrete Distributions

Daniel M. Kane, Ilias Diakonikolas, Ilias Diakonikolas, 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

Clustering mixture models in almost-linear time via list-decodable mean estimation pdf

Daniel M. Kane, Ilias Diakonikolas, Kevin Tian, 2021, STOC.

Robustly Learning any Clusterable Mixture of Gaussians pdf

Ilias Diakonikolas, Samuel B. Hopkins, Daniel Kane, 2020, ArXiv.

Robustness meets algorithms

Daniel M. Kane, Ankur Moitra, Ilias Diakonikolas, 2021, Commun. ACM.

Learning Poisson Binomial Distributions

Rocco A. Servedio, Ilias Diakonikolas, Constantinos Daskalakis, 2011, STOC '12.

Learning Poisson Binomial Distributions (Extended Abstract)

Rocco A. Servedio, Ilias Diakonikolas, Constantinos Daskalakis, 2012 .

Supervised design space exploration by compositional approximation of Pareto sets

Luca P. Carloni, Hung-Yi Liu, Ilias Diakonikolas, 2011, 2011 48th ACM/EDAC/IEEE Design Automation Conference (DAC).

Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures

Ilias Diakonikolas, Samuel B. Hopkins, Ainesh Bakshi, 2020, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games

Ilias Diakonikolas, Chrystalla Pavlou, Ilias Diakonikolas, 2018, AAAI.

Approximation Schemes for ReLU Regression

Adam R. Klivans, M. Soltanolkotabi, Ilias Diakonikolas, 2020, COLT.

The fourier transform of poisson multinomial distributions and its algorithmic applications

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2015, STOC.

Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models

Daniel M. Kane, Ilias Diakonikolas, Ilias Diakonikolas, 2020, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS).

Explorer Efficient Density Estimation via Piecewise Polynomial Approximation

Rocco A. Servedio, Ilias Diakonikolas, Siu-on Chan, 2013 .

Testing Bayesian Networks

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2016, IEEE Transactions on Information Theory.

Differentially Private Identity and Equivalence Testing of Discrete Distributions

Ronitt Rubinfeld, Ilias Diakonikolas, Maryam Aliakbarpour, 2018, ICML.

Equipping Experts/Bandits with Long-term Memory

Haipeng Luo, Kai Zheng, Ilias Diakonikolas, 2019, NeurIPS.

Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

Daniel M. Kane, Ilias Diakonikolas, Vasilis Kontonis, 2020, COLT.

Optimal Algorithms for Testing Closeness of Discrete Distributions pdf

Ilias Diakonikolas, Paul Valiant, Siu-on Chan, 2013, SODA.

Forster Decomposition and Learning Halfspaces with Noise pdf

Daniel M. Kane, Christos Tzamos, Ilias Diakonikolas, 2021, NeurIPS.

Boosting in the Presence of Massart Noise pdf

Christos Tzamos, Russell Impagliazzo, Ilias Diakonikolas, 2021, COLT.

Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise

Ilias Diakonikolas, D. Kane, 2020, COLT.

Hardness of Learning Halfspaces with Massart Noise pdf

Daniel M. Kane, Ilias Diakonikolas, Ilias Diakonikolas, 2020, ArXiv.

Distribution-Independent PAC Learning of Halfspaces with Massart Noise

Christos Tzamos, Ilias Diakonikolas, Themis Gouleakis, 2019, NeurIPS.

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families

Ilias Diakonikolas, Alistair Stewart, Gregory Valiant, 2019, NeurIPS.

Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities pdf

Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, 2018, COLT.

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals

Daniel M. Kane, Ilias Diakonikolas, Nikos Zarifis, 2021, COLT.

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise pdf

Daniel M. Kane, Christos Tzamos, Ilias Diakonikolas, 2020, ArXiv.

Learning Halfspaces with Tsybakov Noise pdf

Christos Tzamos, Ilias Diakonikolas, Vasilis Kontonis, 2020, ArXiv.

Learning Halfspaces with Massart Noise Under Structured Distributions pdf

Christos Tzamos, Ilias Diakonikolas, Vasilis Kontonis, 2020, COLT 2020.

Efficiently learning halfspaces with Tsybakov noise

Daniel M. Kane, Christos Tzamos, Ilias Diakonikolas, 2021, STOC.

Outlier Robust Mean Estimation with Subgaussian Rates via Stability pdf

Ankit Pensia, Daniel M. Kane, Ilias Diakonikolas, 2020, NeurIPS.

Differentially Private Identity and Closeness Testing of Discrete Distributions pdf

Ronitt Rubinfeld, Ilias Diakonikolas, Maryam Aliakbarpour, 2017, ArXiv.

Testing for Concise Representations

Ilias Diakonikolas, R. Servedio, R. Rubinfeld, 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

Robust Learning of Fixed-Structure Bayesian Networks

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2016, NeurIPS.

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin

Daniel M. Kane, Ilias Diakonikolas, Pasin Manurangsi, 2019, NeurIPS.

Testing Identity of Structured Distributions

Daniel M. Kane, Ilias Diakonikolas, Vladimir Nikishkin, 2014, SODA.

Self-Directed Linear Classification

Ilias Diakonikolas, Christos Tzamos, Vasilis Kontonis, 2023, COLT.

Agnostic Proper Learning of Halfspaces under Gaussian Marginals

Daniel M. Kane, Christos Tzamos, Ilias Diakonikolas, 2021, COLT.

Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise

Ilias Diakonikolas, D. Kane, Jelena Diakonikolas, 2023, arXiv.org.

Testing Identity of Multidimensional Histograms pdf

Daniel M. Kane, Ilias Diakonikolas, John Peebles, 2018, COLT.

Learning general halfspaces with general Massart noise under the Gaussian distribution

Daniel M. Kane, Christos Tzamos, Ilias Diakonikolas, 2021, STOC.

A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points

Ilias Diakonikolas, D. Kane, 2022, COLT.

Learning k-Modal Distributions via Testing pdf

Rocco A. Servedio, Ilias Diakonikolas, Constantinos Daskalakis, 2012, Theory Comput..

Nearly-Tight Bounds for Testing Histogram Distributions

Ilias Diakonikolas, C. Canonne, Daniel Kane, 2022, NeurIPS.

Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms

Rocco A. Servedio, Ilias Diakonikolas, Siu-on Chan, 2014, NIPS.

Efficient density estimation via piecewise polynomial approximation

Ilias Diakonikolas, R. Servedio, Xiaorui Sun, 2013, STOC.

Learning mixtures of structured distributions over discrete domains pdf

Rocco A. Servedio, Ilias Diakonikolas, Siu-on Chan, 2012, SODA.

Statistical Query Lower Bounds for List-Decodable Linear Regression pdf

Ankit Pensia, Daniel M. Kane, Ilias Diakonikolas, 2021, NeurIPS.

A Polynomial Time Algorithm for Maximum Likelihood Estimation of Multivariate Log-concave Densities pdf

Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, 2018, ArXiv.

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms pdf

Jerry Li, Ilias Diakonikolas, Ludwig Schmidt, 2018, COLT.

Near-Optimal Closeness Testing of Discrete Histogram Distributions

Daniel M. Kane, Ilias Diakonikolas, Vladimir Nikishkin, 2017, ICALP.

Learning Multivariate Log-concave Distributions

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2016, COLT.

Properly Learning Poisson Binomial Distributions in Almost Polynomial Time

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2015, COLT.

Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions

Daniel M. Kane, Ilias Diakonikolas, Vladimir Nikishkin, 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2015, COLT.

Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables pdf

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2015, ArXiv.

Lecture 10 : Overview of Current Research in Distribution Learning / Testing

Ilias Diakonikolas, Scribes Yuxin Sun, 2019 .

Learning Sums of Independent Integer Random Variables

Ryan O'Donnell, Rocco A. Servedio, Li-Yang Tan, 2013, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science.

SQ Lower Bounds for Learning Bounded Covariance GMMs

Ilias Diakonikolas, D. Kane, Thanasis Pittas, 2023, ArXiv.

Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA

Ilias Diakonikolas, D. Kane, Ankit Pensia, 2023, ICML.

A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm

Jasper C. H. Lee, Ilias Diakonikolas, D. Kane, 2023, ArXiv.

Outlier-Robust Learning of Ising Models Under Dobrushin's Condition

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2021, COLT.

SQ Lower Bounds for Learning Mixtures of Separated and Bounded Covariance Gaussians

L. Rosasco, Gergely Neu, Ilias Diakonikolas, 2023, COLT.

High-Dimensional Robust Mean Estimation via Gradient Descent pdf

Yu Cheng, Ilias Diakonikolas, Mahdi Soltanolkotabi, 2020, ICML.

ReLU Regression with Massart Noise pdf

Christos Tzamos, Ilias Diakonikolas, Jongho Park, 2021, NeurIPS.

Private Testing of Distributions via Sample Permutations

Ronitt Rubinfeld, Daniel M. Kane, Ilias Diakonikolas, 2019, NeurIPS.

Distribution-Independent Regression for Generalized Linear Models with Oblivious Corruptions

Ilias Diakonikolas, Christos Tzamos, Sushrut Karmalkar, 2023, COLT.

Fourier-Based Testing for Families of Distributions pdf

Ilias Diakonikolas, Alistair Stewart, Clément L. Canonne, 2017, Electron. Colloquium Comput. Complex..

Testing for Families of Distributions via the Fourier Transform

Ilias Diakonikolas, Alistair Stewart, Clément L. Canonne, 2018, NeurIPS.

Proceedings of the Twenty-third Annual ACM-SIAM Symposium on Discrete Algorithms

Rocco A. Servedio, Ilias Diakonikolas, Constantinos Daskalakis, 2012 .

Bounding the average sensitivity and noise sensitivity of polynomial threshold functions

Adam R. Klivans, Ilias Diakonikolas, P. Harsha, 2010, STOC '10.

Testing Conditional Independence of Discrete Distributions

Daniel M. Kane, Ilias Diakonikolas, Alistair Stewart, 2017, 2018 Information Theory and Applications Workshop (ITA).

SQ Lower Bounds for Learning Mixtures of Linear Classifiers

Ilias Diakonikolas, D. Kane, Yuxin Sun, 2023, NeurIPS.

Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials

Ilias Diakonikolas, D. Kane, 2023, ArXiv.

Edinburgh Research Explorer a New Approach for Testing Properties of Discrete Distributions a New Approach for Testing Properties of Discrete Distributions

Ilias Diakonikolas, D. Kane, 2022 .

The complexity of optimal multidimensional pricing for a unit-demand buyer

Xi Chen, Mihalis Yannakakis, Ilias Diakonikolas, 2018, Games Econ. Behav..

On the Complexity of Optimal Lottery Pricing and Randomized Mechanisms

M. Yannakakis, X. Chen, Ilias Diakonikolas, 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

Learning transformed product distributions pdf

Rocco A. Servedio, Ilias Diakonikolas, Constantinos Daskalakis, 2011, ArXiv.

Learning Online Algorithms with Distributional Advice

Christos Tzamos, Ilias Diakonikolas, Vasilis Kontonis, 2021, ICML.