Dynamic Ham-Sandwich Cuts of Convex Polygons in the Plane. - Timothy G. Abbott; Erik D. Demaine; Martin L. Demaine; Daniel Kane 001; Stefan Langerman; Jelani Nelson; Vincent Yeung (CCCG, 2005)

On the Complexity of Two-PlayerWin-Lose Games. - Timothy G. Abbott; Daniel Kane 001; Paul Valiant (FOCS, 2005)

On the Sn-Modules Generated by Partitions of a Given Shape. - Daniel Kane 001; Steven Sivek (Electron. J. Comb., 2008)

Revisiting Norm Estimation in Data Streams - Daniel M. Kane; Jelani Nelson; David P. Woodruff (CoRR, 2008)

The geometry of binary search trees. - Erik D. Demaine; Dion Harmon; John Iacono; Daniel Kane 001; Mihai Patrascu (SODA, 2009)

A Pseudopolynomial Algorithm for Alexandrov's Theorem. - Daniel Kane 001; Gregory N. Price; Erik D. Demaine (WADS, 2009)

Dynamic ham-sandwich cuts in the plane. - Timothy G. Abbott; Michael A. Burr; Timothy M. Chan; Erik D. Demaine; Martin L. Demaine; John Hugg; Daniel Kane 001; Stefan Langerman; Jelani Nelson; Eynat Rafalin; Kathryn Seyboth; Vincent Yeung (Comput. Geom., 2009)

The Gaussian Surface Area and Noise Sensitivity of Degree-$d$ Polynomials - Daniel M. Kane (CoRR, 2009)

Bounded Independence Fools Degree-2 Threshold Functions. - Ilias Diakonikolas; Daniel M. Kane; Jelani Nelson (Electron. Colloquium Comput. Complex., 2009)

The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions. - Daniel M. Kane (CCC, 2010)

An optimal algorithm for the distinct elements problem. - Daniel M. Kane; Jelani Nelson; David P. Woodruff (PODS, 2010)

On the Exact Space Complexity of Sketching and Streaming Small Norms. - Daniel M. Kane; Jelani Nelson; David P. Woodruff (SODA, 2010)

Unary Subset-Sum is in Logspace - Daniel M. Kane (CoRR, 2010)

A Sparser Johnson-Lindenstrauss Transform - Daniel M. Kane; Jelani Nelson (CoRR, 2010)

Almost Optimal Explicit Johnson-Lindenstrauss Transformations. - Daniel Kane 001; Raghu Meka; Jelani Nelson (Electron. Colloquium Comput. Complex., 2010)

A Derandomized Sparse Johnson-Lindenstrauss Transform. - Daniel M. Kane; Jelani Nelson (Electron. Colloquium Comput. Complex., 2010)

On Solving Games Constructed Using Both Short and Long Conjunctive Sums. - Daniel M. Kane (Integers, 2010)

Almost Optimal Explicit Johnson-Lindenstrauss Families. - Daniel Kane 001; Raghu Meka; Jelani Nelson (APPROX-RANDOM, 2011)

k-Independent Gaussians Fool Polynomial Threshold Functions. - Daniel M. Kane (CCC, 2011)

A Small PRG for Polynomial Threshold Functions of Gaussians. - Daniel M. Kane (FOCS, 2011)

Fast moment estimation in data streams in optimal space. - Daniel M. Kane; Jelani Nelson; Ely Porat; David P. Woodruff (STOC, 2011)

The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions. - Daniel M. Kane (Comput. Complex., 2011)

Quantum interpolation of polynomials. - Daniel M. Kane; Samuel A. Kutin (Quantum Inf. Comput., 2011)

Tight Bounds for Testing k-Linearity. - Eric Blais; Daniel M. Kane (APPROX-RANDOM, 2012)

A Structure Theorem for Poorly Anticoncentrated Gaussian Chaoses and Applications to the Study of Polynomial Threshold Functions. - Daniel M. Kane (FOCS, 2012)

Counting Arbitrary Subgraphs in Data Streams. - Daniel M. Kane; Kurt Mehlhorn; Thomas Sauerwald; He Sun 001 (ICALP (2), 2012)

A Structure Theorem for Poorly Poorly Anticoncentrated Gaussian Chaoses and Applications to the Study of Polynomial Threshold Functions. - Daniel M. Kane (CoRR, 2012)

A Low-Depth Monotone Function that is not an Approximate Junta - Daniel M. Kane (CoRR, 2012)

Learning Halfspaces Under Log-Concave Densities: Polynomial Approximations and Moment Matching. - Daniel M. Kane; Adam R. Klivans; Raghu Meka (COLT, 2013)

A PRG for lipschitz functions of polynomials with applications to sparsest cut. - Daniel M. Kane; Raghu Meka (STOC, 2013)

A Short Implicant of CNFs with Relatively Many Satisfying Assignments. - Daniel M. Kane; Osamu Watanabe 001 (Electron. Colloquium Comput. Complex., 2013)

A Monotone Function Given By a Low-Depth Decision Tree That Is Not an Approximate Junta. - Daniel M. Kane (Theory Comput., 2013)

A Pseudorandom Generator for Polynomial Threshold Functions of Gaussian with Subpolynomial Seed Length. - Daniel M. Kane (CCC, 2014)

The average sensitivity of an intersection of half spaces. - Daniel M. Kane (STOC, 2014)

The correct exponent for the Gotsman-Linial Conjecture. - Daniel M. Kane (Comput. Complex., 2014)

Pseudorandomness for concentration bounds and signed majorities. - Parikshit Gopalan; Daniel M. Kane; Raghu Meka (CoRR, 2014)

Sparser Johnson-Lindenstrauss Transforms. - Daniel M. Kane; Jelani Nelson (J. ACM, 2014)

A Polylogarithmic PRG for Degree 2 Threshold Functions in the Gaussian Setting. - Daniel M. Kane (CCC, 2015)

Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions. - Ilias Diakonikolas; Daniel M. Kane; Vladimir Nikishkin (FOCS, 2015)

Testing Identity of Structured Distributions. - Ilias Diakonikolas; Daniel M. Kane; Vladimir Nikishkin (SODA, 2015)

Central limit theorems for some set partition statistics. - Bobbie Chern; Persi Diaconis; Daniel M. Kane; Robert C. Rhoades (Adv. Appl. Math., 2015)

Nearly Optimal Learning and Sparse Covers for Sums of Independent Integer Random Variables. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (CoRR, 2015)

Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits. - Daniel M. Kane; Ryan Williams 001 (Electron. Colloquium Comput. Complex., 2015)

Mass-surveillance without the State: Strongly Undetectable Algorithm-Substitution Attacks. - Mihir Bellare; Joseph Jaeger; Daniel Kane 001 (IACR Cryptol. ePrint Arch., 2015)

3D floorplan representations: Corner links and partial order. - Fang Qiao; Ilgweon Kang; Daniel Kane 001; Fung Yu Young; Chung-Kuan Cheng; Ronald L. Graham (3DIC, 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 (COLT, 2016)

Fourier-Sparse Interpolation without a Frequency Gap. - Xue Chen 001; Daniel M. Kane; Eric Price 001; Zhao Song 002 (FOCS, 2016)

The fourier transform of poisson multinomial distributions and its algorithmic applications. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2016)

A Short Implicant of a CNF Formula with Many Satisfying Assignments. - Daniel Kane 001; Osamu Watanabe 001 (Algorithmica, 2016)

Efficient Robust Proper Learning of Log-concave Distributions. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (CoRR, 2016)

A New Approach for Testing Properties of Discrete Distributions. - Ilias Diakonikolas; Daniel M. Kane (Electron. Colloquium Comput. Complex., 2016)

Statistical Query Lower Bounds for Robust Estimation of High-dimensional Gaussians and Gaussian Mixtures. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (Electron. Colloquium Comput. Complex., 2016)

Big-Key Symmetric Encryption: Resisting Key Exfiltration. - Mihir Bellare; Daniel Kane 001; Phillip Rogaway (IACR Cryptol. ePrint Arch., 2016)

Learning Multivariate Log-concave Distributions. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (COLT, 2017)

Robust Polynomial Regression up to the Information Theoretic Limit. - Daniel Kane 001; Sushrut Karmalkar; Eric Price 001 (FOCS, 2017)

Near-Optimal Closeness Testing of Discrete Histogram Distributions. - Ilias Diakonikolas; Daniel M. Kane; Vladimir Nikishkin (ICALP, 2017)

Being Robust (in High Dimensions) Can Be Practical. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (ICML, 2017)

A Bound on Partitioning Clusters. - Daniel Kane 001; Terence Tao (Electron. J. Comb., 2017)

Sharp Bounds for Generalized Uniformity Testing. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (Electron. Colloquium Comput. Complex., 2017)

A Polynomial Restriction Lemma with Applications. - Valentine Kabanets; Daniel M. Kane; Zhenjian Lu (Electron. Colloquium Comput. Complex., 2017)

Near-optimal linear decision trees for k-SUM and related problems. - Daniel M. Kane; Shachar Lovett; Shay Moran (Electron. Colloquium Comput. Complex., 2017)

On Communication Complexity of Classification Problems. - Daniel M. Kane; Roi Livni; Shay Moran; Amir Yehudayoff (Electron. Colloquium Comput. Complex., 2017)

Active classification with comparison queries. - Daniel M. Kane; Shachar Lovett; Shay Moran; Jiapeng Zhang (Electron. Colloquium Comput. Complex., 2017)

Labeling the complete bipartite graph with no zero cycles. - Daniel M. Kane; Shachar Lovett; Sankeerth Rao (Electron. Colloquium Comput. Complex., 2017)

A PRG for Boolean PTF of Degree 2 with Seed Length Subpolynomial in epsilon and Logarithmic in n. - Daniel Kane 001; Sankeerth Rao (CCC, 2018)

Robust Learning of Fixed-Structure Bayesian Networks. - Yu Cheng 002; Ilias Diakonikolas; Daniel Kane 001; Alistair Stewart (NeurIPS, 2018)

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (SODA, 2018)

Testing conditional independence of discrete distributions. - Clément L. Canonne; Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2018)

List-decodable robust mean estimation and learning mixtures of spherical gaussians. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2018)

Learning geometric concepts with nasty noise. - Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (STOC, 2018)

Quantum Money from Modular Forms. - Daniel M. Kane (CoRR, 2018)

Degree-$d$ Chow Parameters Robustly Determine Degree-$d$ PTFs (and Algorithmic Applications). - Ilias Diakonikolas; Daniel Kane 001 (Electron. Colloquium Comput. Complex., 2018)

Generalized comparison trees for point-location problems. - Daniel M. Kane; Shachar Lovett; Shay Moran (Electron. Colloquium Comput. Complex., 2018)

Pseudorandomness via the Discrete Fourier Transform. - Parikshit Gopalan; Daniel M. Kane; Raghu Meka (SIAM J. Comput., 2018)

The Optimal Approximation Factor in Density Estimation. - Olivier Bousquet; Daniel Kane 001; Shay Moran (COLT, 2019)

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

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

Learning Ising Models with Independent Failures. - Surbhi Goel; Daniel M. Kane; Adam R. Klivans (COLT, 2019)

Sever: A Robust Meta-Algorithm for Stochastic Optimization. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Jacob Steinhardt; Alistair Stewart (ICML, 2019)

The Orthogonal Vectors Conjecture for Branching Programs and Formulas. - Daniel M. Kane; Richard Ryan Williams (ITCS, 2019)

Private Testing of Distributions via Sample Permutations. - Maryam Aliakbarpour; Ilias Diakonikolas; Daniel Kane 001; Ronitt Rubinfeld (NeurIPS, 2019)

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering. - Ilias Diakonikolas; Daniel Kane 001; Sushrut Karmalkar; Eric Price 001; Alistair Stewart (NeurIPS, 2019)

Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi (NeurIPS, 2019)

Degree-푑 chow parameters robustly determine degree-푑 PTFs (and algorithmic applications). - Ilias Diakonikolas; Daniel M. Kane (STOC, 2019)

Waring's Theorem for Binary Powers. - Daniel M. Kane; Carlo Sanna; Jeffrey O. Shallit (Comb., 2019)

Recent Advances in Algorithmic High-Dimensional Robust Statistics. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2019)

Robust Estimators in High-Dimensions Without the Computational Intractability. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Ankur Moitra; Alistair Stewart (SIAM J. Comput., 2019)

The Independence Number of the Birkhoff Polytope Graph, and Applications to Maximally Recoverable Codes. - Daniel Kane 001; Shachar Lovett; Sankeerth Rao (SIAM J. Comput., 2019)

Three-dimensional Floorplan Representations by Using Corner Links and Partial Order. - Ilgweon Kang; Fang Qiao; Dongwon Park; Daniel Kane 001; Evangeline F. Y. Young; Chung-Kuan Cheng; Ronald L. Graham (ACM Trans. Design Autom. Electr. Syst., 2019)

Robust High-Dimensional Statistics. - Ilias Diakonikolas; Daniel M. Kane (Beyond the Worst-Case Analysis of Algorithms, 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)

Noise-tolerant, Reliable Active Classification with Comparison Queries. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Gaurav Mahajan (COLT, 2020)

Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures. - Ainesh Bakshi; Ilias Diakonikolas; Samuel B. Hopkins 001; Daniel Kane 001; Sushrut Karmalkar; Pravesh K. Kothari (FOCS, 2020)

Small Covers for Near-Zero Sets of Polynomials and Learning Latent Variable Models. - Ilias Diakonikolas; Daniel M. Kane (FOCS, 2020)

Point Location and Active Learning: Learning Halfspaces Almost Optimally. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Gaurav Mahajan (FOCS, 2020)

List-Decodable Mean Estimation via Iterative Multi-Filtering. - Ilias Diakonikolas; Daniel Kane 001; Daniel Kongsgaard (NeurIPS, 2020)

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

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

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals. - Ilias Diakonikolas; Daniel Kane 001; Nikos Zarifis (NeurIPS, 2020)

The Power of Comparisons for Actively Learning Linear Classifiers. - Max Hopkins; Daniel Kane 001; Shachar Lovett (NeurIPS, 2020)

Robustly Learning any Clusterable Mixture of Gaussians. - Ilias Diakonikolas; Samuel B. Hopkins 001; Daniel Kane 001; Sushrut Karmalkar (CoRR, 2020)

Prisoners, Rooms, and Lightswitches. - Daniel M. Kane; Scott Duke Kominers (CoRR, 2020)

A Polynomial Time Algorithm for Learning Halfspaces with Tsybakov Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2020)

Robustly Learning Mixtures of k Arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (CoRR, 2020)

Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2020)

Optimal Testing of Discrete Distributions with High Probability. - Ilias Diakonikolas; Themis Gouleakis; Daniel Kane 001; John Peebles; Eric Price 001 (Electron. Colloquium Comput. Complex., 2020)

Testing Bayesian Networks. - Clément L. Canonne; Ilias Diakonikolas; Daniel M. Kane; Alistair Stewart (IEEE Trans. Inf. Theory, 2020)

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

The Sample Complexity of Robust Covariance Testing. - Ilias Diakonikolas; Daniel M. Kane (COLT, 2021)

Agnostic Proper Learning of Halfspaces under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (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)

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

Bounded Memory Active Learning through Enriched Queries. - Max Hopkins; Daniel Kane 001; Shachar Lovett; Michal Moshkovitz (COLT, 2021)

The Entropy of Lies: Playing Twenty Questions with a Liar. - Yuval Dagan; Yuval Filmus; Daniel Kane 001; Shay Moran (ITCS, 2021)

List-Decodable Mean Estimation in Nearly-PCA Time. - Ilias Diakonikolas; Daniel Kane 001; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (NeurIPS, 2021)

Statistical Query Lower Bounds for List-Decodable Linear Regression. - Ilias Diakonikolas; Daniel Kane 001; Ankit Pensia; Thanasis Pittas; Alistair Stewart (NeurIPS, 2021)

Forster Decomposition and Learning Halfspaces with Noise. - Ilias Diakonikolas; Daniel Kane 001; Christos Tzamos (NeurIPS, 2021)

Robust Learning of Mixtures of Gaussians. - Daniel M. Kane (SODA, 2021)

Efficiently learning halfspaces with Tsybakov noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2021)

Robustness meets algorithms. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (Commun. ACM, 2021)

Prisoners, Rooms, and Light Switches. - Daniel M. Kane; Scott Duke Kominers (Electron. J. Comb., 2021)

Highway: Efficient Consensus with Flexible Finality. - Daniel Kane 001; Andreas Fackler; Adam Gagol; Damian Straszak (CoRR, 2021)

The Optimality of Polynomial Regression for Agnostic Learning under Gaussian Marginals. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (CoRR, 2021)

Fooling Gaussian PTFs via Local Hyperconcentration. - Ryan O'Donnell; Rocco A. Servedio; Li-Yang Tan; Daniel Kane 001 (CoRR, 2021)

Threshold Phenomena in Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (CoRR, 2021)

Quantum Money from Quaternion Algebras. - Daniel M. Kane; Shahed Sharif; Alice Silverberg (IACR Cryptol. ePrint Arch., 2021)

Hardness of Learning a Single Neuron with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi; Lisheng Ren (AISTATS, 2022)

Coresets for Data Discretization and Sine Wave Fitting. - Alaa Maalouf; Murad Tukan; Eric Price 001; Daniel M. Kane; Dan Feldman (AISTATS, 2022)

Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001 (COLT, 2022)

Non-Gaussian Component Analysis via Lattice Basis Reduction. - Ilias Diakonikolas; Daniel Kane 001 (COLT, 2022)

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

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

Computational-Statistical Gap in Reinforcement Learning. - Daniel Kane 001; Sihan Liu; Shachar Lovett; Gaurav Mahajan (COLT, 2022)

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

Outlier-Robust Sparse Estimation via Non-Convex Optimization. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Shivam Gupta 002; Daniel Kane 001; Mahdi Soltanolkotabi (NeurIPS, 2022)

Nearly-Tight Bounds for Testing Histogram Distributions. - Clément L. Canonne; Ilias Diakonikolas; Daniel Kane 001; Sihan Liu (NeurIPS, 2022)

List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering. - Ilias Diakonikolas; Daniel Kane 001; Sushrut Karmalkar; Ankit Pensia; Thanasis Pittas (NeurIPS, 2022)

Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions. - Ilias Diakonikolas; Daniel Kane 001; Jasper C. H. Lee; Ankit Pensia (NeurIPS, 2022)

Cryptographic Hardness of Learning Halfspaces with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001; Pasin Manurangsi; Lisheng Ren (NeurIPS, 2022)

SQ Lower Bounds for Learning Single Neurons with Massart Noise. - Ilias Diakonikolas; Daniel Kane 001; Lisheng Ren; Yuxin Sun (NeurIPS, 2022)

Robustly learning mixtures of k arbitrary Gaussians. - Ainesh Bakshi; Ilias Diakonikolas; He Jia; Daniel M. Kane; Pravesh K. Kothari; Santosh S. Vempala (STOC, 2022)

Clustering mixture models in almost-linear time via list-decodable mean estimation. - Ilias Diakonikolas; Daniel M. Kane; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (STOC, 2022)

Learning general halfspaces with general Massart noise under the Gaussian distribution. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (STOC, 2022)

Computational-Statistical Gaps in Reinforcement Learning. - Daniel Kane 001; Sihan Liu; Shachar Lovett; Gaurav Mahajan (CoRR, 2022)

Near-Optimal Bounds for Testing Histogram Distributions. - Clément L. Canonne; Ilias Diakonikolas; Daniel M. Kane; Sihan Liu (CoRR, 2022)

A Strongly Polynomial Algorithm for Approximate Forster Transforms and its Application to Halfspace Learning. - Ilias Diakonikolas; Christos Tzamos; Daniel Kane 001 (Electron. Colloquium Comput. Complex., 2022)

vqSGD: Vector Quantized Stochastic Gradient Descent. - Venkata Gandikota; Daniel Kane 001; Raj Kumar Maity; Arya Mazumdar (IEEE Trans. Inf. Theory, 2022)

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

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

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

A Nearly Tight Bound for Fitting an Ellipsoid to Gaussian Random Points. - Daniel Kane 001; Ilias Diakonikolas (COLT, 2023)

Exponential Hardness of Reinforcement Learning with Linear Function Approximation. - Sihan Liu; Gaurav Mahajan; Daniel Kane 001; Shachar Lovett; Gellért Weisz; Csaba Szepesvári (COLT, 2023)

Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA. - Ilias Diakonikolas; Daniel Kane 001; Ankit Pensia; Thanasis Pittas (ICML, 2023)

Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals. - Ilias Diakonikolas; Daniel Kane 001; Lisheng Ren (ICML, 2023)

Near-Optimal Bounds for Learning Gaussian Halfspaces with Random Classification Noise. - Ilias Diakonikolas; Jelena Diakonikolas; Daniel Kane 001; Puqian Wang; Nikos Zarifis (NeurIPS, 2023)

Efficient Testable Learning of Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel Kane 001; Vasilis Kontonis; Sihan Liu; Nikos Zarifis (NeurIPS, 2023)

A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm. - Ilias Diakonikolas; Daniel Kane 001; Jasper C. H. Lee; Ankit Pensia; Thanasis Pittas (NeurIPS, 2023)

Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression. - Ilias Diakonikolas; Daniel Kane 001; Ankit Pensia; Thanasis Pittas (NeurIPS, 2023)

SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions. - Ilias Diakonikolas; Daniel Kane 001; Lisheng Ren; Yuxin Sun (NeurIPS, 2023)

SQ Lower Bounds for Learning Mixtures of Linear Classifiers. - Ilias Diakonikolas; Daniel Kane 001; Yuxin Sun (NeurIPS, 2023)

Sampling Equilibria: Fast No-Regret Learning in Structured Games. - Daniel Beaglehole; Max Hopkins; Daniel Kane 001; Sihan Liu; Shachar Lovett (SODA, 2023)

Gaussian Mean Testing Made Simple. - Ilias Diakonikolas; Daniel M. Kane; Ankit Pensia (SOSA, 2023)

SQ Lower Bounds for Learning Bounded Covariance GMMs. - Ilias Diakonikolas; Daniel M. Kane; Thanasis Pittas; Nikos Zarifis (CoRR, 2023)

Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2023)

New Lower Bounds for Testing Monotonicity and Log Concavity of Distributions. - Yuqian Cheng; Daniel M. Kane; Zhicheng Zheng (COLT, 2024)

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

Testable Learning of General Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Nikos Zarifis (COLT, 2024)

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

Agnostically Learning Multi-Index Models with Queries. - Ilias Diakonikolas; Daniel M. Kane; Vasilis Kontonis; Christos Tzamos; Nikos Zarifis (FOCS, 2024)

Replicability in High Dimensional Statistics. - Max Hopkins; Russell Impagliazzo; Daniel M. Kane; Sihan Liu; Christopher Ye 001 (FOCS, 2024)

Robust Sparse Estimation for Gaussians with Optimal Error under Huber Contamination. - Ilias Diakonikolas; Daniel Kane 001; Sushrut Karmalkar; Ankit Pensia; Thanasis Pittas (ICML, 2024)

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

Online Robust Mean Estimation. - Daniel M. Kane; Ilias Diakonikolas; Hanshen Xiao; Sihan Liu (SODA, 2024)

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 (STOC, 2024)

Testing Closeness of Multivariate Distributions via Ramsey Theory. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu (STOC, 2024)

Efficient Testable Learning of General Halfspaces with Adversarial Label Noise. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Nikos Zarifis (CoRR, 2024)

Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models. - Ilias Diakonikolas; Daniel M. Kane (CoRR, 2024)

Locally Sampleable Uniform Symmetric Distributions. - Daniel Kane 001; Anthony Ostuni; Kewen Wu 001 (Electron. Colloquium Comput. Complex., 2024)

Locality Bounds for Sampling Hamming Slices. - Daniel M. Kane; Anthony Ostuni; Kewen Wu 001 (Electron. Colloquium Comput. Complex., 2024)

Realizable Learning is All You Need. - Max Hopkins; Daniel M. Kane; Shachar Lovett; Gaurav Mahajan (TheoretiCS, 2024)

Do PAC-Learners Learn the Marginal Distribution? - Max Hopkins; Daniel M. Kane; Shachar Lovett; Gaurav Mahajan (ALT, 2025)

Faster Algorithms for Agnostically Learning Disjunctions and their Implications. - Ilias Diakonikolas; Daniel M. Kane; Lisheng Ren (COLT, 2025)

Clustering Mixtures of Bounded Covariance Distributions Under Optimal Separation. - Ilias Diakonikolas; Daniel M. Kane; Jasper C. H. Lee; Thanasis Pittas (SODA, 2025)

Entangled Mean Estimation in High Dimensions. - Ilias Diakonikolas; Daniel M. Kane; Sihan Liu; Thanasis Pittas (STOC, 2025)

Robust Learning of Multi-index Models via Iterative Subspace Approximation. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Nikos Zarifis (CoRR, 2025)

Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Thanasis Pittas (CoRR, 2025)

Batch List-Decodable Linear Regression via Higher Moments. - Ilias Diakonikolas; Daniel M. Kane; Sushrut Karmalkar; Sihan Liu; Thanasis Pittas (CoRR, 2025)

On Learning Parallel Pancakes with Mostly Uniform Weights. - Ilias Diakonikolas; Daniel M. Kane; Sushrut Karmalkar; Jasper C. H. Lee; Thanasis Pittas (CoRR, 2025)

Algorithms and SQ Lower Bounds for Robustly Learning Real-valued Multi-index Models. - Ilias Diakonikolas; Giannis Iakovidis; Daniel M. Kane; Lisheng Ren (CoRR, 2025)

On Fine-Grained Distinct Element Estimation. - Ilias Diakonikolas; Daniel M. Kane; Jasper C. H. Lee; Thanasis Pittas; David P. Woodruff; Samson Zhou (CoRR, 2025)

Replicable Distribution Testing. - Ilias Diakonikolas; Jingyi Gao; Daniel M. Kane; Sihan Liu; Christopher Ye 001 (CoRR, 2025)