 Monotonicity Testing of High-Dimensional Distributions with Subcube ConditioningSymposium on the Theory of Computing (STOC), 2025 |
 Approximating the total variation distance between spin systemsAnnual Conference Computational Learning Theory (COLT), 2025 |
 Optimal Algorithms for Augmented Testing of Discrete DistributionsNeural Information Processing Systems (NeurIPS), 2024 |
 Total Variation Distance Meets Probabilistic InferenceInternational Conference on Machine Learning (ICML), 2023 |
 Lifting uniform learners via distributional decompositionSymposium on the Theory of Computing (STOC), 2023 |
 Learning Hidden Markov Models Using Conditional SamplesAnnual Conference Computational Learning Theory (COLT), 2023 |
 Estimating the Effective Support Size in Constant Query ComplexitySIAM Symposium on Simplicity in Algorithms (SOSA), 2022 |
 Bias Reduction for Sum EstimationInternational Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), 2022 |
 Daisy Bloom FiltersScandinavian Workshop on Algorithm Theory (SWAT), 2022 |
 Better Sum Estimation via Weighted SamplingACM-SIAM Symposium on Discrete Algorithms (SODA), 2021 |
 On Distribution Testing in the Conditional Sampling ModelACM-SIAM Symposium on Discrete Algorithms (SODA), 2020 |
 Learning and Testing Junta Distributions with Subcube ConditioningAnnual Conference Computational Learning Theory (COLT), 2020 |
 Efficient Distance Approximation for Structured High-Dimensional
Distributions via LearningNeural Information Processing Systems (NeurIPS), 2020 |
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 A Chasm Between Identity and Equivalence Testing with Conditional
QueriesInternational Workshop and International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), 2014 |
 On the Power of Conditional Samples in Distribution TestingInformation Technology Convergence and Services (ITCS), 2012 |
