Approximation Algorithms

OneBatchPAM: A Fast and Frugal K-Medoids Algorithm AAAI 2025 Provably Accurate Shapley Value Estimation via Leverage Score Sampling ICLR 2025 Faster Approximation of Probabilistic and Distributional Values via Least Squares ICLR 2024 On Socially Fair Low-Rank Approximation and Column Subset Selection NeurIPS 2024 Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms ICLR 2024 Time–Approximation Trade-Offs for Learning Bayesian Networks PGM 2024 Coverage-centric Coreset Selection for High Pruning Rates ICLR 2023 Fair, Polylog-Approximate Low-Cost Hierarchical Clustering NeurIPS 2023 Provably Auditing Ordinary Least Squares in Low Dimensions ICLR 2023 Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation ICLR 2023 Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams. ICLR 2023 From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization COLT 2022 Hardness of Maximum Likelihood Learning of DPPs COLT 2022 Learning-Augmented k-means Clustering ICLR 2022 Recruitment Strategies That Take a Chance NeurIPS 2022 Approximation Schemes for ReLU Regression COLT 2020 Fairness in Streaming Submodular Maximization: Algorithms and Hardness NeurIPS 2020 Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method NeurIPS 2020 Hierarchical Clustering with Structural Constraints ICML 2018 Gaussian Quadrature for Kernel Features NeurIPS 2017 Recursive Sampling for the Nystrom Method NeurIPS 2017 A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ NeurIPS 2016 Adaptive Stochastic Optimization: From Sets to Paths NeurIPS 2015 Distributed Submodular Cover: Succinctly Summarizing Massive Data NeurIPS 2015 Orthogonal NMF through Subspace Exploration NeurIPS 2015