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cs.DS2026
High-Magnetization Sampling at Low Temperatures: Ising Models and Bayesian Sparse Linear Regression
Syamantak Kumar, Purnamrita Sarkar, Kevin Tian +1
Sparsity is a powerful structural resource in optimization and statistics. We develop frameworks for leveraging sparsity in sampling problems over the Hamming slice $\mathcal{X}_k^…
cs.DS2025
Private Geometric Median in Nearly-Linear Time
Syamantak Kumar, Daogao Liu, Kevin Tian +1
Estimating the geometric median of a dataset is a robust counterpart to mean estimation, and is a fundamental problem in computational geometry. Recently, [HSU24] gave an $(\vareps…
cs.DS2024
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
Hilal Asi, Daogao Liu, Kevin Tian
We study the problem of differentially private stochastic convex optimization (DP-SCO) with heavy-tailed gradients, where we assume a -moment bound on the Lipschitz…