Stochastic Methods

Stochastic Gradient Succeeds for Bandits ICML 2023 Stochastic Graphical Bandits with Heavy-Tailed Rewards UAI 2023 Stochastic Mirror Descent for Large-Scale Sparse Recovery AISTATS 2023 Stochastic Optimization for Spectral Risk Measures AISTATS 2023 Stochastic Optimization under Distributional Drift JMLR 2023 Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms IJCAI 2023 Streaming PCA for Markovian Data NeurIPS 2023 Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences IJCAI 2023 Subset-Based Instance Optimality in Private Estimation ICML 2023 Taming graph kernels with random features ICML 2023 Tanimoto Random Features for Scalable Molecular Machine Learning NeurIPS 2023 The Behavior and Convergence of Local Bayesian Optimization NeurIPS 2023 The Price of Differential Privacy under Continual Observation ICML 2023 Tighter Analysis for ProxSkip ICML 2023 Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond ICML 2023 Tighter Robust Upper Bounds for Options via No-Regret Learning AAAI 2023 Top-k data selection via distributed sample quantile inference L4DC 2023 Towards Quantum Machine Learning for Constrained Combinatorial Optimization: a Quantum QAP Solver ICML 2023 Transformer-based Stagewise Decomposition for Large-Scale Multistage Stochastic Optimization ICML 2023 Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters NeurIPS 2023 Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs AAAI 2023 Truncating Trajectories in Monte Carlo Reinforcement Learning ICML 2023 Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy ICML 2023 Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective ICML 2023 Unbiased Compression Saves Communication in Distributed Optimization: When and How Much? NeurIPS 2023