Continuous Optimization

Stochastic Gradient Flow Dynamics of Test Risk and its Exact Solution for Weak Features ICML 2024 Stochastic Newton Proximal Extragradient Method NeurIPS 2024 Stochastic Optimal Control and Estimation with Multiplicative and Internal Noise NeurIPS 2024 Stochastic Optimization with Arbitrary Recurrent Data Sampling ICML 2024 Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions ICML 2024 Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates JMLR 2024 Structured Transforms Across Spaces with Cost-Regularized Optimal Transport AISTATS 2024 Superposed Atomic Representation for Robust High-Dimensional Data Recovery of Multiple Low-Dimensional Structures AAAI 2024 Supervised Kernel Thinning NeurIPS 2024 Symmetric Matrix Completion with ReLU Sampling ICML 2024 Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor NeurIPS 2024 Testing the Feasibility of Linear Programs with Bandit Feedback ICML 2024 The behavioral toolbox L4DC 2024 The Computational Complexity of Finding Second-Order Stationary Points ICML 2024 The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels NeurIPS 2024 The Non-Overlapping Statistical Approximation to Overlapping Group Lasso JMLR 2024 Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians ICML 2024 The Price of Adaptivity in Stochastic Convex Optimization COLT 2024 The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems NeurIPS 2024 The Role of Learning Algorithms in Collective Action ICML 2024 The Solution Path of SLOPE AISTATS 2024 TIC-TAC: A Framework For Improved Covariance Estimation In Deep Heteroscedastic Regression ICML 2024 Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions JMLR 2024 TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization AAAI 2024 Total Variation Distance Meets Probabilistic Inference ICML 2024