conftrace_

convergence rate

607 papers

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Co-occurring keywords

stochastic gradient descent (1091) convex optimization (1321) stochastic optimization (1060) gradient descent (1144) convergence analysis (395) non-convex optimization (547) nonconvex optimization (316) variance reduction (523) stochastic gradient (296) learning theory (519)

Papers

Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks JMLR 2024
Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks NIPS 2024
Debiasing Synthetic Data Generated by Deep Generative Models NIPS 2024
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing JMLR 2024
Optimal Locally Private Nonparametric Classification with Public Data JMLR 2024
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency COLT 2024
Open Problem: Anytime Convergence Rate of Gradient Descent COLT 2024
Analysis of Kernel Mirror Prox for Measure Optimization AISTATS 2024
ADOPT: Modified Adam Can Converge with Any $\beta_2$ with the Optimal Rate NIPS 2024
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling AISTATS 2024
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning NIPS 2024
Parameter-Agnostic Optimization under Relaxed Smoothness AISTATS 2024
Physics-informed machine learning as a kernel method COLT 2024
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract) COLT 2024
Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods JMLR 2024
Mean-Field Langevin Dynamics for Signed Measures via a Bilevel Approach NIPS 2024
Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts AAAI 2024
Random Smoothing Regularization in Kernel Gradient Descent Learning JMLR 2024
Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing NIPS 2024
Compressed and distributed least-squares regression: convergence rates with applications to federated learning JMLR 2024
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method JMLR 2024
Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates JMLR 2024
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces NIPS 2024
Provable Acceleration of Nesterov’s Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks IJCAI 2024
Optimizing ADMM and Over-Relaxed ADMM Parameters for Linear Quadratic Problems AAAI 2024