conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Optimization

14,983 papers

Papers per year

18
112
342
1289
1646
'05
'10
'15
'20
'25

Papers

Graph-Coupled Oscillator Networks ICML 2022
Hindering Adversarial Attacks with Implicit Neural Representations ICML 2022
FedNL: Making Newton-Type Methods Applicable to Federated Learning ICML 2022
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers ICML 2022
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training ICML 2022
A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1 ICML 2022
PoF: Post-Training of Feature Extractor for Improving Generalization ICML 2022
Re-evaluating Word Mover’s Distance ICML 2022
Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness ICML 2022
Linear-Time Gromov Wasserstein Distances using Low Rank Couplings and Costs ICML 2022
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation ICML 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms ICML 2022
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning ICML 2022
Staged Training for Transformer Language Models ICML 2022
Gradient-Free Method for Heavily Constrained Nonconvex Optimization ICML 2022
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity ICML 2022
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes ICML 2022
Robust Group Synchronization via Quadratic Programming ICML 2022
Scalable Computation of Causal Bounds ICML 2022
Fair Representation Learning through Implicit Path Alignment ICML 2022
Faster Algorithms for Learning Convex Functions ICML 2022
Demystifying the Adversarial Robustness of Random Transformation Defenses ICML 2022
Communicating via Markov Decision Processes ICML 2022
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning ICML 2022
A General Recipe for Likelihood-free Bayesian Optimization ICML 2022