conftrace_

convergence guarantee

169 papers

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Co-occurring keywords

stochastic gradient descent (1091) stochastic optimization (1060) gradient descent (1144) non-convex optimization (547) convergence analysis (395) federated learning (1333) reinforcement learning (4352) neural network optimization (1293) off-policy learning (228) neural network (6616)

Papers

Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad NIPS 2024
Unraveling the Gradient Descent Dynamics of Transformers NIPS 2024
Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation NIPS 2024
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence AISTATS 2024
On the convergence of adaptive first order methods: Proximal gradient and alternating minimization algorithms L4DC 2024
A Boosting-Type Convergence Result for AdaBoost.MH with Factorized Multi-Class Classifiers NIPS 2024
SAMPa: Sharpness-aware Minimization Parallelized NIPS 2024
Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs JMLR 2024
Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique JMLR 2024
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence NIPS 2024
Regularized Q-Learning NIPS 2024
SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective CVPR 2024
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling NIPS 2024
Local and Adaptive Mirror Descents in Extensive-Form Games NIPS 2024
An Inexact Augmented Lagrangian Algorithm for Training Leaky ReLU Neural Network with Group Sparsity JMLR 2023
Sparse Training with Lipschitz Continuous Loss Functions and a Weighted Group L0-norm Constraint JMLR 2023
On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size JMLR 2023
Policy-Based Primal-Dual Methods for Convex Constrained Markov Decision Processes AAAI 2023
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space ICML 2023
Gibbsian Polar Slice Sampling ICML 2023
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance ICML 2023
Computational Guarantees for Doubly Entropic Wasserstein Barycenters NIPS 2023
H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets NIPS 2023
Auxiliary Learning as an Asymmetric Bargaining Game ICML 2023
Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods ICML 2023