stochastic gradient descent

The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing NeurIPS 2024 The Marginal Value of Momentum for Small Learning Rate SGD ICLR 2024 The Road Less Scheduled NeurIPS 2024 The Role of Learning Algorithms in Collective Action ICML 2024 Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions JMLR 2024 Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective NeurIPS 2024 Tolerating Outliers: Gradient-Based Penalties for Byzantine Robustness and Inclusion IJCAI 2024 Towards Exact Gradient-based Training on Analog In-memory Computing NeurIPS 2024 Towards Stability and Generalization Bounds in Decentralized Minibatch Stochastic Gradient Descent AAAI 2024 Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States UAI 2024 Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning ICLR 2024 Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024 Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets JMLR 2024 Universality in Transfer Learning for Linear Models NeurIPS 2024 Wasserstein Differential Privacy AAAI 2024 Weight Conditioning for Smooth Optimization of Neural Networks ECCV 2024 What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding ICML 2024 What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis ICML 2024 Why Do We Need Weight Decay in Modern Deep Learning? NeurIPS 2024 Why is parameter averaging beneficial in SGD? An objective smoothing perspective AISTATS 2024 Why Transformers Need Adam: A Hessian Perspective NeurIPS 2024 Why Warmup the Learning Rate? Underlying Mechanisms and Improvements NeurIPS 2024 L_2-Uniform Stability of Randomized Learning Algorithms: Sharper Generalization Bounds and Confidence Boosting NeurIPS 2023 A Bayesian Approach To Analysing Training Data Attribution In Deep Learning NeurIPS 2023 A Continuous-time Stochastic Gradient Descent Method for Continuous Data JMLR 2023