conftrace_

gradient descent

1144 papers

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Also known as

GD

Co-occurring keywords

neural network (6616) neural network optimization (1293) convex optimization (1321) non-convex optimization (547) convergence rate (607) stochastic optimization (1060) convergence analysis (395) stochastic gradient descent (1091) nonconvex optimization (316) implicit bia (107)

Papers

Online Optimization over Riemannian Manifolds JMLR 2023
Spurious Valleys and Clustering Behavior of Neural Networks ICML 2023
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling ICML 2023
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold COLT 2023
On the Training Instability of Shuffling SGD with Batch Normalization ICML 2023
Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection JMLR 2023
SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning NIPS 2023
FAMO: Fast Adaptive Multitask Optimization NIPS 2023
Transformers Learn In-Context by Gradient Descent ICML 2023
Monotonic Alpha-divergence Minimisation for Variational Inference JMLR 2023
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability ICML 2023
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression NIPS 2023
Unifying Nesterov’s Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions ICML 2023
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning ICML 2023
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability NIPS 2023
Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians NIPS 2023
Forget Unlearning: Towards True Data-Deletion in Machine Learning ICML 2023
Convex and Non-convex Optimization Under Generalized Smoothness NIPS 2023
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign? NIPS 2023
Generalized-Smooth Nonconvex Optimization is As Efficient As Smooth Nonconvex Optimization ICML 2023
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy NIPS 2023
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time NIPS 2023
Beyond the Edge of Stability via Two-step Gradient Updates ICML 2023
Gradient Descent Converges Linearly for Logistic Regression on Separable Data ICML 2023
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods ACML 2023