Optimization & Theory

SGD vs GD: Rank Deficiency in Linear Networks NeurIPS 2024 SGLang: Efficient Execution of Structured Language Model Programs NeurIPS 2024 ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models EMNLP 2024 ShareBERT: Embeddings Are Capable of Learning Hidden Layers AAAI 2024 Sharpened Lazy Incremental Quasi-Newton Method AISTATS 2024 Sharpness-Aware Minimization and the Edge of Stability JMLR 2024 SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation AAAI 2024 Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood NeurIPS 2024 ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization NeurIPS 2024 SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild CVPR 2024 Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization NeurIPS 2024 SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design CVPR 2024 SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models AAAI 2024 Simple and Fast Distillation of Diffusion Models NeurIPS 2024 Simplicity Bias in Overparameterized Machine Learning AAAI 2024 Simplifying Transformer Blocks ICLR 2024 Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions NeurIPS 2024 Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm AISTATS 2024 SIRIUS : Contexual Sparisty with Correction for Efficient LLMs NeurIPS 2024 Sketched Lanczos uncertainty score: a low-memory summary of the Fisher information NeurIPS 2024 Sketching for Distributed Deep Learning: A Sharper Analysis NeurIPS 2024 Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning NeurIPS 2024 Sliced Wasserstein with Random-Path Projecting Directions ICML 2024 Slicing Vision Transformer for Flexible Inference NeurIPS 2024 SlimSAM: 0.1% Data Makes Segment Anything Slim NeurIPS 2024