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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

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Papers (including subtopics)

Shadowheart SGD: Distributed Asynchronous SGD with Optimal Time Complexity Under Arbitrary Computation and Communication Heterogeneity NeurIPS 2024 ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models EMNLP 2024 Shaping the distribution of neural responses with interneurons in a recurrent circuit model NeurIPS 2024 SHAP@k: Efficient and Probably Approximately Correct (PAC) Identification of Top-K Features AAAI 2024 Sharp analysis of power iteration for tensor PCA JMLR 2024 Sharpened Lazy Incremental Quasi-Newton Method AISTATS 2024 Sharp error bounds for imbalanced classification: how many examples in the minority class? AISTATS 2024 Sharpness-Aware Minimization Activates the Interactive Teaching's Understanding and Optimization NeurIPS 2024 Sharpness-Aware Minimization and the Edge of Stability JMLR 2024 Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning ICLR 2024 Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance NeurIPS 2024 Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss ICML 2024 SharpSeq: Empowering Continual Event Detection through Sharpness-Aware Sequential-task Learning NAACL 2024 Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood NeurIPS 2024 Sheared Backpropagation for Fine-tuning Foundation Models CVPR 2024 Shears: Unstructured Sparsity with Neural Low-rank Adapter Search NAACL 2024 SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning NeurIPS 2024 ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization NeurIPS 2024 Shifted Interpolation for Differential Privacy ICML 2024 Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models ACL 2024 Should I try multiple optimizers when fine-tuning a pre-trained Transformer for NLP tasks? Should I tune their hyperparameters? EACL 2024 Show Your Work with Confidence: Confidence Bands for Tuning Curves NAACL 2024 Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization NeurIPS 2024 SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning ACL 2024 s-ID: Causal Effect Identification in a Sub-population AAAI 2024