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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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52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms ICML 2024 Agnostic Membership Query Learning with Nontrivial Savings: New Results and Techniques ALT 2024 Agnostic Multi-Robust Learning using ERM AISTATS 2024 Agnostic Sample Compression Schemes for Regression ICML 2024 A Goal-Directed Dialogue System for Assistance in Safety-Critical Application IJCAI 2024 A Greedy Approximation for k-Determinantal Point Processes AISTATS 2024 A Hierarchical Adaptive Multi-Task Reinforcement Learning Framework for Multiplier Circuit Design ICML 2024 A hierarchical decomposition for explaining ML performance discrepancies NeurIPS 2024 A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network NSDI 2024 A high-quality robust diffusion framework for corrupted dataset ECCV 2024 Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound AAAI 2024 A Huber Loss Minimization Approach to Byzantine Robust Federated Learning AAAI 2024 A Hypothesis-Driven Framework for the Analysis of Self-Rationalising Models EACL 2024 Ai-sampler: Adversarial Learning of Markov kernels with involutive maps ICML 2024 A Kernel Perspective on Distillation-based Collaborative Learning NeurIPS 2024 A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment JMLR 2024 A large deviations perspective on policy gradient algorithms L4DC 2024 A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks NeurIPS 2024 A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models EMNLP 2024 Algorithm and Hardness for Dynamic Attention Maintenance in Large Language Models ICML 2024 Algorithm for Automatic Legislative Text Consolidation EMNLP 2024 Algorithmic Capabilities of Random Transformers NeurIPS 2024 Algorithmic Foundation of Federated Learning with Sequential Data AAAI 2024 Algorithmic progress in language models NeurIPS 2024 Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses ICML 2024