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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

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

SUT: Active Defects Probing for Transcompiler Models EMNLP 2023 SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search WACV 2023 SViTT: Temporal Learning of Sparse Video-Text Transformers CVPR 2023 Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration NeurIPS 2023 SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient ICML 2023 SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication ICLR 2023 SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting AAAI 2023 Symbolic Discovery of Optimization Algorithms NeurIPS 2023 Symbolic Explanations for Hyperparameter Optimization AutoML 2023 Symbolic Metamodels for Interpreting Black-Boxes Using Primitive Functions AAAI 2023 Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search ICLR 2023 Symmetric Pruning in Quantum Neural Networks ICLR 2023 Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow ICLR 2023 Synchronization and Diversity of Solutions AAAI 2023 SynJax: Structured Probability Distributions for JAX EMNLP 2023 Systematic Generalization by Finetuning? Analyzing Pretrained Language Models Using Constituency Tests EMNLP 2023 System Identification of Neural Systems: If We Got It Right, Would We Know? ICML 2023 T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization NeurIPS 2023 TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second ICLR 2023 TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches NSDI 2023 Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective COLT 2023 Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds NeurIPS 2023 Tailoring Language Generation Models under Total Variation Distance ICLR 2023 Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks ICLR 2023 Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system NeurIPS 2023