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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)

What Language Model to Train if You Have One Million GPU Hours? EMNLP 2022 What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment EMNLP 2022 What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization ICLR 2022 What to Learn, and How: Toward Effective Learning from Rationales ACL 2022 What You See is What You Get: Principled Deep Learning via Distributional Generalization NeurIPS 2022 When and How Mixup Improves Calibration ICML 2022 When Are Linear Stochastic Bandits Attackable? ICML 2022 When are Local Queries Useful for Robust Learning? NeurIPS 2022 When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee ICML 2022 When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks EMNLP 2022 When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? ICLR 2022 When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation ACL 2022 When Combinatorial Thompson Sampling meets Approximation Regret NeurIPS 2022 When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS 2022 When Do Flat Minima Optimizers Work? NeurIPS 2022 When Hardness of Approximation Meets Hardness of Learning JMLR 2022 When Is Partially Observable Reinforcement Learning Not Scary? COLT 2022 When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint JMLR 2022 When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems EMNLP 2022 When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits NeurIPS 2022 When to Update Your Model: Constrained Model-based Reinforcement Learning NeurIPS 2022 When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations ICLR 2022 When Votes Change and Committees Should (Not) IJCAI 2022 Which Model is Best: Comparing Methods and Metrics for Automatic Laughter Detection in a Naturalistic Conversational Dataset Interspeech 2022 Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective ICLR 2022