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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)

Improved Kernel Alignment Regret Bound for Online Kernel Learning AAAI 2023 Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming ACL 2023 Improved Online Conformal Prediction via Strongly Adaptive Online Learning ICML 2023 Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation ICML 2023 Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning JMLR 2023 Improved Regret for Efficient Online Reinforcement Learning with Linear Function Approximation ICML 2023 Improved Robust Algorithms for Learning with Discriminative Feature Feedback AISTATS 2023 Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning AISTATS 2023 Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs ICLR 2023 Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs ICML 2023 Improved Test-Time Adaptation for Domain Generalization CVPR 2023 Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization NeurIPS 2023 Improving Adaptive Conformal Prediction Using Self-Supervised Learning AISTATS 2023 Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples ICML 2023 Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics ICML 2023 Improving Adversarial Robustness via Information Bottleneck Distillation NeurIPS 2023 Improving Adversarial Transferability via Intermediate-level Perturbation Decay NeurIPS 2023 Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models ACL 2023 Improving Bi-level Optimization Based Methods with Inspiration from Humans’ Classroom Study Techniques ICML 2023 Improving Chinese Pop Song and Hokkien Gezi Opera Singing Voice Synthesis by Enhancing Local Modeling EMNLP 2023 Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method ACL 2023 Improving Differentiable Neural Architecture Search by Encouraging Transferability ICLR 2023 Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining AISTATS 2023 Improving Expert Predictions with Conformal Prediction ICML 2023 Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data NeurIPS 2023