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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)

Breaking the Communication-Privacy-Accuracy Trilemma NeurIPS 2020 Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning ICML 2020 Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model NeurIPS 2020 Bridging Maximum Likelihood and Adversarial Learning via α-Divergence AAAI 2020 Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness ICLR 2020 Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection CVPR 2020 Bridging the Gap Between f-GANs and Wasserstein GANs ICML 2020 Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS NeurIPS 2020 Budget-Constrained Bandits over General Cost and Reward Distributions AISTATS 2020 Budgeted Online Influence Maximization ICML 2020 Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints ICLR 2020 Budget Learning via Bracketing AISTATS 2020 Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors AAAI 2020 Bundle Adjustment on a Graph Processor CVPR 2020 Byte Pair Encoding is Suboptimal for Language Model Pretraining EMNLP 2020 Byzantine Ordered Consensus without Byzantine Oligarchy OSDI 2020 Byzantine Resilient Distributed Multi-Task Learning NeurIPS 2020 Cakewalk Sampling AAAI 2020 Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data EMNLP 2020 Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation AISTATS 2020 Calibrated Surrogate Losses for Adversarially Robust Classification COLT 2020 Calibrating Deep Neural Networks using Focal Loss NeurIPS 2020 Calibration, Entropy Rates, and Memory in Language Models ICML 2020 Can Cross Entropy Loss Be Robust to Label Noise? IJCAI 2020 Can Deep Learning Recognize Subtle Human Activities? CVPR 2020