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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)

Unconditional Coresets for Regularized Loss Minimization AISTATS 2020 Uncorrected Least-Squares Temporal Difference with Lambda-Return AAAI 2020 Understanding and Exploring the Network with Stochastic Architectures NeurIPS 2020 Understanding and Mitigating the Tradeoff between Robustness and Accuracy ICML 2020 Understanding and Robustifying Differentiable Architecture Search ICLR 2020 Understanding and Stabilizing GANs’ Training Dynamics Using Control Theory ICML 2020 Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks NeurIPS 2020 Understanding Attention for Text Classification ACL 2020 Understanding Deep Architecture with Reasoning Layer NeurIPS 2020 Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition NeurIPS 2020 Understanding Generalization in Deep Learning via Tensor Methods AISTATS 2020 Understanding Generalization in Neural Networks for Robustness against Adversarial Vulnerabilities AAAI 2020 Understanding Generalization in Recurrent Neural Networks ICLR 2020 Understanding Global Feature Contributions With Additive Importance Measures NeurIPS 2020 Understanding Gradient Clipping in Private SGD: A Geometric Perspective NeurIPS 2020 Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness ICLR 2020 Understanding Neural Abstractive Summarization Models via Uncertainty EMNLP 2020 Understanding spiking networks through convex optimization NeurIPS 2020 Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling ICML 2020 Understanding the Difficulty of Training Transformers EMNLP 2020 Understanding the Disharmony between Weight Normalization Family and Weight Decay AAAI 2020 Understanding the Effect of Voice Quality and Accent on Talker Similarity Interspeech 2020 Understanding the Effects of Batching in Online Active Learning AISTATS 2020 Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle ICML 2020 Understanding the Limitations of Variational Mutual Information Estimators ICLR 2020