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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)

Towards Competitive N-gram Smoothing AISTATS 2020 Towards Convergence Rate Analysis of Random Forests for Classification NeurIPS 2020 Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts NeurIPS 2020 Towards Discriminability and Diversity: Batch Nuclear-Norm Maximization Under Label Insufficient Situations CVPR 2020 Towards Interpretation of Pairwise Learning AAAI 2020 Towards Learning Convolutions from Scratch NeurIPS 2020 Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes NeurIPS 2020 Towards multi-sequence MR image recovery from undersampled k-space data MIDL 2020 Towards Non-Parametric Drift Detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD) ICML 2020 Towards Oracle Knowledge Distillation with Neural Architecture Search AAAI 2020 Towards Problem-dependent Optimal Learning Rates NeurIPS 2020 Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent AAAI 2020 Towards Real-Time DNN Inference on Mobile Platforms with Model Pruning and Compiler Optimization IJCAI 2020 Towards Robustifying NLI Models Against Lexical Dataset Biases ACL 2020 Towards Scalable Bayesian Learning of Causal DAGs NeurIPS 2020 Towards Stable and Efficient Training of Verifiably Robust Neural Networks ICLR 2020 Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation ACL 2020 Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning NeurIPS 2020 Towards Understanding Hierarchical Learning: Benefits of Neural Representations NeurIPS 2020 Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations EMNLP 2020 Towards Understanding the Dynamics of the First-Order Adversaries ICML 2020 Towards Universal Languages for Tractable Ontology Mediated Query Answering AAAI 2020 Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations CVPR 2020 Toward the Fundamental Limits of Imitation Learning NeurIPS 2020 Toward universal testing of dynamic network models ALT 2020