Skip to content
conftrace_

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
'5
'10
'15
'20
'25

Papers (including subtopics)

Point process models for sequence detection in high-dimensional neural spike trains NeurIPS 2020 Policy Learning of MDPs with Mixed Continuous/Discrete Variables: A Case Study on Model-Free Control of Markovian Jump Systems L4DC 2020 Policy Search by Target Distribution Learning for Continuous Control AAAI 2020 Polishing the Classical Likelihood Ratio Test by Supervised Learning for Voice Activity Detection Interspeech 2020 Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks ICLR 2020 POP ≡ POCL, Right? Complexity Results for Partial Order (Causal Link) Makespan Minimization AAAI 2020 Porous Lattice Transformer Encoder for Chinese NER COLING 2020 Position-based Scaled Gradient for Model Quantization and Pruning NeurIPS 2020 Positive Unlabeled Learning with Class-prior Approximation IJCAI 2020 Posterior Calibrated Training on Sentence Classification Tasks ACL 2020 Posterior-Guided Neural Architecture Search AAAI 2020 Posterior Re-calibration for Imbalanced Datasets NeurIPS 2020 Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications JMLR 2020 Potential Driven Reinforcement Learning for Hard Exploration Tasks IJCAI 2020 PowerNorm: Rethinking Batch Normalization in Transformers ICML 2020 Practical Low-Rank Communication Compression in Decentralized Deep Learning NeurIPS 2020 Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes AISTATS 2020 Practical Quasi-Newton Methods for Training Deep Neural Networks NeurIPS 2020 Precise Tradeoffs in Adversarial Training for Linear Regression COLT 2020 Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations ICLR 2020 Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods AAAI 2020 Predicting Declension Class from Form and Meaning ACL 2020 Predicting Performance for Natural Language Processing Tasks ACL 2020 Predicting Propositional Satisfiability via End-to-End Learning AAAI 2020 Predicting the Growth of Morphological Families from Social and Linguistic Factors ACL 2020