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)

Exact marginal prior distributions of finite Bayesian neural networks NeurIPS 2021 Exact Optimization of Conformal Predictors via Incremental and Decremental Learning ICML 2021 Exact Reduction of Huge Action Spaces in General Reinforcement Learning AAAI 2021 Examining and Combating Spurious Features under Distribution Shift ICML 2021 Examining the Inductive Bias of Neural Language Models with Artificial Languages ACL 2021 ExCAR: Event Graph Knowledge Enhanced Explainable Causal Reasoning ACL 2021 Expanding Boundaries of Gap Safe Screening JMLR 2021 Expected Eligibility Traces AAAI 2021 Expected Validation Performance and Estimation of a Random Variable’s Maximum EMNLP 2021 Experimental Design for Regret Minimization in Linear Bandits AISTATS 2021 Expert advice problem with noisy low rank loss ACML 2021 Explainable Automated Graph Representation Learning with Hyperparameter Importance ICML 2021 ExplainaBoard: An Explainable Leaderboard for NLP ACL 2021 Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach AAAI 2021 Explaining Contextualization in Language Models using Visual Analytics ACL 2021 Explaining Deep Neural Network Models with Adversarial Gradient Integration IJCAI 2021 Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021 Explaining Hyperparameter Optimization via Partial Dependence Plots NeurIPS 2021 Explaining Latent Representations with a Corpus of Examples NeurIPS 2021 Explicit loss asymptotics in the gradient descent training of neural networks NeurIPS 2021 Explicit Regularization of Stochastic Gradient Methods through Duality AISTATS 2021 Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions NeurIPS 2021 Exploiting Curriculum Learning in Unsupervised Neural Machine Translation EMNLP 2021 Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach NeurIPS 2021 Exploiting Sparsity for Neural Network Verification L4DC 2021