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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)

Evolution of Neural Tangent Kernels under Benign and Adversarial Training NeurIPS 2022 ExactBoost: Directly Boosting the Margin in Combinatorial and Non-decomposable Metrics AISTATS 2022 Exact Community Recovery in Correlated Stochastic Block Models COLT 2022 Exact Community Recovery over Signed Graphs AISTATS 2022 Exact learning dynamics of deep linear networks with prior knowledge NeurIPS 2022 Exact Learning of Preference Structure: Single-peaked Preferences and Beyond ICML 2022 Exact Optimal Accelerated Complexity for Fixed-Point Iterations ICML 2022 Exact Paired-Permutation Testing for Structured Test Statistics NAACL 2022 Exact Solutions of a Deep Linear Network NeurIPS 2022 Execution-based Evaluation for Data Science Code Generation Models EMNLP 2022 Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks JMLR 2022 Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently UAI 2022 Expected Improvement for Contextual Bandits NeurIPS 2022 Expected Regret and Pseudo-Regret are Equivalent When the Optimal Arm is Unique JMLR 2022 Experience Replay with Likelihood-free Importance Weights L4DC 2022 Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces NeurIPS 2022 Experimental Standards for Deep Learning in Natural Language Processing Research EMNLP 2022 Explainable Shapley-Based Allocation (Student Abstract) AAAI 2022 Explaining Preferences with Shapley Values NeurIPS 2022 Explicit Alignment Learning for Neural Machine Translation IJCAI 2022 Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods JMLR 2022 Exploiting Explainable Metrics for Augmented SGD CVPR 2022 Exploiting Independent Instruments: Identification and Distribution Generalization ICML 2022 Exploiting Invariance in Training Deep Neural Networks AAAI 2022 Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping NeurIPS 2022