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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)

Alleviating the Inequality of Attention Heads for Neural Machine Translation COLING 2022 All You Need is a Good Functional Prior for Bayesian Deep Learning JMLR 2022 Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions AISTATS 2022 Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations ICLR 2022 A Local Optimization Framework for Multi-Objective Ergodic Search RSS 2022 A Loss Curvature Perspective on Training Instabilities of Deep Learning Models ICLR 2022 Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022 Alternating Mirror Descent for Constrained Min-Max Games NeurIPS 2022 A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback AAAI 2022 A Manifold View of Adversarial Risk AISTATS 2022 AME: Attention and Memory Enhancement in Hyper-Parameter Optimization CVPR 2022 A Minimal Model for Compositional Generalization on gSCAN EMNLP 2022 A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes ICML 2022 Amodal Segmentation Through Out-of-Task and Out-of-Distribution Generalization With a Bayesian Model CVPR 2022 A Model-Agnostic Randomized Learning Framework based on Random Hypothesis Subspace Sampling ICML 2022 A Model-Oriented Approach for Lifting Symmetry-Breaking Constraints in Answer Set Programming IJCAI 2022 A Model Selection Approach for Corruption Robust Reinforcement Learning ALT 2022 A Momentumized, Adaptive, Dual Averaged Gradient Method JMLR 2022 Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation AISTATS 2022 Amortized Implicit Differentiation for Stochastic Bilevel Optimization ICLR 2022 Amortized Inference for Heterogeneous Reconstruction in Cryo-EM NeurIPS 2022 Amortized Mixing Coupling Processes for Clustering NeurIPS 2022 Amortized Projection Optimization for Sliced Wasserstein Generative Models NeurIPS 2022 Amortized Proximal Optimization NeurIPS 2022 Amortized Rejection Sampling in Universal Probabilistic Programming AISTATS 2022