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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)

Discovering Evolution Strategies via Meta-Black-Box Optimization ICLR 2023 Discovering Language Model Behaviors with Model-Written Evaluations ACL 2023 Discovering Many Diverse Solutions with Bayesian Optimization AISTATS 2023 Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering CVPR 2023 Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023 Discrete Predictor-Corrector Diffusion Models for Image Synthesis ICLR 2023 Discrete-Smoothness in Online Algorithms with Predictions NeurIPS 2023 Discrete Two Player All-Pay Auction with Complete Information IJCAI 2023 Discrete Variational Calculus for Accelerated Optimization JMLR 2023 Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation AAAI 2023 Discriminating Known From Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder CVPR 2023 Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier NeurIPS 2023 DISCS: A Benchmark for Discrete Sampling NeurIPS 2023 Disentangled Multi-Fidelity Deep Bayesian Active Learning ICML 2023 Disentangling Learning Representations with Density Estimation ICLR 2023 Disentangling the Mechanisms Behind Implicit Regularization in SGD ICLR 2023 Disposable Transfer Learning for Selective Source Task Unlearning ICCV 2023 DiSProD: Differentiable Symbolic Propagation of Distributions for Planning IJCAI 2023 Dissecting Deep Metric Learning Losses for Image-Text Retrieval WACV 2023 Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning ICML 2023 Distance-to-Set Priors and Constrained Bayesian Inference AISTATS 2023 Distortion and Uncertainty Aware Loss for Panoramic Depth Completion ICML 2023 Distributed Algorithms for U-statistics-based Empirical Risk Minimization JMLR 2023 Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost ICML 2023 Distributed Extra-gradient with Optimal Complexity and Communication Guarantees ICLR 2023