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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)

Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora EMNLP 2021 Learning Finite-Dimensional Representations For Koopman Operators L4DC 2021 Learning Frequency Domain Approximation for Binary Neural Networks NeurIPS 2021 Learning from Biased Data: A Semi-Parametric Approach ICML 2021 Learning from Censored and Dependent Data: The case of Linear Dynamics COLT 2021 Learning from Complementary Labels via Partial-Output Consistency Regularization IJCAI 2021 Learning from History for Byzantine Robust Optimization ICML 2021 Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks AAAI 2021 Learning from Noisy Labels with Complementary Loss Functions AAAI 2021 Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions NeurIPS 2021 Learning Generalized Gumbel-max Causal Mechanisms NeurIPS 2021 Learning Generalized Intersection Over Union for Dense Pixelwise Prediction ICML 2021 Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning AAAI 2021 Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction NeurIPS 2021 Learning Gradient Fields for Molecular Conformation Generation ICML 2021 Learning Graph Neural Networks with Approximate Gradient Descent AAAI 2021 Learning Groupwise Explanations for Black-Box Models IJCAI 2021 Learning Hard Optimization Problems: A Data Generation Perspective NeurIPS 2021 Learning Hard Retrieval Decoder Attention for Transformers EMNLP 2021 Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence NeurIPS 2021 Learning Hyperbolic Representations of Topological Features ICLR 2021 Learning Implicitly with Noisy Data in Linear Arithmetic IJCAI 2021 Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize ICLR 2021 Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021 Learning in Matrix Games can be Arbitrarily Complex COLT 2021