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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)

Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning ICML 2022 Distributionally-Aware Kernelized Bandit Problems for Risk Aversion ICML 2022 Distributionally Robust Fair Principal Components via Geodesic Descents ICLR 2022 Distributionally Robust Models with Parametric Likelihood Ratios ICLR 2022 Distributionally Robust Optimization with Data Geometry NeurIPS 2022 Distributionally Robust Recurrent Decoders with Random Network Distillation ACL 2022 Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks AISTATS 2022 Distribution-Based Measures of Surprise for Creative Language: Experiments with Humor and Metaphor EMNLP 2022 Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation COLING 2022 Distribution Compression in Near-Linear Time ICLR 2022 Distribution Consistent Neural Architecture Search CVPR 2022 Distribution-Informed Neural Networks for Domain Adaptation Regression NeurIPS 2022 Distribution Regression with Sliced Wasserstein Kernels ICML 2022 DIVA: Dataset Derivative of a Learning Task ICLR 2022 Diversified Adversarial Attacks based on Conjugate Gradient Method ICML 2022 Diversified Recommendations for Agents with Adaptive Preferences NeurIPS 2022 Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes AISTATS 2022 Diversity and Generalization in Neural Network Ensembles AISTATS 2022 Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction ACL 2022 Divisive Feature Normalization Improves Image Recognition Performance in AlexNet ICLR 2022 DNA: Domain Generalization with Diversified Neural Averaging ICML 2022 DN-DETR: Accelerate DETR Training by Introducing Query DeNoising CVPR 2022 DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning ICML 2022 Do Bayesian variational autoencoders know what they don’t know? UAI 2022 Document-Level Event Argument Extraction via Optimal Transport ACL 2022