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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)

Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models MLHC 2021 Uncertainty-Driven Loss for Single Image Super-Resolution NeurIPS 2021 Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection CVPR 2021 Uncertainty-Guided Model Generalization to Unseen Domains CVPR 2021 Uncertainty in minimum cost multicuts for image and motion segmentation UAI 2021 Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks AAAI 2021 Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance EMNLP 2021 Uncertainty Principles of Encoding GANs ICML 2021 Uncertainty Quantification and Deep Ensembles NeurIPS 2021 Uncertainty Quantification in CNN Through the Bootstrap of Convex Neural Networks AAAI 2021 Uncertainty quantification using martingales for misspecified Gaussian processes ALT 2021 Uncertainty Reduction for Model Adaptation in Semantic Segmentation CVPR 2021 Uncertainty Sets for Image Classifiers using Conformal Prediction ICLR 2021 Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning ICML 2021 Uncovering Latent Biases in Text: Method and Application to Peer Review AAAI 2021 Understanding and Improving Early Stopping for Learning with Noisy Labels NeurIPS 2021 Understanding Bandits with Graph Feedback NeurIPS 2021 Understanding Decoupled and Early Weight Decay AAAI 2021 Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization NeurIPS 2021 Understanding Failures of Deep Networks via Robust Feature Extraction CVPR 2021 Understanding Gradient Clipping In Incremental Gradient Methods AISTATS 2021 Understanding How Encoder-Decoder Architectures Attend NeurIPS 2021 Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron ACML 2021 Understanding Over-parameterization in Generative Adversarial Networks ICLR 2021 Understanding Partial Multi-Label Learning via Mutual Information NeurIPS 2021