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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)

Understanding approximate and unrolled dictionary learning for pattern recovery ICLR 2022 Understanding Benign Overfitting in Gradient-Based Meta Learning NeurIPS 2022 Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy ICML 2022 Understanding Contrastive Learning Requires Incorporating Inductive Biases ICML 2022 Understanding Dataset Difficulty with V-Usable Information ICML 2022 Understanding Deep Contrastive Learning via Coordinate-wise Optimization NeurIPS 2022 Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration NeurIPS 2022 Understanding Dynamic Tactile Sensing for Liquid Property Estimation RSS 2022 Understanding Gradient Descent on the Edge of Stability in Deep Learning ICML 2022 Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond ICML 2022 Understanding Hyperdimensional Computing for Parallel Single-Pass Learning NeurIPS 2022 Understanding Instance-Level Impact of Fairness Constraints ICML 2022 Understanding Intrinsic Robustness Using Label Uncertainty ICLR 2022 Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective ICLR 2022 Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective NeurIPS 2022 Understanding over-squashing and bottlenecks on graphs via curvature ICLR 2022 Understanding Programmatic Weak Supervision via Source-aware Influence Function NeurIPS 2022 Understanding Riemannian Acceleration via a Proximal Extragradient Framework COLT 2022 Understanding Robust Generalization in Learning Regular Languages ICML 2022 Understanding Robust Overfitting of Adversarial Training and Beyond ICML 2022 Understanding Square Loss in Training Overparametrized Neural Network Classifiers NeurIPS 2022 Understanding Stochastic Optimization Behavior at the Layer Update Level (Student Abstract) AAAI 2022 Understanding the Eluder Dimension NeurIPS 2022 Understanding the Evolution of Linear Regions in Deep Reinforcement Learning NeurIPS 2022 Understanding the Failure of Batch Normalization for Transformers in NLP NeurIPS 2022