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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)

Exact Soft Analytical Side-Channel Attacks using Tractable Circuits ICML 2024 Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization NeurIPS 2024 Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets COLING 2024 Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks ACL 2024 ExCP: Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking ICML 2024 Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification JMLR 2024 Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration AAAI 2024 Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation NeurIPS 2024 Expand-and-Cluster: Parameter Recovery of Neural Networks ICML 2024 Expanding the Reach of Social Choice Theory IJCAI 2024 Expected Probabilistic Hierarchies NeurIPS 2024 Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport NeurIPS 2024 Experience as Source for Anticipation and Planning: Experiential Policy Learning for Target-driven Recommendation Dialogues EMNLP 2024 Experts Don’t Cheat: Learning What You Don’t Know By Predicting Pairs ICML 2024 Explaining Generalization Power of a DNN Using Interactive Concepts AAAI 2024 Explaining Language Model Predictions with High-Impact Concepts EACL 2024 Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions CVPR 2024 Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers AISTATS 2024 Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization NeurIPS 2024 Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning (Abstract Reprint) AAAI 2024 Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion NeurIPS 2024 Exploiting Data Geometry in Machine Learning AAAI 2024 Exploiting Human-AI Dependence for Learning to Defer ICML 2024 Exploiting Positional Bias for Query-Agnostic Generative Content in Search ACL 2024 Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration NeurIPS 2024