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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)

An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract) AAAI 2024 An Entropy-Based Model for Hierarchical Learning JMLR 2024 An Equivalence Between Static and Dynamic Regret Minimization NeurIPS 2024 A Neural Column Generation Approach to the Vehicle Routing Problem with Two-Dimensional Loading and Last-In-First-Out Constraints IJCAI 2024 A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data ICML 2024 A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models NeurIPS 2024 A Neural-Preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions ICML 2024 A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts NeurIPS 2024 A New Branch-and-Bound Pruning Framework for ℓ_0-Regularized Problems ICML 2024 A New Computationally Efficient Algorithm to solve Feature Selection for Functional Data Classification in High-dimensional Spaces ICML 2024 A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization ICML 2024 A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning NeurIPS 2024 A New, Physics-Informed Continuous-Time Reinforcement Learning Algorithm with Performance Guarantees JMLR 2024 A New Theoretical Perspective on Data Heterogeneity in Federated Optimization ICML 2024 An exactly solvable model for emergence and scaling laws in the multitask sparse parity problem NeurIPS 2024 An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics EACL 2024 An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations NeurIPS 2024 An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models ACL 2024 An Eye Opener Regarding Task-Based Text Gradient Saliency ACL 2024 Animal-Bench: Benchmarking Multimodal Video Models for Animal-centric Video Understanding NeurIPS 2024 An Improved Algorithm for Learning Drifting Discrete Distributions AISTATS 2024 An improved analysis of per-sample and per-update clipping in federated learning ICLR 2024 An Improved Empirical Fisher Approximation for Natural Gradient Descent NeurIPS 2024 An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks ICML 2024 An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models NeurIPS 2024