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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

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52
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828
3,646
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2000
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Papers (including subtopics)

FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation ACL 2024 Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning NeurIPS 2024 FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization CVPR 2024 Frequency-mix Knowledge Distillation for Fake Speech Detection Interspeech 2024 Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations NeurIPS 2024 Frication noise features of Polish voiceless dental fricative and affricate produced by children with and without speech disorder Interspeech 2024 Friendly Sharpness-Aware Minimization CVPR 2024 FRoG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in LLMs EMNLP 2024 From Activation to Initialization: Scaling Insights for Optimizing Neural Fields CVPR 2024 From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions ICML 2024 From Biased to Unbiased Dynamics: An Infinitesimal Generator Approach NeurIPS 2024 From Bottom to Top: Extending the Potential of Parameter Efficient Fine-Tuning EMNLP 2024 From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs JMLR 2024 From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach AISTATS 2024 From Data Imputation to Data Cleaning — Automated Cleaning of Tabular Data Improves Downstream Predictive Performance AISTATS 2024 From Generalization Analysis to Optimization Designs for State Space Models ICML 2024 From Inverse Optimization to Feasibility to ERM ICML 2024 From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization NeurIPS 2024 From Past to Future: Rethinking Eligibility Traces AAAI 2024 From Pixels to Metal: AI-Empowered Numismatic Art IJCAI 2024 From Posterior Sampling to Meaningful Diversity in Image Restoration ICLR 2024 From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning NAACL 2024 From raw data to safety: Reducing conservatism by set expansion L4DC 2024 From Sparse to Soft Mixtures of Experts ICLR 2024 From Unstructured Data to In-Context Learning: Exploring What Tasks Can Be Learned and When NeurIPS 2024