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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)

Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning ICML 2023 Promises and Pitfalls of Threshold-based Auto-labeling NeurIPS 2023 Prompting is not a substitute for probability measurements in large language models EMNLP 2023 Proper Losses for Discrete Generative Models ICML 2023 Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds COLT 2023 Proper Scoring Rules for Survival Analysis ICML 2023 Properties of the Mallows Model Depending on the Number of Alternatives: A Warning for an Experimentalist ICML 2023 Proportionality Guarantees in Elections with Interdependent Issues IJCAI 2023 Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization NeurIPS 2023 Protecting Global Properties of Datasets with Distribution Privacy Mechanisms AISTATS 2023 Protein Design with Guided Discrete Diffusion NeurIPS 2023 ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design NeurIPS 2023 ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics NeurIPS 2023 ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers NeurIPS 2023 Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval NeurIPS 2023 Prototype-oriented unsupervised anomaly detection for multivariate time series ICML 2023 Prototypical Model with Information-Theoretic Loss Functions for Generalized Zero-Shot Learning ACML 2023 Prototypical Partial Optimal Transport for Universal Domain Adaptation AAAI 2023 Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs NeurIPS 2023 Provable Benefit of Mixup for Finding Optimal Decision Boundaries ICML 2023 Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond NeurIPS 2023 Provable benefits of score matching NeurIPS 2023 Provable convergence guarantees for black-box variational inference NeurIPS 2023 Provable Data Subset Selection For Efficient Neural Networks Training ICML 2023 Provable Defense Against Geometric Transformations ICLR 2023