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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
352
828
3,646
5,854
2000
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Papers (including subtopics)

Preserving Pre-trained Representation Space: On Effectiveness of Prefix-tuning for Large Multi-modal Models EMNLP 2024 PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks ICLR 2024 Pre-trained Gaussian Processes for Bayesian Optimization JMLR 2024 Pretrained Optimization Model for Zero-Shot Black Box Optimization NeurIPS 2024 Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context NeurIPS 2024 Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method EMNLP 2024 Pretraining with Random Noise for Fast and Robust Learning without Weight Transport NeurIPS 2024 Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization NeurIPS 2024 Preventing Model Collapse in Gaussian Process Latent Variable Models ICML 2024 Pricing with Contextual Elasticity and Heteroscedastic Valuation ICML 2024 PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation EACL 2024 PRIME: Prioritizing Interpretability in Failure Mode Extraction ICLR 2024 Principal eigenstate classical shadows COLT 2024 Principled Architecture-aware Scaling of Hyperparameters ICLR 2024 Principled Bayesian Optimization in Collaboration with Human Experts NeurIPS 2024 Principled Gradient-Based MCMC for Conditional Sampling of Text ICML 2024 Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF ICML 2024 Principled Preferential Bayesian Optimization ICML 2024 Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors NeurIPS 2024 Principles for AI-Assisted Social Influence and Their Application to Social Mediation EMNLP 2024 Principles from Clinical Research for NLP Model Generalization NAACL 2024 PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses ICML 2024 Prior-dependent analysis of posterior sampling reinforcement learning with function approximation AISTATS 2024 Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy NeurIPS 2024 Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis ICML 2024