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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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828
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2000
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Papers (including subtopics)

Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis NeurIPS 2024 Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy NeurIPS 2024 Training-free Multi-objective Diffusion Model for 3D Molecule Generation ICLR 2024 Training Implicit Generative Models via an Invariant Statistical Loss AISTATS 2024 Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit JMLR 2024 Training Unbiased Diffusion Models From Biased Dataset ICLR 2024 Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability NeurIPS 2024 TRAM: Bridging Trust Regions and Sharpness Aware Minimization ICLR 2024 Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection CVPR 2024 Transductive Active Learning: Theory and Applications NeurIPS 2024 Transductive conformal inference with adaptive scores AISTATS 2024 Transductive Learning is Compact NeurIPS 2024 Transductive Zero-Shot and Few-Shot CLIP CVPR 2024 Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness NeurIPS 2024 Transfer learning for tensor Gaussian graphical models JMLR 2024 Transfer Q-star : Principled Decoding for LLM Alignment NeurIPS 2024 Transferring Learning Trajectories of Neural Networks ICLR 2024 Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization NeurIPS 2024 Transformers are Minimax Optimal Nonparametric In-Context Learners NeurIPS 2024 Transformers Can Represent n-gram Language Models NAACL 2024 Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models ICML 2024 Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context ICML 2024 Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape ICML 2024 Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression NeurIPS 2024 Transformers need glasses! Information over-squashing in language tasks NeurIPS 2024