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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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'25

Papers (including subtopics)

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Power ICLR 2025 Generalization Analysis for Controllable Learning ICML 2025 Generalization Analysis for Deep Contrastive Representation Learning AAAI 2025 Generalization Analysis for Supervised Contrastive Representation Learning under Non-IID Settings ICML 2025 Generalization and Distributed Learning of GFlowNets ICLR 2025 Generalization and Robustness of the Tilted Empirical Risk ICML 2025 Generalization Bounds and Model Complexity for Kolmogorov–Arnold Networks ICLR 2025 Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging ICLR 2025 Generalization Bounds for Dependent Data using Online-to-Batch Conversion. AISTATS 2025 Generalization bounds for mixing processes via delayed online-to-PAC conversions ALT 2025 Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks ICML 2025 Generalization error bound for denoising score matching under relaxed manifold assumption COLT 2025 Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs ICLR 2025 Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors ICLR 2025 Generalization in Federated Learning: A Conditional Mutual Information Framework ICML 2025 Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis ICLR 2025 Generalization Lower Bounds for GD and SGD in Smooth Stochastic Convex Optimization AISTATS 2025 Generalization of Graph Neural Networks Is Robust to Model Mismatch AAAI 2025 Generalization of noisy SGD in unbounded non-convex settings ICML 2025 Generalization Performance of Ensemble Clustering: From Theory to Algorithm ICML 2025 Generalization-Preserved Learning: Closing the Backdoor to Catastrophic Forgetting in Continual Deepfake Detection ICCV 2025 Generalization through variance: how noise shapes inductive biases in diffusion models ICLR 2025 Generalization v.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data ICLR 2025 Generalized additive models via direct optimization of regularized decision stump forests ICML 2025 Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning AAAI 2025