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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)

Unconstrained Online Learning with Unbounded Losses ICML 2023 Uncovering Meanings of Embeddings via Partial Orthogonality NeurIPS 2023 Uncovering Neural Scaling Laws in Molecular Representation Learning NeurIPS 2023 Understanding and Bridging the Modality Gap for Speech Translation ACL 2023 Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective ICML 2023 Understanding and Improving Ensemble Adversarial Defense NeurIPS 2023 Understanding Backdoor Attacks through the Adaptability Hypothesis ICML 2023 Understanding Contrastive Learning via Distributionally Robust Optimization NeurIPS 2023 Understanding DDPM Latent Codes Through Optimal Transport ICLR 2023 Understanding Deep Generative Models With Generalized Empirical Likelihoods CVPR 2023 Understanding Edge-of-Stability Training Dynamics with a Minimalist Example ICLR 2023 Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes NeurIPS 2023 Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias ICML 2023 Understanding Hessian Alignment for Domain Generalization ICCV 2023 Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization NeurIPS 2023 Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing ICML 2023 Understanding Influence Functions and Datamodels via Harmonic Analysis ICLR 2023 Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks NeurIPS 2023 Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles ICLR 2023 Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers NeurIPS 2023 Understanding Oversquashing in GNNs through the Lens of Effective Resistance ICML 2023 Understanding Plasticity in Neural Networks ICML 2023 Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL NeurIPS 2023 Understanding Representation Learnability of Nonlinear Self-Supervised Learning AAAI 2023 Understanding Self-attention Mechanism via Dynamical System Perspective ICCV 2023