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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)

Understanding MLP-Mixer as a wide and sparse MLP ICML 2024 Understanding Model Selection for Learning in Strategic Environments NeurIPS 2024 Understanding Progressive Training Through the Framework of Randomized Coordinate Descent AISTATS 2024 Understanding prompt engineering may not require rethinking generalization ICLR 2024 Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation ICLR 2024 Understanding Representation of Deep Equilibrium Models from Neural Collapse Perspective NeurIPS 2024 Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data NeurIPS 2024 Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation ICML 2024 Understanding Stochastic Natural Gradient Variational Inference ICML 2024 Understanding Surprising Generalization Phenomena in Deep Learning AAAI 2024 Understanding the difficulty of solving Cauchy problems with PINNs L4DC 2024 Understanding the Effects of RLHF on LLM Generalisation and Diversity ICLR 2024 Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modeling NeurIPS 2024 Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective NeurIPS 2024 Understanding the Generalization Benefits of Late Learning Rate Decay AISTATS 2024 Understanding the Impact of Introducing Constraints at Inference Time on Generalization Error ICML 2024 Understanding the Learning Dynamics of Alignment with Human Feedback ICML 2024 Understanding the Role of Equivariance in Self-supervised Learning NeurIPS 2024 Understanding the Training Speedup from Sampling with Approximate Losses ICML 2024 Understanding Transformer Reasoning Capabilities via Graph Algorithms NeurIPS 2024 Understanding Transformers via N-Gram Statistics NeurIPS 2024 Understanding Unimodal Bias in Multimodal Deep Linear Networks ICML 2024 Understanding Visual Feature Reliance through the Lens of Complexity NeurIPS 2024 UniADS: Universal Architecture-Distiller Search for Distillation Gap AAAI 2024 UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling NeurIPS 2024