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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)

An Adaptive Implicit Hitting Set Algorithm for MAP and MPE Inference PGM 2024 An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression ICLR 2024 ANAH: Analytical Annotation of Hallucinations in Large Language Models ACL 2024 An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters JMLR 2024 An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization JMLR 2024 Analysing and Validating Language Complexity Metrics Across South American Indigenous Languages ACL 2024 Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting NeurIPS 2024 Analysing The Impact of Sequence Composition on Language Model Pre-Training ACL 2024 Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts ICML 2024 Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression UAI 2024 Analysis of Corrected Graph Convolutions NeurIPS 2024 Analysis of Differentially Private Synthetic Data: A Measurement Error Approach AAAI 2024 Analysis of Kernel Mirror Prox for Measure Optimization AISTATS 2024 Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity ICLR 2024 Analysis of Using Sigmoid Loss for Contrastive Learning AISTATS 2024 Analytically deriving Partial Information Decomposition for affine systems of stable and convolution-closed distributions NeurIPS 2024 Analyzing and Improving Optimal-Transport-based Adversarial Networks ICLR 2024 Analyzing and Improving the Training Dynamics of Diffusion Models CVPR 2024 Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps ICLR 2024 Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework AutoML 2024 Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System AAAI 2024 Analyzing Interpretability of Summarization Model with Eye-gaze Information COLING 2024 Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training NeurIPS 2024 An Analysis of Elo Rating Systems via Markov Chains NeurIPS 2024 An Analysis of Linear Time Series Forecasting Models ICML 2024