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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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52
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828
3,646
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2000
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Papers (including subtopics)

Norma: Towards Practical Network Load Testing NSDI 2023 Norm-based Generalization Bounds for Sparse Neural Networks NeurIPS 2023 Norm of Word Embedding Encodes Information Gain EMNLP 2023 Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts NeurIPS 2023 Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization ICML 2023 Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits ICML 2023 No time to waste: practical statistical contact tracing with few low-bit messages AISTATS 2023 No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models NeurIPS 2023 Novel Ordering-Based Approaches for Causal Structure Learning in the Presence of Unobserved Variables AAAI 2023 NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation ICML 2023 NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning ICLR 2023 NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning ICML 2023 NTK-SAP: Improving neural network pruning by aligning training dynamics ICLR 2023 NVTC: Nonlinear Vector Transform Coding CVPR 2023 Nyström M-Hilbert-Schmidt independence criterion UAI 2023 Nyström Method for Accurate and Scalable Implicit Differentiation AISTATS 2023 Oblivious near-optimal sampling for multidimensional signals with Fourier constraints AISTATS 2023 OCD: Learning to Overfit with Conditional Diffusion Models ICML 2023 ODE-RSSM: Learning Stochastic Recurrent State Space Model from Irregularly Sampled Data AAAI 2023 ODS: Test-Time Adaptation in the Presence of Open-World Data Shift ICML 2023 Offline Meta Reinforcement Learning with In-Distribution Online Adaptation ICML 2023 Offline Model-Based Reinforcement Learning for Tokamak Control L4DC 2023 Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient ICLR 2023 Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling ICML 2023 OKRidge: Scalable Optimal k-Sparse Ridge Regression NeurIPS 2023