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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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828
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2000
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Papers (including subtopics)

Performance Bounds for Policy-Based Average Reward Reinforcement Learning Algorithms NeurIPS 2023 Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex NeurIPS 2023 Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks EACL 2023 Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources NeurIPS 2023 Performative Prediction with Neural Networks AISTATS 2023 Performative Reinforcement Learning ICML 2023 PermutoSDF: Fast Multi-View Reconstruction With Implicit Surfaces Using Permutohedral Lattices CVPR 2023 Persistent Homology through Image Segmentation (Student Abstract) AAAI 2023 Perturbation Analysis of Neural Collapse ICML 2023 PerturbScore: Connecting Discrete and Continuous Perturbations in NLP EMNLP 2023 Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes ICLR 2023 PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems NeurIPS 2023 Petals: Collaborative Inference and Fine-tuning of Large Models ACL 2023 {PF}$^2$ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization AISTATS 2023 PFNs4BO: In-Context Learning for Bayesian Optimization ICML 2023 PGrad: Learning Principal Gradients For Domain Generalization ICLR 2023 Phase-aware Adversarial Defense for Improving Adversarial Robustness ICML 2023 Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width NeurIPS 2023 Phase-Informed Bayesian Ensemble Models Improve Performance of COVID-19 Forecasts AAAI 2023 PhaseMP: Robust 3D Pose Estimation via Phase-conditioned Human Motion Prior ICCV 2023 Phase-shifted adversarial training UAI 2023 Phase transition for detecting a small community in a large network ICLR 2023 PhysDiff: Physics-Guided Human Motion Diffusion Model ICCV 2023 Physics-Driven ML-Based Modelling for Correcting Inverse Estimation NeurIPS 2023 Physics-Guided Active Learning of Environmental Flow Fields L4DC 2023