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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
352
828
3,646
5,854
2000
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Papers (including subtopics)

Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework AAAI 2023 Beyond Average Return in Markov Decision Processes NeurIPS 2023 Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Scaffolded Interactive Activities AAAI 2023 Beyond calibration: estimating the grouping loss of modern neural networks ICLR 2023 Beyond Confidence: Reliable Models Should Also Consider Atypicality NeurIPS 2023 Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift NeurIPS 2023 Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence NeurIPS 2023 Beyond Graph Convolutional Network: An Interpretable Regularizer-Centered Optimization Framework AAAI 2023 Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD ICLR 2023 Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization ICML 2023 Beyond mAP: Towards Better Evaluation of Instance Segmentation CVPR 2023 Beyond MLE: Convex Learning for Text Generation NeurIPS 2023 Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends NeurIPS 2023 Beyond NaN: Resiliency of Optimization Layers in the Face of Infeasibility AAAI 2023 Beyond Normal: On the Evaluation of Mutual Information Estimators NeurIPS 2023 Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time NeurIPS 2023 Beyond Parallel Pancakes: Quasi-Polynomial Time Guarantees for Non-Spherical Gaussian Mixtures COLT 2023 Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions AISTATS 2023 Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models ACL 2023 Beyond probability partitions: Calibrating neural networks with semantic aware grouping NeurIPS 2023 Beyond Spectral Gap: The Role of the Topology in Decentralized Learning JMLR 2023 Beyond Strict Competition: Approximate Convergence of Multi-agent Q-Learning Dynamics IJCAI 2023 Beyond the Edge of Stability via Two-step Gradient Updates ICML 2023 Beyond the Golden Ratio for Variational Inequality Algorithms JMLR 2023 Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels ICML 2023