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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)

Improving equilibrium propagation without weight symmetry through Jacobian homeostasis ICLR 2024 Improving Equivariant Model Training via Constraint Relaxation NeurIPS 2024 Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning COLING 2024 Improving Generalization and Convergence by Enhancing Implicit Regularization NeurIPS 2024 Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach NeurIPS 2024 Improving Generalization in Offline Reinforcement Learning via Adversarial Data Splitting ICML 2024 Improving Generalization of Speech Separation in Real-World Scenarios: Strategies in Simulation, Optimization, and Evaluation Interspeech 2024 Improving Generalization via Meta-Learning on Hard Samples CVPR 2024 Improving Gradient-Guided Nested Sampling for Posterior Inference ICML 2024 Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation ICML 2024 Improving Interpretation Faithfulness for Vision Transformers ICML 2024 Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint ACL 2024 Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes NeurIPS 2024 Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model NAACL 2024 Improving Minimum Bayes Risk Decoding with Multi-Prompt EMNLP 2024 Improving Neural Additive Models with Bayesian Principles ICML 2024 Improving Neural Logic Machines via Failure Reflection ICML 2024 Improving Neural Network Generalization on Data-Limited Regression with Doubly-Robust Boosting AAAI 2024 Improving Neural ODE Training with Temporal Adaptive Batch Normalization NeurIPS 2024 Improving Noise Robustness in Self-supervised Pre-trained Model for Speaker Verification Interspeech 2024 Improving Normalization With the James-Stein Estimator WACV 2024 Improving physics-informed neural networks with meta-learned optimization JMLR 2024 Improving Readability Assessment with Ordinal Log-Loss NAACL 2024 Improving Robustness for Joint Optimization of Camera Pose and Decomposed Low-Rank Tensorial Radiance Fields AAAI 2024 Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization ICML 2024