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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)

QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning EMNLP 2024 QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers NeurIPS 2024 QLABGrad: A Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning AAAI 2024 QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models ICLR 2024 Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent ICML 2024 QTIP: Quantization with Trellises and Incoherence Processing NeurIPS 2024 QT-ViT: Improving Linear Attention in ViT with Quadratic Taylor Expansion NeurIPS 2024 Quadratic models for understanding catapult dynamics of neural networks ICLR 2024 Quality and Quantity of Machine Translation References for Automatic Metrics COLING 2024 Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model ACL 2024 Quality-Diversity Algorithms Can Provably Be Helpful for Optimization IJCAI 2024 Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design ICML 2024 Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner NeurIPS 2024 Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing NeurIPS 2024 Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage EACL 2024 Quantifying Contamination in Evaluating Code Generation Capabilities of Language Models ACL 2024 Quantifying Task Priority for Multi-Task Optimization CVPR 2024 Quantifying the Gain in Weak-to-Strong Generalization NeurIPS 2024 Quantifying the Hyperparameter Sensitivity of Neural Networks for Character-level Sequence-to-Sequence Tasks EACL 2024 Quantifying the Role of Textual Predictability in Automatic Speech Recognition Interspeech 2024 Quantifying Unintended Memorization in BEST-RQ ASR Encoders Interspeech 2024 Quantitative Convergences of Lie Group Momentum Optimizers NeurIPS 2024 Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models AISTATS 2024 Quantum algorithm for large-scale market equilibrium computation NeurIPS 2024 Quantum Algorithms and Lower Bounds for Finite-Sum Optimization ICML 2024