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Keyword

sample efficiency

Description: Sample efficiency is the ability of a learning method to achieve strong performance using relatively few training examples or interactions with an environment.
OpenAlex ID: keywords/sample-efficiency
Prioritized Experience Replay
2015 · Schaul, Tom, Quan, John, et al. · arXiv (Cornell University) · 3,003
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Soft Actor-Critic Algorithms and Applications
2018 · Haarnoja, Tuomas, Zhou, Aurick, et al. · arXiv (Cornell University) · 1,993
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A method for unbiased selective sampling, using ranked sets
1952 · GA McIntyre · Australian Journal of Agricultural Research · 1,392
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
2018 · Anusha Nagabandi, Gregory Kahn, et al. · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 900
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
2020 · Levine, Sergey, Kumar, Aviral, et al. · arXiv (Cornell University) · 824
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
2018 · Aravind Rajeswaran, Vikash Kumar, et al. · 818
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ConViT: improving vision transformers with soft convolutional inductive biases*
2022 · Stéphane d’Ascoli, Hugo Touvron, et al. · Journal of Statistical Mechanics Theory and Experiment · 781
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Reinforcement Learning, Fast and Slow
2019 · Matthew Botvinick, Sam Ritter, et al. · Trends in Cognitive Sciences · 666
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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
2016 · Finn, Chelsea, Levine, Sergey, et al. · arXiv (Cornell University) · 638
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Proximal Policy Optimization in StarCraft
2019 · Yuefan Liu · 615
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End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks
2019 · Richard Cheng, Gábor Orosz, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 604
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
2021 · Yu Tian, Guansong Pang, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 589
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Open-ended interview questions and saturation
2018 · Susan C. Weller, Ben Vickers, et al. · PLoS ONE · 561
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Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
2018 · Chua, Kurtland, Calandra, Roberto, et al. · arXiv (Cornell University) · 525
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BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
2019 · Balandat, Maximilian, Karrer, Brian, et al. · arXiv (Cornell University) · 515
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Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task
2020 · Ferreira, Bruno Carlos Luís, Gonçalo Oliveira, Hugo, et al. · arXiv (Cornell University) · 503
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Sample Efficient Actor-Critic with Experience Replay
2016 · Wang, Ziyu, Bapst, Victor, et al. · arXiv (Cornell University) · 494
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CURL: Contrastive Unsupervised Representations for Reinforcement Learning
2020 · Srinivas, Aravind, Laskin, Michael, et al. · arXiv (Cornell University) · 489
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Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
2017 · Wu, Yuhuai, Mansimov, Elman, et al. · arXiv (Cornell University) · 477
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Data-Efficient Hierarchical Reinforcement Learning
2018 · Nachum, Ofir, Gu, Shixiang, et al. · arXiv (Cornell University) · 446
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LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
2023 · Chan Hee Song, Brian M. Sadler, et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 432
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Learning to Walk Via Deep Reinforcement Learning
2019 · Tuomas Haarnoja, Sehoon Ha, et al. · 430
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Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning
2020 · Jin Wang, Jia Hu, et al. · IEEE Transactions on Parallel and Distributed Systems · 416
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Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
2019 · Rakelly, Kate, Zhou, Aurick, et al. · arXiv (Cornell University) · 371
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Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
2018 · Nagabandi, Anusha, Clavera, Ignasi, et al. · arXiv (Cornell University) · 370
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Works count: 8,099
Citation count: 84,500
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