“‘Active Learning’ Tag”,2019-12-15 (AI scaling, AI safety, Bayes, decision theory)⁠:

Bibliography for tag reinforcement-learning/exploration/active-learning, most recent first: 2 related tags, 109 annotations, & 25 links (parent).

• See Also
• Links
  • “Probing the Decision Boundaries of In-Context Learning in Large Language Models”, Zhao et al 2024
  • “Beyond Model Collapse: Scaling Up With Synthesized Data Requires Reinforcement”, Feng et al 2024
  • “Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge”, Strieth-Kalthoff et al 2024
  • “Sparse Universal Transformer”, Tan et al 2023
  • “Skill-It! A Data-Driven Skills Framework for Understanding and Training Language Models”, Chen et al 2023
  • “AlpaGasus: Training A Better Alpaca With Fewer Data”, Chen et al 2023
  • “Instruction Mining: High-Quality Instruction Data Selection for Large Language Models”, Cao et al 2023
  • “No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-Based Language Models”, Kaddour et al 2023
  • “Estimating Label Quality and Errors in Semantic Segmentation Data via Any Model”, Lad & Mueller2023
  • “Self Expanding Neural Networks”, Mitchell et al 2023
  • “DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining”, Xie et al 2023
  • “Chatting With GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing”, Liu et al 2023
  • “TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Eldan & Li2023
  • “Q2d: Turning Questions into Dialogs to Teach Models How to Search”, Bitton et al 2023
  • “Segment Anything”, Kirillov et al 2023
  • “Scaling Expert Language Models With Unsupervised Domain Discovery”, Gururangan et al 2023
  • “Modern Bayesian Experimental Design”, Rainforth et al 2023
  • “Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities”, Kirsch & Gal2023
  • “Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Bronstein et al 2022
  • “CDCD: Continuous Diffusion for Categorical Data”, Dieleman et al 2022
  • “Query by Committee Made Real”, Gilad-Bachrach et al 2022
  • “Weakly Supervised Structured Output Learning for Semantic Segmentation”, Vezhnevets et al 2022
  • “The Power of Ensembles for Active Learning in Image Classification”, Beluch et al 2022
  • “Multi-Class Active Learning for Image Classification”, Joshi et al 2022
  • “Multi-Class Active Learning by Uncertainty Sampling With Diversity Maximization”, Yang et al 2022
  • “The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Kocsis et al 2022
  • “Detecting Label Errors in Token Classification Data”, Wang & Mueller2022
  • “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Mindermann et al 2022
  • “Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Zhang et al 2022
  • “Multi-Task Self-Training for Learning General Representations”, Ghiasi et al 2021
  • “Predictive Coding: a Theoretical and Experimental Review”, Millidge et al 2021
  • “Dataset Distillation With Infinitely Wide Convolutional Networks”, Nguyen et al 2021
  • “Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning”, Kirsch et al 2021
  • “Adapting the Function Approximation Architecture in Online Reinforcement Learning”, Martin & Modayil2021
  • “B-Pref: Benchmarking Preference-Based Reinforcement Learning”, Lee et al 2021
  • “Fully General Online Imitation Learning”, Cohen et al 2021
  • “When Do Curricula Work?”, Wu et al 2020
  • “Dataset Meta-Learning from Kernel Ridge-Regression”, Nguyen et al 2020
  • “Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Swayamdipta et al 2020
  • “BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits”, Tiwari et al 2020
  • “Exploring Bayesian Optimization: Breaking Bayesian Optimization into Small, Sizeable Chunks”, Agnihotri & Batra2020
  • “Small-GAN: Speeding Up GAN Training Using Core-Sets”, Sinha et al 2019
  • “A Deep Active Learning System for Species Identification and Counting in Camera Trap Images”, Norouzzadeh et al 2019
  • “On Warm-Starting Neural Network Training”, Ash & Adams2019
  • “Accelerating Deep Learning by Focusing on the Biggest Losers”, Jiang et al 2019
  • “Data Valuation Using Reinforcement Learning”, Yoon et al 2019
  • “BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”, Kirsch et al 2019
  • “BADGE: Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds”, Ash et al 2019
  • “Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Ho et al 2019
  • “Learning Loss for Active Learning”, Yoo & Kweon2019
  • “A Recipe for Training Neural Networks”, Karpathy2019
  • “ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang et al 2019
  • “End-To-End Robotic Reinforcement Learning without Reward Engineering”, Singh et al 2019
  • “Data Shapley: Equitable Valuation of Data for Machine Learning”, Ghorbani & Zou2019
  • “Learning from Dialogue After Deployment: Feed Yourself, Chatbot!”, Hancock et al 2019
  • “The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Kuznetsova et al 2018
  • “Computational Mechanisms of Curiosity and Goal-Directed Exploration”, Schwartenbeck et al 2018
  • “Conditional Neural Processes”, Garnelo et al 2018
  • “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Pang et al 2018
  • “More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Calandra et al 2018
  • “Fingerprint Policy Optimization for Robust Reinforcement Learning”, Paul et al 2018
  • “AutoAugment: Learning Augmentation Policies from Data”, Cubuk et al 2018
  • “Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization”, McLeod et al 2018
  • “Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Hadash et al 2018
  • “Active Learning With Partial Feedback”, Hu et al 2018
  • “Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Zhou et al 2018
  • “Less Is More: Sampling Chemical Space With Active Learning”, Smith et al 2018
  • “The Eighty Five Percent Rule for Optimal Learning”, Wilson et al 2018
  • “ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, Kim & Choi2018
  • “Learning a Generative Model for Validity in Complex Discrete Structures”, Janz et al 2017
  • “Learning by Asking Questions”, Misra et al 2017
  • “BlockDrop: Dynamic Inference Paths in Residual Networks”, Wu et al 2017
  • “Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, Yang et al 2017
  • “Classification With Costly Features Using Deep Reinforcement Learning”, Janisch et al 2017
  • “Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-Sensitive Learning”, Depeweg et al 2017
  • “Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, Shim et al 2017
  • “Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, Jayaraman & Grauman2017
  • “Active Learning for Convolutional Neural Networks: A Core-Set Approach”, Sener & Savarese2017
  • “Interpretable Active Learning”, Phillips et al 2017
  • “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun et al 2017
  • “A Tutorial on Thompson Sampling”, Russo et al 2017
  • “Learning to Learn from Noisy Web Videos”, Yeung et al 2017
  • “Teaching Machines to Describe Images via Natural Language Feedback”, Ling & Fidler2017
  • “Ask the Right Questions: Active Question Reformulation With Reinforcement Learning”, Buck et al 2017
  • “BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography”, Wilber et al 2017
  • “PBO: Preferential Bayesian Optimization”, Gonzalez et al 2017
  • “OHEM: Training Region-Based Object Detectors With Online Hard Example Mining”, Shrivastava et al 2016
  • “The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition”, Krause et al 2015
  • “LSUN: Construction of a Large-Scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu et al 2015
  • “Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, Gal & Ghahramani2015
  • “Just Sort It! A Simple and Effective Approach to Active Preference Learning”, Maystre & Grossglauser2015
  • “Learning With Intelligent Teacher: Similarity Control and Knowledge Transfer”, Vapnik & Izmailov2015
  • “Minimax Analysis of Active Learning”, Hanneke & Yang2014
  • “Algorithmic and Human Teaching of Sequential Decision Tasks”, Cakmak & Lopes2012
  • “Bayesian Active Learning for Classification and Preference Learning”, Houlsby et al 2011
  • “Rates of Convergence in Active Learning”, Hanneke2011
  • “The True Sample Complexity of Active Learning”, Balcan et al 2010
  • “Active Testing for Face Detection and Localization”, Sznitman & Jedynak2010
  • “The Wisdom of the Few: a Collaborative Filtering Approach Based on Expert Opinions from the Web”, Amatriain et al 2009
  • “Learning and Example Selection for Object and Pattern Detection”, Sung1995
  • “Information-Based Objective Functions for Active Data Selection”, MacKay1992
  • “Active Learning Literature Survey”
  • “Brief Summary of the Panel Discussion at DL Workshop @ICML 2015”
  • “Active Learning”
  • “Aurora’s Approach to Development”
  • “Active Learning for High Dimensional Inputs Using Bayesian Convolutional Neural Networks”
  • “AI-Guided Robots Are Ready to Sort Your Recyclables”
  • “When Self-Driving Cars Can’t Help Themselves, Who Takes the Wheel?”
  • “How a Feel-Good AI Story Went Wrong in Flint: A Machine-Learning Model Showed Promising Results, but City Officials and Their Engineering Contractor Abandoned It.”
  • Sort By Magic
    • dataset-curation
    • augmentation-policy
    • active-bayesian-learning
    • batch-active-learning
    • information-optimization
  • Wikipedia
• Miscellaneous
• Bibliography