“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”, Bronstein Et Al 2022

“Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula”⁠, Eli Bronstein, Sirish Srinivasan, Supratik Paul, Aman Sinha, Matthew O’Kelly, Payam Nikdel, Shimon Whiteson2022-12-02 (model-free RL⁠, robotics⁠, AI safety; similar)

“CDCD: Continuous Diffusion for Categorical Data”, Dieleman Et Al 2022

“CDCD: Continuous diffusion for categorical data”⁠, Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond2022-11-28 (diffusion model/discrete; similar)

“Query by Committee Made Real”, Gilad-Bachrach Et Al 2022

“Query by Committee Made Real”⁠, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby2022-11-11 (similar)

“Multi-class Active Learning for Image Classification”, Joshi Et Al 2022

“Multi-class active learning for image classification”⁠, Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolopoulos2022-11-10 (similar)

“The Power of Ensembles for Active Learning in Image Classification”, Beluch Et Al 2022

“The Power of Ensembles for Active Learning in Image Classification”⁠, William H. Beluch, Tim Genewein, Andreas Nürnberger, Jan M. Köhler2022-11-10 (CNN; similar)

“Weakly Supervised Structured Output Learning for Semantic Segmentation”, Vezhnevets Et Al 2022

“Weakly supervised structured output learning for semantic segmentation”⁠, Alexander Vezhnevets, Vittorio Ferrari, Joachim M. Buhmann2022-11-10 (similar)

“Multi-Class Active Learning by Uncertainty Sampling With Diversity Maximization”, Yang Et Al 2022

“Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization”⁠, Yi Yang, Zhigang Ma, Feiping Nie, Xiaojun Chang, Alexander G. Hauptmann2022-11-09 (similar)

“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Kocsis Et Al 2022

“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”⁠, Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner, Laura Leal-Taixé, Ismail Elezi2022-10-11 (CNN⁠, MLP NN⁠, knowledge distillation; similar)

“Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning”, Sorscher Et Al 2022

“Beyond neural scaling laws: beating power law scaling via data pruning”⁠, Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, Ari S. Morcos2022-06-29 (AI scaling; backlinks; similar; bibliography)

“RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt”, Mindermann Et Al 2022

“RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt”⁠, Sören Mindermann, Jan Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen2022-06-14 (CNN⁠, MLP NN⁠, Transformer NN⁠, meta-learning; similar; bibliography)

“Bamboo: Building Mega-Scale Vision Dataset Continually With Human-Machine Synergy”, Zhang Et Al 2022

“Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy”⁠, Yuanhan Zhang, Qinghong Sun, Yichun Zhou, Zexin He, Zhenfei Yin, Kun Wang, Lu Sheng, Yu Qiao, Jing Shao2022-03-15 (ML dataset⁠, CLIP; similar)

“Multi-Task Self-Training for Learning General Representations”, Ghiasi Et Al 2021

“Multi-Task Self-Training for Learning General Representations”⁠, Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin2021-08-25 (knowledge distillation⁠, RL scaling; similar)

“Dataset Distillation With Infinitely Wide Convolutional Networks”, Nguyen Et Al 2021

“Dataset Distillation with Infinitely Wide Convolutional Networks”⁠, Timothy Nguyen, Roman Novak, Lechao Xiao, Jaehoon Lee2021-07-27 (CNN⁠, knowledge distillation⁠, meta-learning; similar)

“Predictive Coding: a Theoretical and Experimental Review”, Millidge Et Al 2021

“Predictive Coding: a Theoretical and Experimental Review”⁠, Beren Millidge, Anil Seth, Christopher L. Buckley2021-07-27 (neural net⁠, neuroscience; similar)

“Adapting the Function Approximation Architecture in Online Reinforcement Learning”, Martin & Modayil2021

“Adapting the Function Approximation Architecture in Online Reinforcement Learning”⁠, John D. Martin, Joseph Modayil2021-06-17 (similar)

“B-Pref: Benchmarking Preference-Based Reinforcement Learning”, Lee Et Al 2021

“B-Pref: Benchmarking Preference-Based Reinforcement Learning”⁠, Kimin Lee, Laura Smith, Anca Dragan, Pieter Abbeel2021-06-08 (preference learning; similar)

“Fully General Online Imitation Learning”, Cohen Et Al 2021

“Fully General Online Imitation Learning”⁠, Michael K. Cohen, Marcus Hutter, Neel Nanda2021-02-17 (similar)

“When Do Curricula Work?”, Wu Et Al 2020

“When Do Curricula Work?”⁠, Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur2020-12-05 (similar)

“Dataset Meta-Learning from Kernel Ridge-Regression”, Nguyen Et Al 2020

“Dataset Meta-Learning from Kernel Ridge-Regression”⁠, Timothy Nguyen, Zhourong Chen, Jaehoon Lee2020-10-30 (knowledge distillation⁠, meta-learning; similar)

“Dataset Cartography: Mapping and Diagnosing Datasets With Training Dynamics”, Swayamdipta Et Al 2020

“Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics”⁠, Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, Yejin Choi2020-09-22 (neural net/adversarial; similar)

“Exploring Bayesian Optimization: Breaking Bayesian Optimization into Small, Sizeable Chunks”, Agnihotri & Batra2020

“Exploring Bayesian Optimization: Breaking Bayesian Optimization into small, sizeable chunks”⁠, Apoorv Agnihotri, Nipun Batra2020-05-05 (data visualization⁠, Bayes; similar)

“Small-GAN: Speeding Up GAN Training Using Core-sets”, Sinha Et Al 2019

“Small-GAN: Speeding Up GAN Training Using Core-sets”⁠, Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena2019-10-29 (GAN; backlinks; similar)

“A Deep Active Learning System for Species Identification and Counting in Camera Trap Images”, Norouzzadeh Et Al 2019

“A deep active learning system for species identification and counting in camera trap images”⁠, Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune2019-10-22 (similar)

“On Warm-Starting Neural Network Training”, Ash & Adams2019

“On Warm-Starting Neural Network Training”⁠, Jordan T. Ash, Ryan P. Adams2019-10-18 (knowledge distillation; similar)

“Data Valuation Using Reinforcement Learning”, Yoon Et Al 2019

“Data Valuation using Reinforcement Learning”⁠, Jinsung Yoon, Sercan O. Arik, Tomas Pfister2019-09-25 (meta-learning; similar)

“BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”, Kirsch Et Al 2019

“BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning”⁠, Andreas Kirsch, Joost van Amersfoort, Yarin Gal2019-06-19 (similar)

“Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”, Ho Et Al 2019

“Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules”⁠, Daniel Ho, Eric Liang, Ion Stoica, Pieter Abbeel, Xi Chen2019-05-14 (similar; bibliography)

“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”, Wang Et Al 2019

“ProductNet: a Collection of High-Quality Datasets for Product Representation Learning”⁠, Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu2019-04-18 (ML dataset; similar)

“End-to-End Robotic Reinforcement Learning without Reward Engineering”, Singh Et Al 2019

“End-to-End Robotic Reinforcement Learning without Reward Engineering”⁠, Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine2019-04-16 (robotics; similar)

“Data Shapley: Equitable Valuation of Data for Machine Learning”, Ghorbani & Zou2019

“Data Shapley: Equitable Valuation of Data for Machine Learning”⁠, Amirata Ghorbani, James Zou2019-04-05 (similar)

“Learning from Dialogue After Deployment: Feed Yourself, Chatbot!”, Hancock Et Al 2019

“Learning from Dialogue after Deployment: Feed Yourself, Chatbot!”⁠, Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazaré, Jason Weston2019-01-16 (similar)

“Dataset Distillation”, Wang Et Al 2018

“Dataset Distillation”⁠, Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros2018-11-27 (knowledge distillation; backlinks; similar)

“The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale”, Kuznetsova Et Al 2018

“The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale”⁠, Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali2018-11-02 (ML dataset; similar)

“Computational Mechanisms of Curiosity and Goal-directed Exploration”, Schwartenbeck Et Al 2018

“Computational mechanisms of curiosity and goal-directed exploration”⁠, Philipp Schwartenbeck, Johannes Passecker, Tobias U. Hauser, Thomas H. B. FitzGerald, Martin Kronbichler2018-09-07 (neuroscience; similar)

“Conditional Neural Processes”, Garnelo Et Al 2018

“Conditional Neural Processes”⁠, Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh2018-07-04 (similar)

“Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”, Pang Et Al 2018

“Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning”⁠, Kunkun Pang, Mingzhi Dong, Yang Wu, Timothy Hospedales2018-06-12 (neural net⁠, meta-learning; backlinks; similar)

“More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch”, Calandra Et Al 2018

“More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch”⁠, Roberto Calandra, Andrew Owens, Dinesh Jayaraman, Justin Lin, Wenzhen Yuan, Jitendra Malik, Edward H. Adelson2018-05-28 (CNN⁠, robotics; similar)

“Fingerprint Policy Optimisation for Robust Reinforcement Learning”, Paul Et Al 2018

“Fingerprint Policy Optimisation for Robust Reinforcement Learning”⁠, Supratik Paul, Michael A. Osborne, Shimon Whiteson2018-05-27 (meta-learning; similar)

“AutoAugment: Learning Augmentation Policies from Data”, Cubuk Et Al 2018

“AutoAugment: Learning Augmentation Policies from Data”⁠, Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le2018-05-24 (meta-learning; similar; bibliography)

“Optimization, Fast and Slow: Optimally Switching between Local and Bayesian Optimization”, McLeod Et Al 2018

“Optimization, fast and slow: optimally switching between local and Bayesian optimization”⁠, Mark McLeod, Michael A. Osborne, Stephen J. Roberts2018-05-22 (similar)

“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks With Existing Applications”, Hadash Et Al 2018

“Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications”⁠, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kou, Alon Jacovi2018-04-24 (model-based RL; similar)

“Active Learning With Partial Feedback”, Hu Et Al 2018

“Active Learning with Partial Feedback”⁠, Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan2018-02-21 (similar; bibliography)

“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”, Zhou Et Al 2018

“Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts”⁠, Zongwei Zhou, Jae Y. Shin, Suryakanth R. Gurudu, Michael B. Gotway, Jianming Liang2018-02-03 (CNN; similar)

“Less Is More: Sampling Chemical Space With Active Learning”, Smith Et Al 2018

“Less is more: sampling chemical space with active learning”⁠, Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg2018-01-28 (biology; similar)

“The Eighty Five Percent Rule for Optimal Learning”, Wilson Et Al 2018

“The Eighty Five Percent Rule for Optimal Learning”⁠, Robert C. Wilson, Amitai Shenhav, Mark Straccia, Jonathan D. Cohen2018-01-27 (neuroscience; similar)

“ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”, Kim & Choi2018

“ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks”⁠, Tae-Hoon Kim, Jonghyun Choi2018-01-03 (meta-learning; backlinks; similar)

“Learning a Generative Model for Validity in Complex Discrete Structures”, Janz Et Al 2017

“Learning a Generative Model for Validity in Complex Discrete Structures”⁠, David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato2017-12-05 (similar)

“Learning by Asking Questions”, Misra Et Al 2017

“Learning by Asking Questions”⁠, Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten2017-12-04 (similar)

“BlockDrop: Dynamic Inference Paths in Residual Networks”, Wu Et Al 2017

“BlockDrop: Dynamic Inference Paths in Residual Networks”⁠, Zuxuan Wu, Tushar Nagarajan, Abhishek Kumar, Steven Rennie, Larry S. Davis, Kristen Grauman, Rogerio Feris2017-11-22 (CNN⁠, meta-learning; backlinks; similar)

“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”, Yang Et Al 2017

“Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent”⁠, Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela2017-11-21 (RNN⁠, text game; similar)

“Classification With Costly Features Using Deep Reinforcement Learning”, Janisch Et Al 2017

“Classification with Costly Features using Deep Reinforcement Learning”⁠, Jaromír Janisch, Tomáš Pevný, Viliam Lisý2017-11-20 (model-free RL; backlinks; similar)

“Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning”, Depeweg Et Al 2017

“Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning”⁠, Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft2017-10-19 (similar)

“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”, Shim Et Al 2017

“Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification”⁠, Hajin Shim, Sung Ju Hwang, Eunho Yang2017-09-18 (RNN; backlinks; similar)

“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”, Jayaraman & Grauman2017

“Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks”⁠, Dinesh Jayaraman, Kristen Grauman2017-09-01 (RNN; similar)

“Active Learning for Convolutional Neural Networks: A Core-Set Approach”, Sener & Savarese2017

“Active Learning for Convolutional Neural Networks: A Core-Set Approach”⁠, Ozan Sener, Silvio Savarese2017-08-01 (CNN; backlinks; similar)

“Interpretable Active Learning”, Phillips Et Al 2017

“Interpretable Active Learning”⁠, Richard L. Phillips, Kyu Hyun Chang, Sorelle A. Friedler2017-07-31 (similar)

“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun Et Al 2017

“Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”⁠, Chen Sun, Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta2017-07-10 (ML dataset⁠, AI scaling; similar)

“A Tutorial on Thompson Sampling”, Russo Et Al 2017

“A Tutorial on Thompson Sampling”⁠, Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, Zheng Wen2017-07-07 (Bayes; similar)

“Learning to Learn from Noisy Web Videos”, Yeung Et Al 2017

“Learning to Learn from Noisy Web Videos”⁠, Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei2017-06-09 (AI scaling⁠, video analysis; backlinks; similar)

“Teaching Machines to Describe Images via Natural Language Feedback”, Ling & Fidler2017

“Teaching Machines to Describe Images via Natural Language Feedback”⁠, Huan Ling, Sanja Fidler2017-06-01 (model-free RL; backlinks; similar)

“OHEM: Training Region-based Object Detectors With Online Hard Example Mining”, Shrivastava Et Al 2016

“OHEM: Training Region-based Object Detectors with Online Hard Example Mining”⁠, Abhinav Shrivastava, Abhinav Gupta, Ross Girshick2016-04-12 (CNN; similar)

“LSUN: Construction of a Large-scale Image Dataset Using Deep Learning With Humans in the Loop”, Yu Et Al 2015

“LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop”⁠, Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao2015-06-10 (ML dataset⁠, CNN⁠, AI scaling; backlinks; similar)

“Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, Gal & Ghahramani2015

“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”⁠, Yarin Gal, Zoubin Ghahramani2015-06-06 (neural net⁠, Bayes; similar)

“Just Sort It! A Simple and Effective Approach to Active Preference Learning”, Maystre & Grossglauser2015

“Just Sort It! A Simple and Effective Approach to Active Preference Learning”⁠, Lucas Maystre, Matthias Grossglauser2015-02-19 (preference learning⁠, order statistics/comparison; backlinks; similar)

“Minimax Analysis of Active Learning”, Hanneke & Yang2014

“Minimax Analysis of Active Learning”⁠, Steve Hanneke, Liu Yang2014-10-03 (similar)

“Bayesian Active Learning for Classification and Preference Learning”, Houlsby Et Al 2011

“Bayesian Active Learning for Classification and Preference Learning”⁠, Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, Máté Lengyel2011-12-24 (preference learning⁠, Bayes⁠, order statistics/comparison; backlinks; similar)

“Rates of Convergence in Active Learning”, Hanneke2011

“Rates of convergence in active learning”⁠, Steve Hanneke2011-02 (similar; bibliography)

“The True Sample Complexity of Active Learning”, Balcan Et Al 2010

“The true sample complexity of active learning”⁠, Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan2010-04-29 (similar; bibliography)

“Active Testing for Face Detection and Localization”, Sznitman & Jedynak2010

“Active Testing for Face Detection and Localization”⁠, Raphael Sznitman, Bruno Jedynak2010-03-27

“The Wisdom of the Few: a Collaborative Filtering Approach Based on Expert Opinions from the Web”, Amatriain Et Al 2009

“The wisdom of the few: a collaborative filtering approach based on expert opinions from the web”⁠, Xavier Amatriain, Neal Lathia, Josep M. Pujol, Haewoon Kwak, Nuria Oliver2009-07-19 (similar)

“Learning and Example Selection for Object and Pattern Detection”, Sung1995

“Learning and Example Selection for Object and Pattern Detection”⁠, Kah Kay Sung1995-12-15 (similar)

“Active Learning Literature Survey”

“Brief Summary of the Panel Discussion at DL Workshop @ICML 2015”

“Active Learning for High Dimensional Inputs Using Bayesian Convolutional Neural Networks”

“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.”

Wikipedia

1. Active learning (machine learning)

2. Conformal prediction

3. Factorial experiment

4. Human-in-the-loop

5. Optimal experimental design

6. Sequential analysis