It includes active learning, where the learners choose which examples to label and achieve better accuracy with less data compared to the classical approach of passively observing labeled data; It also includes the explainable learning, where the human doesn't merely tell the machine whether its predictions are correct, but provides reasons in a form that is meaningful to both parties.The workshop aims to bring together researchers and practitioners working on the broad areas of human in the loop learning, ranging from the interactive/active learning algorithm designs for real-world decision making systems (e.g., autonomous driving vehicles, robotic systems, etc.
Microsoft is proud to be a Gold sponsor of the 37th International Conference on Machine Learning (ICML), as well as Diamond sponsors at the 1st Women in Machine Learning Un-Workshop and Platinum sponsors of the 4th Queer in AI Workshop.We have over 50 papers accepted to the conference, and you can find details of our publications on the Accepted papers and Workshops tabs. If there are Zoom meetings you cannot access, please comment on the google doc beside these meetings' linksGiuseppe Di Benedetto (Oxford Univerisity); Vito Bellini (Amazon); Giovanni Zappella (Amazon)Yupeng Li (University of Toronto/Shenzhen Research Institute of Big Data)*; Mengjia Xia (Cornell University); Dacheng Wen (The University of Hong Kong); Cheng Zhang (Didi Chuxing); Meng Ai (Didi Chuxing); Qun (Tracy) Li (DiDi)Deep Active Learning: Unified and Principled Method for Query and TrainingChangjian Shui (Université Laval)*; Fan Zhou (Laval University); Christian Gagné (Université Laval); Boyu Wang (University of Western Ontario)GLAD: Localized Anomaly Detection via Human-in-the-Loop LearningMd Rakibul Islam (Washington State university)*; Shubhomoy Das (School of EECS, Washington State University, Pullman); Janardhan Rao Doppa (Washington State University); Sriraam Natarajan (UT Dallas)Human-Centric Efficiency Improvements in Image Annotation for Autonomous DrivingFrédéric Ratle (Samasource)*; Martine Bertrand (Samasource)Online Learning for Distributed and Personal Recommendations - a Fair approachMartin Tegnér (IKEA Retail, Oxford-Man Institute, University of Oxford)*Yet Another Study on Active Learning and Human Pose EstimationSinan Kaplan (Lappeenranta University of Technology)*; Lasse Lensu (Lappeenranta University of Technology)Yewen Pu (MIT)*; Marta Kryven (Massachusetts Institute of Technology); Kevin M Ellis (MIT); Joshua Tenenbaum (MIT); Armando Solar-Lezama (MIT)Preference learning along multiple criteria: A game-theoretic perspectiveKush Bhatia (UC Berkeley)*; Ashwin Pananjady (UC Berkeley); Peter Bartlett (); Anca Dragan (EECS Department, University of California, Berkeley); Martin Wainwright (UC Berkeley)Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable RepresentationsSarath Sreedharan (Arizona State University)*; Utkarsh Soni (Arizona State University); Mudit Verma (Arizona State University); Siddharth Srivastava (Arizona State University); Subbarao Kambhampati (Arizona State University)Interactive Segmentation of RGB-D Indoor Scenes using Deep LearningMaximilian Ruethlein (Friedrich-Alexander University Erlangen-Nuernberg)*; Franz Koeferl (Friedrich-Alexander University Erlangen-Nuernberg); Wolfgang Mehringer (Friedrich-Alexander University Erlangen-Nuernberg); Bjoern Eskofier (Friedrich-Alexander University Erlangen-Nuernberg)Sunayana Rane (MIT)*; Miguel Lázaro-Gredilla (Vicarious AI); Dileep George (Vicarious )Othman Benchekroun (Dathena)*; Adel Rahimi (Dathena); Qini Zhang (Dathena); Tetiana Kodliuk (Dathena)Quick Question: Interrupting Users for Microtasks with Reinforcement LearningBo-Jhang Ho (UCLA); Bharathan Balaji (Amazon)*; Mehmet Koseoglu (UCLA); Sandeep Singh Sandha (University of California - Los Angeles); Siyou Pei (UCLA); Mani Srivastava (UC Los Angeles)Adwait Sahasrabhojanee (USRA/NASA Ames); David Iverson (NASA Ames); shawn r wolfe (); Kevin Bradner (NASA Ames); Nikunj Oza (NASA Ames)*Active Learning Strategies to Reduce Anomaly Detection False Alarm RatesSCRAM: Simple Checks for Realtime Analysis of Model Training for Non-Expert ML ProgrammersEldon Schoop (University of California, Berkeley)*; Forrest Huang (University of California, Berkeley); Bjoern Hartmann (University of California, Berkeley)AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human VideosLaura M Smith (UC Berkeley)*; Nikita Dhawan (UC Berkeley); Marvin Zhang (UC Berkeley); Pieter Abbeel (UC Berkeley); Sergey Levine (UC Berkeley)Personalized Stress Detection with Self-supervised Learned FeaturesStefan Matthes (Fortiss GmbH); Zhiwei Han (fortiss GmbH)*; Tianming Qiu (fortiss GmbH); Bruno Michel (IBM Zurich Research Lab); Sören Klinger (fortiss GmbH); Hao Shen (fortiss GmbH); Yuanting Liu (fortiss GmbH); Bashar Altakrouri (IBM Deutschland GmbH)Yahav Bechavod (Hebrew University of Jerusalem)*; Steven Wu (University of Minnesota); Christopher Jung (University of Pennsylvania)Alejandro Peña (Universidad Autonoma de Madrid); Ignacio Serna (Universidad Autonoma de Madrid); Aythami Morales (Universidad Autonoma de Madrid)*; Julian Fierrez (Universidad Autonoma de Madrid)Explanation Augmented Feedback in Human-in-the-Loop Reinforcement LearningLin Guan (Arizona State University)*; Mudit Verma (Arizona State University); Subbarao Kambhampati (Arizona State University)Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)ClassificationAlberto Olmo (Arizona State University)*; Sailik Sengupta (Arizona State University); Subbarao Kambhampati (Arizona State University)Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopJonathan Chung (Amazon Web Services)*; Runfei Luo (Amazon Web Services); Xavier Raffin (Amazon Web Services); Scott Perry (Amazon Web Services)Faster Human-Machine Collaboration Bounding Box Annotation Framework Based on Active LearningMinzhe Liu (Nanjing University)*; LI DU (Nanjing University); Yuan Du (Nanjing University); Ruofan Guo (Nanjing University); Xiaoliang Chen (University of California, Irvine)Combining Human and Machine Intelligence to Assess Stroke Rehabilitation ExercisesMin Hun Lee (Carnegie Mellon University)*; Daniel Siewiorek (Carnegie Mellon University); Asim Smailagic (Carnegie Mellon University); Alexandre Bernardino (Instituto Superior Técnico); Sergi Bermudez (University of Madeira)Personalized Size Recommendations with Human in the LoopLeonidas Lefakis (Zalando)*; Evgenii Koriagin (Zalando SE); Julia Lasserre (Zalando Research); Reza Shirvany (Zalando SE)A Prospective Human-in-the-Loop Experiment using Reinforcement Learning with Planning for Optimizing Energy Demand ResponseLucas Spangher (U.C.
Microsoft is proud to be a Gold sponsor of the 37th International Conference on Machine Learning (ICML), as well as Diamond sponsors at the 1st Women in Machine Learning Un-Workshop and Platinum sponsors of the 4th Queer in AI Workshop.We have over 50 papers accepted to the conference, and you can find details of our publications on the Accepted papers and Workshops tabs. If there are Zoom meetings you cannot access, please comment on the google doc beside these meetings' linksGiuseppe Di Benedetto (Oxford Univerisity); Vito Bellini (Amazon); Giovanni Zappella (Amazon)Yupeng Li (University of Toronto/Shenzhen Research Institute of Big Data)*; Mengjia Xia (Cornell University); Dacheng Wen (The University of Hong Kong); Cheng Zhang (Didi Chuxing); Meng Ai (Didi Chuxing); Qun (Tracy) Li (DiDi)Deep Active Learning: Unified and Principled Method for Query and TrainingChangjian Shui (Université Laval)*; Fan Zhou (Laval University); Christian Gagné (Université Laval); Boyu Wang (University of Western Ontario)GLAD: Localized Anomaly Detection via Human-in-the-Loop LearningMd Rakibul Islam (Washington State university)*; Shubhomoy Das (School of EECS, Washington State University, Pullman); Janardhan Rao Doppa (Washington State University); Sriraam Natarajan (UT Dallas)Human-Centric Efficiency Improvements in Image Annotation for Autonomous DrivingFrédéric Ratle (Samasource)*; Martine Bertrand (Samasource)Online Learning for Distributed and Personal Recommendations - a Fair approachMartin Tegnér (IKEA Retail, Oxford-Man Institute, University of Oxford)*Yet Another Study on Active Learning and Human Pose EstimationSinan Kaplan (Lappeenranta University of Technology)*; Lasse Lensu (Lappeenranta University of Technology)Yewen Pu (MIT)*; Marta Kryven (Massachusetts Institute of Technology); Kevin M Ellis (MIT); Joshua Tenenbaum (MIT); Armando Solar-Lezama (MIT)Preference learning along multiple criteria: A game-theoretic perspectiveKush Bhatia (UC Berkeley)*; Ashwin Pananjady (UC Berkeley); Peter Bartlett (); Anca Dragan (EECS Department, University of California, Berkeley); Martin Wainwright (UC Berkeley)Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable RepresentationsSarath Sreedharan (Arizona State University)*; Utkarsh Soni (Arizona State University); Mudit Verma (Arizona State University); Siddharth Srivastava (Arizona State University); Subbarao Kambhampati (Arizona State University)Interactive Segmentation of RGB-D Indoor Scenes using Deep LearningMaximilian Ruethlein (Friedrich-Alexander University Erlangen-Nuernberg)*; Franz Koeferl (Friedrich-Alexander University Erlangen-Nuernberg); Wolfgang Mehringer (Friedrich-Alexander University Erlangen-Nuernberg); Bjoern Eskofier (Friedrich-Alexander University Erlangen-Nuernberg)Sunayana Rane (MIT)*; Miguel Lázaro-Gredilla (Vicarious AI); Dileep George (Vicarious )Othman Benchekroun (Dathena)*; Adel Rahimi (Dathena); Qini Zhang (Dathena); Tetiana Kodliuk (Dathena)Quick Question: Interrupting Users for Microtasks with Reinforcement LearningBo-Jhang Ho (UCLA); Bharathan Balaji (Amazon)*; Mehmet Koseoglu (UCLA); Sandeep Singh Sandha (University of California - Los Angeles); Siyou Pei (UCLA); Mani Srivastava (UC Los Angeles)Adwait Sahasrabhojanee (USRA/NASA Ames); David Iverson (NASA Ames); shawn r wolfe (); Kevin Bradner (NASA Ames); Nikunj Oza (NASA Ames)*Active Learning Strategies to Reduce Anomaly Detection False Alarm RatesSCRAM: Simple Checks for Realtime Analysis of Model Training for Non-Expert ML ProgrammersEldon Schoop (University of California, Berkeley)*; Forrest Huang (University of California, Berkeley); Bjoern Hartmann (University of California, Berkeley)AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human VideosLaura M Smith (UC Berkeley)*; Nikita Dhawan (UC Berkeley); Marvin Zhang (UC Berkeley); Pieter Abbeel (UC Berkeley); Sergey Levine (UC Berkeley)Personalized Stress Detection with Self-supervised Learned FeaturesStefan Matthes (Fortiss GmbH); Zhiwei Han (fortiss GmbH)*; Tianming Qiu (fortiss GmbH); Bruno Michel (IBM Zurich Research Lab); Sören Klinger (fortiss GmbH); Hao Shen (fortiss GmbH); Yuanting Liu (fortiss GmbH); Bashar Altakrouri (IBM Deutschland GmbH)Yahav Bechavod (Hebrew University of Jerusalem)*; Steven Wu (University of Minnesota); Christopher Jung (University of Pennsylvania)Alejandro Peña (Universidad Autonoma de Madrid); Ignacio Serna (Universidad Autonoma de Madrid); Aythami Morales (Universidad Autonoma de Madrid)*; Julian Fierrez (Universidad Autonoma de Madrid)Explanation Augmented Feedback in Human-in-the-Loop Reinforcement LearningLin Guan (Arizona State University)*; Mudit Verma (Arizona State University); Subbarao Kambhampati (Arizona State University)Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)ClassificationAlberto Olmo (Arizona State University)*; Sailik Sengupta (Arizona State University); Subbarao Kambhampati (Arizona State University)Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopJonathan Chung (Amazon Web Services)*; Runfei Luo (Amazon Web Services); Xavier Raffin (Amazon Web Services); Scott Perry (Amazon Web Services)Faster Human-Machine Collaboration Bounding Box Annotation Framework Based on Active LearningMinzhe Liu (Nanjing University)*; LI DU (Nanjing University); Yuan Du (Nanjing University); Ruofan Guo (Nanjing University); Xiaoliang Chen (University of California, Irvine)Combining Human and Machine Intelligence to Assess Stroke Rehabilitation ExercisesMin Hun Lee (Carnegie Mellon University)*; Daniel Siewiorek (Carnegie Mellon University); Asim Smailagic (Carnegie Mellon University); Alexandre Bernardino (Instituto Superior Técnico); Sergi Bermudez (University of Madeira)Personalized Size Recommendations with Human in the LoopLeonidas Lefakis (Zalando)*; Evgenii Koriagin (Zalando SE); Julia Lasserre (Zalando Research); Reza Shirvany (Zalando SE)A Prospective Human-in-the-Loop Experiment using Reinforcement Learning with Planning for Optimizing Energy Demand ResponseLucas Spangher (U.C.