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Author Information
Goran Radanovic (Harvard University)
Rati Devidze (Max Planck Institute for Software Systems)
David Parkes (Harvard University)
Adish Singla (Max Planck Institute (MPI-SWS))

Adish Singla is a faculty member at the Max Planck Institute for Software Systems (MPI-SWS), Germany, where he has been leading the Machine Teaching Group since 2017. He conducts research in the area of Machine Teaching, with a particular focus on open-ended learning and problem-solving domains. In recent years, his research has centered around developing AI-driven educational technology for introductory programming environments. He has received several awards for his research, including an AAAI Outstanding Paper Honorable Mention Award (2022) and an ERC Starting Grant (2021). He also has extensive experience working in the industry and is a recipient of several industry awards, including a research grant from Microsoft Research Ph.D. Scholarship Programme (2018), Facebook Graduate Fellowship (2015), Microsoft Tech Transfer Award (2011), and Microsoft Gold Star Award (2010).
Related Events (a corresponding poster, oral, or spotlight)
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2019 Poster: Learning to Collaborate in Markov Decision Processes »
Wed. Jun 12th 01:30 -- 04:00 AM Room Pacific Ballroom #105
More from the Same Authors
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2020 : Contributed Talk: From Predictions to Decisions: Using Lookahead Regularization »
Nir Rosenfeld · Sai Srivatsa Ravindranath · David Parkes -
2023 : Iterative Machine Teaching for Black-box Markov Learners »
Chaoqi Wang · Sandra Zilles · Adish Singla · Yuxin Chen -
2023 Poster: Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning »
Matthias Gerstgrasser · David Parkes -
2021 : Poster spotlight presentations 2 »
Sebastian Tschiatschek · Adish Singla · Besmira Nushi -
2021 : Poster spotlight presentations 1 »
Sebastian Tschiatschek · Adish Singla · Besmira Nushi -
2021 Workshop: Human-AI Collaboration in Sequential Decision-Making »
Besmira Nushi · Adish Singla · Sebastian Tschiatschek -
2021 Poster: Learning Representations by Humans, for Humans »
Sophie Hilgard · Nir Rosenfeld · Mahzarin Banaji · Jack Cao · David Parkes -
2021 Spotlight: Learning Representations by Humans, for Humans »
Sophie Hilgard · Nir Rosenfeld · Mahzarin Banaji · Jack Cao · David Parkes -
2020 Poster: The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation »
Zhe Feng · David Parkes · Haifeng Xu -
2020 Poster: Adaptive Reward-Poisoning Attacks against Reinforcement Learning »
Xuezhou Zhang · Yuzhe Ma · Adish Singla · Jerry Zhu -
2020 Poster: Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning »
Amin Rakhsha · Goran Radanovic · Rati Devidze · Jerry Zhu · Adish Singla -
2019 Poster: Fairness without Harm: Decoupled Classifiers with Preference Guarantees »
Berk Ustun · Yang Liu · David Parkes -
2019 Oral: Fairness without Harm: Decoupled Classifiers with Preference Guarantees »
Berk Ustun · Yang Liu · David Parkes -
2019 Poster: Efficient learning of smooth probability functions from Bernoulli tests with guarantees »
Paul Rolland · Ali Kavis · Alexander Niklaus Immer · Adish Singla · Volkan Cevher -
2019 Oral: Efficient learning of smooth probability functions from Bernoulli tests with guarantees »
Paul Rolland · Ali Kavis · Alexander Niklaus Immer · Adish Singla · Volkan Cevher -
2019 Poster: Optimal Auctions through Deep Learning »
Paul Duetting · Zhe Feng · Harikrishna Narasimhan · David Parkes · Sai Srivatsa Ravindranath -
2019 Oral: Optimal Auctions through Deep Learning »
Paul Duetting · Zhe Feng · Harikrishna Narasimhan · David Parkes · Sai Srivatsa Ravindranath