Arpit Agarwal
Arpit Agarwal
PhD Student
Department of Computer & Information Science
University of Pennsylvania
Link to: CV (last updated: November 2019)
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About Me
I am a PhD student in the Department of Computer & Information Science, at University of Pennsylvania, working with Prof. Shivani Agarwal. Before coming to Penn, I was a PhD student in the Department of Computer Science & Automation at Indian Institute of Science. Before that I completed my masters in computer science and engineering at Indian Institute of Science.
I spent time visiting Prof. David Parkes at Harvard in Fall 2015, working on the problem of multi-task peer prediction.
My research interests broadly lie at the intersection of Machine Learning, Economics and Computation, and Theoretical Computer Science.
Recently, I have been interested at problems on the interface of ranking, choice modeling and multi-armed bandits.
Publications
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Peer Prediction with Heterogeneous Users.
Arpit Agarwal, Debmalya Mandal, David C. Parkes , and Nisarg Shah.
ACM Transactions on Economics and Computation (TEAC). Forthcoming. [paper]
Supercedes the EC-17 paper below.
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Stochastic Submodular Cover with Limited Adaptivity.
Arpit Agarwal, Sepehr Assadi,
and Sanjeev Khanna .
SODA 2019. [paper] [arXiv version]
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Accelerated Spectral Ranking.
Arpit Agarwal, Prathamesh Patil, and Shivani Agarwal.
ICML 2018. [paper]
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Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons.
Arpit Agarwal, Shivani Agarwal, Sepehr Assadi,
and Sanjeev Khanna .
COLT 2017. [paper]
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Peer Prediction with Heterogeneous Users.
Arpit Agarwal, Debmalya Mandal, David C. Parkes , and Nisarg Shah.
EC 2017. [paper]
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Informed Truthfulness in Multi-Task Peer Prediction.
Victor Shnayder, Arpit Agarwal, Rafael Frongillo, and David C. Parkes .
EC 2016. [paper] [arXiv version]
A short version appeared in HCOMP Workshop on Mathematical Foundations of Human Computation, 2016
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On Consistent Surrogate Risk Minimization and Property Elicitation.
Arpit Agarwal and Shivani Agarwal.
COLT 2015. [paper]
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GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare.
Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan and Shivani Agarwal.
ICML 2014. [paper]
Recent Talks
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Stochastic Submodular Cover with Limited Adaptivity.
Symposium on Discrete Algorithms, San Diego, January 2019 .
Google Research Mountain View, July 2019.
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Accelerated Spectral Ranking.
International Conference on Machine Learning, Stockholm, July 2018 .
Google Research New York, September 2018.
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Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons.
Conference on Learning Theory, Amsterdam, July 2017
Microsoft Research Bangalore, July 2017.
Indian Institute of Science, Bangalore, July 2017.
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On Consistent Surrogate Risk Minimization and Property Elicitation.
ACM IKDD Conference on Data Science (CoDS), Pune, March 2016.
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Connections between Calibrated Surrogates in Supervised Learning and Property Elicitation in Probability Forecasting.
Harvard EconCS group meeting, December 2015.
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GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare.
International Conference on Machine Learning (ICML), Beijing, June 2014.
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Randomization at work: An introduction to Randomized Algorithms.
CSA Undergraduate Summer School, June 2013.
Teaching
- Teaching Assistant for CIS 520 Machine Learning.
Department of Computer Science and Information, University of Pennsylvania.
- Teaching Assistant for CIS 620 Advanced Topics in Machine Learning.
Department of Computer Science and Information, University of Pennsylvania.
- Teaching Assistant for E0 270 Machine Learning.
Department of Computer Science and Automation, Indian Institute of Science.
Misc