Arpit Agarwal PhD Student Department of Computer & Information Science University of Pennsylvania Link to: CV (last updated: November 2019)

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

• Stochastic Dueling Bandits with Adversarial Corruption
  Arpit Agarwal, Shivani Agarwal, Prathamesh Patil (alphabetical order) .
  ALT 2021 (To Appear).


• Choice Bandits
  Arpit Agarwal, Nicholas Johnson, Shivani Agarwal.
  NeurIPS 2020.


• Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions
  Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, and Prathamesh Patil (alphabetical order) .
  ICML 2020. [paper]


• Peer Prediction with Heterogeneous Users.
  Arpit Agarwal, Debmalya Mandal, David C. Parkes , and Nisarg Shah (alphabetical order) .
  ACM Transactions on Economics and Computation (TEAC). Forthcoming. [paper]
  Supercedes the EC-17 paper below.


• Stochastic Submodular Cover with Limited Adaptivity.
  Arpit Agarwal, Sepehr Assadi, and Sanjeev Khanna (alphabetical order) .
  SODA 2019. [paper] [arXiv version]
  


• Accelerated Spectral Ranking.
  Arpit Agarwal, Prathamesh Patil, and Shivani Agarwal.
  ICML 2018. [paper]
  


• 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 (alphabetical order) .
  COLT 2017. [paper]
  


• Peer Prediction with Heterogeneous Users.
  Arpit Agarwal, Debmalya Mandal, David C. Parkes , and Nisarg Shah (alphabetical order) .
  EC 2017. [paper]
  


• 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


• On Consistent Surrogate Risk Minimization and Property Elicitation.
  Arpit Agarwal and Shivani Agarwal.
  COLT 2015. [paper]
  
  
• 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

• Stochastic Submodular Cover with Limited Adaptivity.
  Symposium on Discrete Algorithms, San Diego, January 2019 .
  Google Research Mountain View, July 2019.
  


• Accelerated Spectral Ranking.
  International Conference on Machine Learning, Stockholm, July 2018 .
  Google Research New York, September 2018.
  


• 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.
  


• On Consistent Surrogate Risk Minimization and Property Elicitation.
  ACM IKDD Conference on Data Science (CoDS), Pune, March 2016.
  


• Connections between Calibrated Surrogates in Supervised Learning and Property Elicitation in Probability Forecasting.
  Harvard EconCS group meeting, December 2015.
  


• GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare.
  International Conference on Machine Learning (ICML), Beijing, June 2014.
  


• 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

• I organized a reading group on Optimization in the summer of 2016.
• In my free time I enjoy running or debating on Indian politics and history.