Arpit Agarwal Postdoctoral Research Fellow Data Science Institute Columbia University Email: arpit.agarwal [at] columbia.edu Link to: CV (last updated: May 2022)

About Me

I am currently a postdoctoral fellow at the Data Science Institute at Columbia University mentored by Prof. Shipra Agrawal, Prof. Yash Kanoria and Prof. Tim Roughgarden. My research interests lie at the intersection of Machine Learning, Economics and Computation, and Theoretical Computer Science. Specifically, I am interested in the human decision-making process and its interaction with machine learning algorithms-- how humans choose items based on recommendations, how can we learn from their behaviour and recommend better items, and how do these recommendations influence their tastes in the long-term?

Prior to joining Columbia, I completed my PhD from the Department of Computer & Information Science at University of Pennsylvania, under the guidance of Prof. Shivani Agarwal. Before that I completed my masters in computer science and engineering at Indian Institute of Science. I also spent a semester visiting Prof. David Parkes at Harvard, working on the problem of multi-task peer prediction.

Publications

• Diversified Recommendations for Agents with Adaptive Preferences
  Arpit Agarwal, William Brown (alphabetical order) .
  NeurIPS 2022 (To Appear).


• Sublinear Algorithms for Hierarchical Clustering
  Arpit Agarwal, Sanjeev Khanna, Huan Li, Prathamesh Patil (alphabetical order) .
  NeurIPS 2022 (To Appear). [arXiv preprint]


• An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
  Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan (alphabetical order) .
  NeurIPS 2022 (To Appear).


• A Sharp Memory-Regret Trade-Off for Multi-Pass Streaming Bandits
  Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil (alphabetical order) .
  COLT 2022. [paper]


• Batched Dueling Bandits
  Arpit Agarwal, Rohan Ghuge, Viswanath Nagarajan (alphabetical order) .
  ICML 2022. Long presentation (top 2% of submissions). [arXiv preprint].


• PAC Top-$k$ Identification under SST in Limited Rounds
  Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil (alphabetical order) .
  AISTATS 2022. [paper]


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


• Choice Bandits
  Arpit Agarwal, Nicholas Johnson, Shivani Agarwal.
  NeurIPS 2020. [paper] [supplemental]


• 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) 2020. [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.