8 papers
Organizing LLM Inference for Aggregate Predictions from Behavioral Data
Varun Gupta, Vijay Kamble
When using a large language model (LLM) to simulate a user population, how should we combine individual behavioral histories to predict the fraction of users who take a particular…
The Role of Causality in Algorithmic Recourse
Srikanth Avasarala, Varun Gupta, Shahin Jabbari +2
Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage appli…
Causal Multi-Task Demand Learning
Varun Gupta, Vijay Kamble
We study a canonical multi-task demand-learning problem motivated by retail pricing, where a firm seeks to estimate heterogeneous linear price-response functions across multiple de…
Fundamental limits for weighted empirical approximations of tilted distributions
Sarvesh Ravichandran Iyer, Himadri Mandal, Dhruman Gupta +5
Consider the task of generating samples from a tilted distribution of a random vector whose underlying distribution is unknown, but samples from it are available. This finds applic…
Collaborative Prediction: Tractable Information Aggregation via Agreement
Natalie Collina, Ira Globus-Harris, Surbhi Goel +3
We give efficient "collaboration protocols" through which two parties, who observe different features about the same instances, can interact to arrive at predictions that are more…
Tractable Agreement Protocols
Natalie Collina, Surbhi Goel, Varun Gupta +1
We present an efficient reduction that converts any machine learning algorithm into an interactive protocol, enabling collaboration with another party (e.g., a human) to achieve co…
Model Ensembling for Constrained Optimization
Ira Globus-Harris, Varun Gupta, Michael Kearns +1
There is a long history in machine learning of model ensembling, beginning with boosting and bagging and continuing to the present day. Much of this history has focused on combinin…
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability
Natalie Collina, Varun Gupta, Aaron Roth
We study a repeated contracting setting in which a Principal adaptively chooses amongst Agents at each of rounds. The Agents are non-myopic, and so a mechanism for the Prin…