3 citations · 9 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
Algorithmic Challenges in Ensuring Fairness at the Time of Decision
Jad Salem, Swati Gupta, Vijay Kamble
Algorithmic decision-making in societal contexts, such as retail pricing, loan administration, recommendations on online platforms, etc., can be framed as stochastic optimization u…
Maximal Objectives in the Multi-armed Bandit with Applications
Eren Ozbay, Vijay Kamble
In several applications of the stochastic multi-armed bandit problem, the traditional objective of maximizing the expected total reward can be inappropriate. In this paper, motivat…
Individual Fairness in Hindsight
Swati Gupta, Vijay Kamble
Since many critical decisions impacting human lives are increasingly being made by algorithms, it is important to ensure that the treatment of individuals under such algorithms is…
Exploration vs. Exploitation in Team Formation
Ramesh Johari, Vijay Kamble, Anilesh K. Krishnaswamy +1
An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches. This learning probl…
Matching while Learning
Ramesh Johari, Vijay Kamble, Yash Kanoria
We consider the problem faced by a service platform that needs to match limited supply with demand but also to learn the attributes of new users in order to match them better in th…
Sequential Relevance Maximization with Binary Feedback
Vijay Kamble, Nadia Fawaz, Fernando Silveira
Motivated by online settings where users can provide explicit feedback about the relevance of products that are sequentially presented to them, we look at the recommendation proces…