3 citations · 9 across the 13 of their papers we have counts for
15 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…
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…
Price Competition Under Platform-Mediated Search: A Consider-Then-Choose Framework
Siddhartha Banerjee, Chamsi Hssaine, Vijay Kamble
We study the problem of predicting price equilibria on e-commerce platforms where sellers compete across multiple attributes (e.g., price, average rating, delivery speed). In these…
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…
Revenue Management on an On-Demand Service Platform
Vijay Kamble
I consider the optimal hourly (or per-unit-time in general) pricing problem faced by a freelance worker (or a service provider) on an on-demand service platform. Service requests a…
Iterative Local Voting for Collective Decision-making in Continuous Spaces
Nikhil Garg, Vijay Kamble, Ashish Goel +2
Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These…
An Approximate Dynamic Programming Approach to Adversarial Online Learning
Vijay Kamble, Patrick Loiseau, Jean Walrand
We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted rep…
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…
The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms
Vijay Kamble, Nihar Shah, David Marn +2
A major challenge in obtaining evaluations of products or services on e-commerce platforms is eliciting informative responses in the absence of verifiability. This paper proposes t…
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…
Monotonic Preference Aggregation Mechanisms for Purchasing a Shareable Resource
Vijay Kamble, Jean Walrand
Situations where a group of agents come together to jointly buy a resource that they individually cannot afford to buy are commonly observed in markets. For example in the US marke…
Approximately Optimal Scheduling of an M/G/1 Queue with Heavy Tails
Vijay Kamble, Jean Walrand
Distributions with a heavy tail are difficult to estimate. If the design of a scheduling policy is sensitive to the details of heavy tail distributions of the service times, an app…