activity
20122026
most citedRobust Wasserstein Profile Inference and Applications to Machine Learning

270 citations · 352 across the 19 of their papers we have counts for

collaborators

23 papers

math.OC2026

Multiunit I.I.D. Prophet Inequalities via Extreme Value Asymptotics

Jieming Kong, Karthyek Murthy

We study the i.i.d. -selection prophet inequality problem, where a decision-maker sequentially observes independent nonnegative rewards and may accept at most of them wi…

math.OC2025

Going from a Representative Agent to Counterfactuals in Combinatorial Choice

Yanqiu Ruan, Karthyek Murthy, Karthik Natarajan

We study decision-making problems where data comprises points from a collection of binary polytopes, capturing aggregate information stemming from various combinatorial selection e…

math.OC2025

The Scaling Behaviors in Achieving High Reliability via Chance-Constrained Optimization

Anand Deo, Karthyek Murthy

We study the problem of resource provisioning under stringent reliability or service level requirements, which arise in applications such as power distribution, emergency response,…

q-fin.RM2023

Importance Sampling for Minimization of Tail Risks: A Tutorial

Anand Deo, Karthyek Murthy

This paper provides an introductory overview of how one may employ importance sampling effectively as a tool for solving stochastic optimization formulations incorporating tail ris…

stat.ML2022

A Nonparametric Approach with Marginals for Modeling Consumer Choice

Yanqiu Ruan, Xiaobo Li, Karthyek Murthy +1

Given data on the choices made by consumers for different offer sets, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior while bei…

q-fin.RM2022

Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk

Anand Deo, Karthyek Murthy, Tirtho Sarker

This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS). While IS ser…