28 citations · 203 across the 35 of their papers we have counts for
37 papers
Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar
We consider the problem of computing bounds for causal queries on causal graphs with unobserved confounders and discrete valued observed variables, where identifiability does not h…
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Garud Iyengar, Henry Lam, Tianyu Wang
In data-driven optimization, the sample performance of the obtained decision typically incurs an optimistic bias against the true performance, a phenomenon commonly known as the Op…
Learning the Pareto Front Using Bootstrapped Observation Samples
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We consider Pareto front identification (PFI) for linear bandits (PFILin), i.e., the goal is to identify a set of arms with undominated mean reward vectors when the mean reward vec…
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget…
Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework
Garud Iyengar, Henry Lam, Tianyu Wang
Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving stochastic optimization problems that appear in operations manag…
Distributionally Robust End-to-End Portfolio Construction
Giorgio Costa, Garud N. Iyengar
We propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimi…