Search Results for author: Vishal Gupta

Found 15 papers, 2 papers with code

Learning Best-in-Class Policies for the Predict-then-Optimize Framework

no code implementations5 Feb 2024 Michael Huang, Vishal Gupta

This is the first such result in misspecified settings and we provide numerical evidence confirming our PG losses substantively outperform existing proposals when the underlying model is misspecified and the noise is not centrally symmetric.

Balanced Off-Policy Evaluation for Personalized Pricing

2 code implementations24 Feb 2023 Adam N. Elmachtoub, Vishal Gupta, Yunfan Zhao

We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand.

Off-policy evaluation

ETA Prediction with Graph Neural Networks in Google Maps

no code implementations25 Aug 2021 Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, Peter W. Battaglia, Vishal Gupta, Ang Li, Zhongwen Xu, Alvaro Sanchez-Gonzalez, Yujia Li, Petar Veličković

Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike.

Graph Representation Learning

Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization

no code implementations26 Jul 2021 Vishal Gupta, Michael Huang, Paat Rusmevichientong

Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization. Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance.

Balance Regularized Neural Network Models for Causal Effect Estimation

no code implementations23 Nov 2020 Mehrdad Farajtabar, Andrew Lee, Yuanjian Feng, Vishal Gupta, Peter Dolan, Harish Chandran, Martin Szummer

Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce.

Representation Learning

Data-Pooling in Stochastic Optimization

1 code implementation1 Jun 2019 Vishal Gupta, Nathan Kallus

This intuition further suggests that data-pooling offers the most benefits when there are many problems, each of which has a small amount of relevant data.

Management Stochastic Optimization

Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques

no code implementations WS 2018 Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black

As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian).

Question Answering Sentence

Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base

no code implementations WS 2018 Vishal Gupta, Manoj Chinnakotla, Manish Shrivastava

Our network is trained only on English questions provided in this dataset and noisy Hindi translations of these questions and can answer English-Hindi CM questions effectively without the need of translation into English.

Automatic Speech Recognition (ASR) Information Retrieval +8

A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation

no code implementations21 May 2014 Dimitris Bertsimas, J. Daniel Griffith, Vishal Gupta, Mykel J. Kochenderfer, Velibor V. Mišić, Robert Moss

In this paper, we adapt both MCTS and MO to a problem inspired by tactical wildfire and management and undertake an extensive computational study comparing the two methods on large scale instances in terms of both the state and the action spaces.

Management Stochastic Optimization

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