Search Results for author:
Found 16 papers, 2 papers with code
Beyond Discretization: Learning the Optimal Solution Path
We propose an alternative approach that parameterizes the solution path with a set of basis functions and solves a \emph{single} stochastic optimization problem to learn the entire solution path.
Decision-Focused Learning with Directional Gradients
We propose a novel family of decision-aware surrogate losses, called Perturbation Gradient (PG) losses, for the predict-then-optimize framework.
Balanced Off-Policy Evaluation for Personalized Pricing
We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand.
ETA Prediction with Graph Neural Networks in Google Maps
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.
Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization
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
Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce.
Data-Pooling in Stochastic Optimization
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.
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs
SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG).
Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques
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).
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base
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.
A Comparison of Monte Carlo Tree Search and Mathematical Optimization for Large Scale Dynamic Resource Allocation
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.
