7 citations · 15 across the 6 of their papers we have counts for
7 papers
Robust Active Learning for Few-Shot Example Selection in Text-to-SQL
Arash Pourhabib
Domain-specific text-to-SQL systems ground a large language model by retrieving annotated few-shot examples, and each example needs expert-written SQL. We treat the choice of which…
A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models
Rui Tuo, Shiyuan He, Arash Pourhabib +2
This paper develops a frequentist solution to the functional calibration problem, where the value of a calibration parameter in a computer model is allowed to vary with the value o…
Self-tuned mirror descent schemes for smooth and nonsmooth high-dimensional stochastic optimization
Nahidsadat Majlesinasab, Farzad Yousefian, Arash Pourhabib
We consider randomized block coordinate stochastic mirror descent (RBSMD) methods for solving high-dimensional stochastic optimization problems with strongly convex objective funct…
Sequential Monte Carlo Filtering Estimation of Ebola Progression in West Africa
Narges Montazeri Shahtori, Caterina Scoglio, Arash Pourhabib +1
We use a multivariate formulation of sequential Monte Carlo filter that utilizes mechanistic models for Ebola virus propagation and available incidence data to simultaneously estim…
Sparse Pseudo-input Local Kriging for Large Spatial Datasets with Exogenous Variables
Babak Farmanesh, Arash Pourhabib
We study large-scale spatial systems that contain exogenous variables, e.g. environmental factors that are significant predictors in spatial processes. Building predictive models f…
A Bayesian framework for functional calibration of expensive computational models through non-isometric matching
Babak Farmanesh, Arash Pourhabib, Balabhaskar Balasundaram +1
We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functiona…
Empirical Similarity for Absent Data Generation in Imbalanced Classification
Arash Pourhabib
When the training data in a two-class classification problem is overwhelmed by one class, most classification techniques fail to correctly identify the data points belonging to the…