3 citations · 7 across the 14 of their papers we have counts for
17 papers
Two-Loop Stochastic Mirror Langevin Algorithms for Constrained Sampling
Ruiting Tong, Antik Chakraborty, Raghu Pasupathy
We study the problem of sampling from a target distribution supported on a convex set , when the potential is accessible only th…
Near-Optimal Private Tests for Simple and MLR Hypotheses
Yu-Wei Chen, Raghu Pasupathy, Jordan Awan
We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio cond…
Frank-Wolfe Recursions for the Emergency Response Problem on Measure Spaces
Di Yu, Shane G. Henderson, Raghu Pasupathy
We consider an optimization problem over measures for emergency response to out-of-hospital cardiac arrest (OHCA), where the goal is to allocate volunteer resources across a spatia…
Drift Optimization of Regulated Stochastic Models Using Sample Average Approximation
Zihe Zhou, Harsha Honnappa, Raghu Pasupathy
This paper introduces a drift optimization model of stochastic optimization problems driven by regulated stochastic processes. A broad range of problems across operations research,…
Optimal Survey Design for Private Mean Estimation
Yu-Wei Chen, Raghu Pasupathy, Jordan A. Awan
This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) a…
On The Global Convergence Of Online RLHF With Neural Parametrization
Mudit Gaur, Amrit Singh Bedi, Raghu Pasupathy +1
The importance of Reinforcement Learning from Human Feedback (RLHF) in aligning large language models (LLMs) with human values cannot be overstated. RLHF is a three-stage process t…
Deterministic and Stochastic Frank-Wolfe Recursion on Probability Spaces
Di Yu, Shane G. Henderson, Raghu Pasupathy
Motivated by applications in emergency response and experimental design, we consider smooth stochastic optimization problems over probability measures supported on compact subsets…
Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms
Yunsoo Ha, Sara Shashaani, Raghu Pasupathy
Model update (MU) and candidate evaluation (CE) are classical steps incorporated inside many stochastic trust-region (TR) algorithms. The sampling effort exerted within these steps…
Uncertainty Quantification using Simulation Output: Batching as an Inferential Device
Yongseok Jeon, Yi Chu, Raghu Pasupathy +1
We present batching as an omnibus device for uncertainty quantification using simulation output. We consider the classical context of a simulationist performing uncertainty quantif…
Overlapping Batch Confidence Intervals on Statistical Functionals Constructed from Time Series: Application to Quantiles, Optimization, and Estimation
Ziwei Su, Raghu Pasupathy, Yingchieh Yeh +1
We propose a general purpose confidence interval procedure (CIP) for statistical functionals constructed using data from a stationary time series. The procedures we propose are bas…
A Retrospective Approximation Approach for Smooth Stochastic Optimization
David Newton, Raghu Bollapragada, Raghu Pasupathy +1
Stochastic Gradient (SG) is the defacto iterative technique to solve stochastic optimization (SO) problems with a smooth (non-convex) objective and a stochastic first-order ora…
Adaptive Sequential SAA for Solving Two-stage Stochastic Linear Programs
Raghu Pasupathy, Yongjia Song
We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose…
Optimal Allocations for Sample Average Approximation
Prateek Jaiswal, Harsha Honnappa, Raghu Pasupathy
We consider a single stage stochastic program without recourse with a strictly convex loss function. We assume a compact decision space and grid it with a finite set of points. In…
Dominating Points of Gaussian Extremes
Harsha Honnappa, Raghu Pasupathy, Prateek Jaiswal
We quantify the large deviations of Gaussian extreme value statistics on closed convex sets in d-dimensional Euclidean space. The asymptotics imply that the extreme value distribut…
Optimal placement of a small order in a diffusive limit order book
José E. Figueroa-López, Hyoeun Lee, Raghu Pasupathy
We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a di…
ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization
Sara Shashaani, Fatemeh Hashemi, Raghu Pasupathy
We consider unconstrained optimization problems where only "stochastic" estimates of the objective function are observable as replicates from a Monte Carlo oracle. The Monte Carlo…
Efficient Estimation in the Tails of Gaussian Copulas
Kalyani Nagaraj, Jie Xu, Raghu Pasupathy +1
We consider the question of efficient estimation in the tails of Gaussian copulas. Our special focus is estimating expectations over multi-dimensional constrained sets that have a…