12 citations · 33 across the 13 of their papers we have counts for
14 papers
Experimental evidence on tonal noise generation and its control in tip-leakage flows
Prateek Jaiswal, Stephane Moreau
The present study is first to provide experimental evidence of tonal noise and acoustic feedback by a stationary airfoil with a tip-gap placed between two side plates. Tones are li…
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1
Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named -TS, where we use a…
Aeroacoustic investigation of airfoil at near stall conditions
Prateek Jaiswal, Jose Rendón, Stéphane Moreau
This paper presents a detailed aeroacoustic investigation of a Controlled-Diffusion airfoil at near stall condition. The study aims at answering two research questions: identify th…
Effects of porous substrates on the structure of turbulent boundary layers
Prateek Jaiswal, Bharathram Ganapathisubramani
Three different porous substrates (with different pore sizes, s, and permeabilities, K) are used to examine their effect on the structure of boundary layer flow over them. The flow…
Facial De-morphing: Extracting Component Faces from a Single Morph
Sudipta Banerjee, Prateek Jaiswal, Arun Ross
A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple ident…
Multistart Algorithm for Identifying All Optima of Nonconvex Stochastic Functions
Prateek Jaiswal, Jeffrey Larson
We propose a multistart algorithm to identify all local minima of a constrained, nonconvex stochastic optimization problem. The algorithm uniformly samples points in the domain and…
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data…
Estimating Stochastic Poisson Intensities Using Deep Latent Models
Ruixin Wang, Prateek Jaiwal, Harsha Honnappa
We present methodology for estimating the stochastic intensity of a doubly stochastic Poisson process. Statistical and theoretical analyses of traffic traces show that these proces…
Variational Bayesian Methods for Stochastically Constrained System Design Problems
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We study system design problems stated as parameterized stochastic programs with a chance-constraint set. We adopt a Bayesian approach that requires the computation of a posterior…
Asymptotic Consistency of Loss-Calibrated Variational Bayes
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
This paper establishes the asymptotic consistency of the {\it loss-calibrated variational Bayes} (LCVB) method. LCVB was proposed in~\cite{LaSiGh2011} as a method for approximately…
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We study data-driven decision-making problems in the Bayesian framework, where the expectation in the Bayes risk is replaced by a risk-sensitive entropic risk measure. We focus on…
Asymptotic Consistency of Rényi-Approximate Posteriors
Prateek Jaiswal, Vinayak A. Rao, Harsha Honnappa
We study the asymptotic consistency properties of -Rényi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with…
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…