Prateek Jaiswal

2026

1. Mgmt Sci

   Learning When to Trust in Observational Data: Deconfounded Transfer for Online Decision-Making

   Prateek Jaiswal, Esmaeil Keyvanshokooh, and Junyu Cao

   Submitted to Management Science, under review, 2026
2. MOR

   Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling

   Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, and 1 more author

   Revision 1 submitted to Mathematics of Operations Research, 2026

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2023

1. JMLR

   Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors

   Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, and 1 more author

   Revision 2 submitted to Journal of Machine Learning Research, 2023

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   Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of α-TS where we use a fractional posterior instead of the standard posterior distribution. To compute a fractional posterior, the likelihood in the definition of the standard posterior is tempered with a factor α. For α-TS we obtain both instance-dependent and instance-independent frequentist regret bounds under very mild conditions on the prior and reward distributions. Both the sub-Gaussian and exponential family models satisfy our general conditions on the reward distribution. Our conditions on the prior distribution just require its density to be positive, continuous, and bounded. We also establish another instance-dependent regret upper bound that matches (up to constants) to that of improved UCB [Auer and Ortner, 2010]. Our regret analysis carefully combines recent theoretical developments in the non-asymptotic concentration analysis and Bernstein-von Mises type results for the fractional posterior distribution. Moreover, our analysis does not require additional structural properties such as closed-form posteriors or conjugate priors.

2. NeurIPS

   On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making

   Prateek Jaiswal, Harsha Honnappa, and Vinayak Rao

   In Thirty-seventh Conference on Neural Information Processing Systems, 2023

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   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 problems where calculating the posterior distribution is intractable, a typical situation in modern applications with large datasets and complex data generating models. We leverage a dual representation of the entropic risk measure to introduce a novel risk-sensitive variational Bayesian (RSVB) framework for jointly computing a risk-sensitive posterior approximation and the corresponding decision rule. The proposed RSVB framework can be used to extract computational methods for doing risk-sensitive approximate Bayesian inference. We show that our general framework includes two well-known computational methods for doing approximate Bayesian inference viz. naive VB and loss-calibrated VB. We also study the impact of these computational approximations on the predictive performance of the inferred decision rules and values. We compute the convergence rates of the RSVB approximate posterior and also of the corresponding optimal value and decision rules. We illustrate our theoretical findings in both parametric and nonparametric settings with the help of three examples: the single and multi-product newsvendor model and Gaussian process classification.

3. SIOPT

   Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency

   Prateek Jaiswal, Harsha Honnappa, and Vinayak A. Rao

   SIAM Journal on Optimization, 2023

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   This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems. However, the computation of Bayesian posteriors is typically an intractable problem and has spawned a large literature on approximate Bayesian computation. Here, in the context of chance-constrained optimization, we focus on the question of statistical consistency (in an appropriate sense) of the optimal value, computed using an approximate posterior distribution. To this end, we rigorously prove a frequentist consistency result demonstrating the convergence of the optimal value to the optimal value of a fixed, parameterized constrained optimization problem. We augment this by also establishing a probabilistic rate of convergence of the optimal value. We also prove the convex feasibility of the approximate Bayesian stochastic optimization problem. Finally, we demonstrate the utility of our approach on an optimal staffing problem for an M/M/c queueing model.