33 citations · 35 across the 13 of their papers we have counts for
15 papers
Simulating Stress Laws under Extremal Dependence: Characterizing What Generative Models Must Preserve
Mantu Gupta, Anand Deo
We study stress-scenario generation for systems driven by multivariate heavy-tailed risk factors. Within regions where several financial losses are simultaneously extreme, stress a…
No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
Subhabrata Majumdar, Anand Deo, Partha Pratim Saha +1
Neural network classifiers trained by cross-entropy minimization are highly sensitive to label noise and adversarial contamination. While robust alternatives offer bounded influenc…
An Extreme Value Perspective on Learning Stress Laws
Mantu Gupta, Anand Deo
We introduce Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities in both heavy and light-tailed se…
Generating Plausible Stress Scenarios via Large Deviations
Anand Deo
Financial stress tests based on handpicked scenarios can mislead risk management by overlooking genuinely dangerous configurations or overemphasising shocks that are too implausibl…
Decision-Scaled Scenario Approach for Rare Chance-Constrained Optimization
Jaeseok Choi, Anand Deo, Constantino Lagoa +1
Chance-constrained optimization is a suitable modeling framework for safety-critical applications where violating constraints is nearly unacceptable. The scenario approach is a pop…
EVT-Based Rate-Preserving Distributional Robustness for Tail Risk Functionals
Anand Deo
Risk measures such as Conditional Value-at-Risk (CVaR) focus on extreme losses, where scarce tail data makes model error unavoidable. To hedge misspecification, one evaluates worst…
The Scaling Behaviors in Achieving High Reliability via Chance-Constrained Optimization
Anand Deo, Karthyek Murthy
We study the problem of resource provisioning under stringent reliability or service level requirements, which arise in applications such as power distribution, emergency response,…
Reduced Sample Complexity in Scenario-Based Control System Design via Constraint Scaling
Jaeseok Choi, Anand Deo, Constantino Lagoa +1
The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability…
Importance Sampling for Minimization of Tail Risks: A Tutorial
Anand Deo, Karthyek Murthy
This paper provides an introductory overview of how one may employ importance sampling effectively as a tool for solving stochastic optimization formulations incorporating tail ris…
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk
Anand Deo, Karthyek Murthy, Tirtho Sarker
This paper investigates the use of retrospective approximation solution paradigm in solving risk-averse optimization problems effectively via importance sampling (IS). While IS ser…
Efficient Black-Box Importance Sampling for VaR and CVaR Estimation
Anand Deo, Karthyek Murthy
This paper considers Importance Sampling (IS) for the estimation of tail risks of a loss defined in terms of a sophisticated object such as a machine learning feature map or a mixe…
Achieving Efficiency in Black Box Simulation of Distribution Tails with Self-structuring Importance Samplers
Anand Deo, Karthyek Murthy
This paper presents a novel Importance Sampling (IS) scheme for estimating distribution tails of performance measures modeled with a rich set of tools such as linear programs, inte…
Optimizing tail risks using an importance sampling based extrapolation for heavy-tailed objectives
Anand Deo, Karthyek Murthy
Motivated by the prominence of Conditional Value-at-Risk (CVaR) as a measure for tail risk in settings affected by uncertainty, we develop a new formula for approximating CVaR base…
City-Scale Agent-Based Simulators for the Study of Non-Pharmaceutical Interventions in the Context of the COVID-19 Epidemic
Shubhada Agrawal, Siddharth Bhandari, Anirban Bhattacharjee +14
We highlight the usefulness of city-scale agent-based simulators in studying various non-pharmaceutical interventions to manage an evolving pandemic. We ground our studies in the c…
Credit Risk: Simple Closed Form Approximate Maximum Likelihood Estimator
Anand Deo, Sandeep Juneja
We consider discrete default intensity based and logit type reduced form models for conditional default probabilities for corporate loans where we develop simple closed form approx…