33 citations · 35 across the 13 of their papers we have counts for
5 papers · 1 filter
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…