Author

Anand Deo

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Preprint Aug 2026

Simulating Stress Laws under Extremal Dependence: Characterizing What Generative Models Must Preserve

We study stress-scenario generation for systems driven by multivariate heavy-tailed risk factors. Within regions where several financial losses are simultaneously extreme, stress analysis concerns both the conditional law of the risk factors and the most plausible configurations producing those losses. We show that bot...

Mantu Gupta, Anand Deo · 0 citations

Preprint Jul 2026

An Extreme Value Perspective on Learning Stress Laws

Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities in both heavy and light-tailed settings, leverages asymptotic tail structure to enable standard generative models to generate representative extreme samples and estimate rare-event proba...

Mantu Gupta, Anand Deo · 0 citations

Preprint Aug 2026

No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers

A consistency theory for robust neural classifiers based on the S-divergence family that requires no identifiability assumption is developed and it is proved that empirical S-divergence minimizers converge to the population-optimal equivalence class under mild regularity conditions.

Subhabrata Majumdar, Anand Deo, Partha Pratim Saha et al. · 0 citations