6 papers · 1 filter
Copula-Stein Discrepancy: A Generator-Based Stein Operator for Archimedean Dependence
Agnideep Aich, Ashit Baran Aich
Kernel Stein discrepancies (KSDs) are widely used for goodness-of-fit testing, but standard KSDs can be insensitive to higher-order dependence features such as tail dependence. We…
Copula Discrepancy: Benchmarking Dependence Structure
Agnideep Aich, Ashit Baran Aich
We study a simple statistic for benchmarking how well a sample preserves a known bivariate dependence structure. Given a target copula family (Clayton or Gumbel) and parameter $θ_P…
Bag of Coins: A Statistical Probe into Neural Confidence Structures
Agnideep Aich, Sameera Hewage, Md Monzur Murshed +2
Modern neural networks often produce miscalibrated confidence scores and struggle to detect out-of-distribution (OOD) inputs, while most existing methods post-process outputs witho…
Temporal Conformal Prediction (TCP): A Distribution-Free Statistical and Machine Learning Framework for Adaptive Risk Forecasting
Agnideep Aich, Ashit Baran Aich, Dipak C. Jain
We propose \textbf{Temporal Conformal Prediction (TCP)}, a distribution-free framework for constructing well-calibrated prediction intervals in nonstationary time series. TCP coupl…
Deep Copula Classifier: Theory, Consistency, and Empirical Evaluation
Agnideep Aich, Ashit Baran Aich
We present the Deep Copula Classifier (DCC), a class-conditional generative model that separates marginal estimation from dependence modeling using neural copula densities. DCC is…
Symplectic Generative Networks (SGNs): A Hamiltonian Framework for Invertible Deep Generative Modeling
Agnideep Aich, Ashit Aich
We introduce the \emph{Symplectic Generative Network (SGN)}, a deep generative model that leverages Hamiltonian mechanics to construct an invertible, volume-preserving mapping betw…