1 citations · 3 across the 14 of their papers we have counts for
14 papers
Bayesian Inference for Joint Tail Risk in Paired Biomarkers via Archimedean Copulas with Restricted Jeffreys Priors
Agnideep Aich, Md. Monzur Murshed, Sameera Hewage +1
We propose a Bayesian copula-based framework to quantify clinically interpretable joint tail risks from paired continuous biomarkers. After converting each biomarker margin to rank…
Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification
Agnideep Aich, Sameera Hewage, Md Monzur Murshed
Clinical and gene-expression models predict breast cancer outcomes, but simple linear fusion ignores dependence between their risk scores. Using METABRIC, we tested whether modelin…
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…
The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
Jose Cribeiro-Ramallo, Agnideep Aich, Florian Kalinke +2
Kernel Stein discrepancies (KSDs) have emerged as a powerful tool for quantifying goodness-of-fit over the last decade, featuring numerous successful applications. To the best of o…
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…
From Sublinear to Linear: Local Convergence in Finite-Width Networks via Locally Polyak-Lojasiewicz Regions
Agnideep Aich, Ashit Baran Aich, Bruce Wade
We study local linear convergence of gradient descent for finite-width feedforward networks under the squared empirical loss. Prior work shows that GD can remain confined to a Loca…
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): Rolling Calibration for Financial Risk Forecasting
Agnideep Aich, Ashit Baran Aich, Dipak C. Jain
We study two rolling conformal procedures that construct prediction intervals for financial returns. Temporal Conformal Prediction (TCP) uses a zero-truncated version of the confor…
CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction
Agnideep Aich, Md Monzur Murshed, Bruce Wade +1
Class imbalance remains a practical obstacle in the development of clinical prediction models for conditions such as diabetes mellitus, where the number of confirmed cases is often…
A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
Agnideep Aich, Sameera Hewage, Md Monzur Murshed +5
In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing bot…
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…
A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine Learning
Agnideep Aich, Md Monzur Murshed, Sameera Hewage +1
Effective feature selection is critical for robust and interpretable predictive modeling in medicine, especially when risk factors matter most in extreme patient strata. Many stand…
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…
IGNIS: A Robust Neural Network Framework for Constrained Parameter Estimation in Archimedean Copulas
Agnideep Aich
Classical estimators, the cornerstones of statistical inference, face insurmountable challenges when applied to important emerging classes of Archimedean copulas. These models exhi…