5 citations · 9 across the 8 of their papers we have counts for
8 papers
Model Risk Management for Generative AI In Financial Institutions
Anwesha Bhattacharyya, Ye Yu, Hanyu Yang +4
The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of busin…
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
Yueyang Shen, Agus Sudjianto, Arun Prakash R +5
We propose and study a minimalist approach towards synthetic tabular data generation. The model consists of a minimalistic unsupervised SparsePCA encoder (with contingent clusterin…
Assessing Robustness of Machine Learning Models using Covariate Perturbations
Arun Prakash R, Anwesha Bhattacharyya, Joel Vaughan +1
As machine learning models become increasingly prevalent in critical decision-making models and systems in fields like finance, healthcare, etc., ensuring their robustness against…
Using Markov Boundary Approach for Interpretable and Generalizable Feature Selection
Anwesha Bhattacharyya, Yaqun Wang, Joel Vaughan +1
The perceived advantage of machine learning (ML) models is that they are flexible and can incorporate a large number of features. However, many of these are typically correlated or…
Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation
Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithm…
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…
Bayesian Analysis of High-dimensional Discrete Graphical Models
Anwesha Bhattacharyya, Yves Atchade
This work introduces a Bayesian methodology for fitting large discrete graphical models with spike-and-slab priors to encode sparsity. We consider a quasi-likelihood approach that…
An approach to large-scale Quasi-Bayesian inference with spike-and-slab priors
Yves Atchade, Anwesha Bhattacharyya
We propose a general framework using spike-and-slab prior distributions to aid with the development of high-dimensional Bayesian inference. Our framework allows inference with a ge…