Search Results for author:
Found 13 papers, 0 papers with code
Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations
We propose and study a minimalist approach towards synthetic tabular data generation.
Assessing Robustness of Machine Learning Models using Covariate Perturbations
As machine learning models become increasingly prevalent in critical decision-making models and systems in fields like finance, healthcare, etc., ensuring their robustness against adversarial attacks and changes in the input data is paramount, especially in cases where models potentially overfit.
Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation
Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance.
Quantifying Inherent Randomness in Machine Learning Algorithms
Among the different algorithms, randomness in model training causes larger variation for FFNNs compared to tree-based methods.
Interpretable Feature Engineering for Time Series Predictors using Attention Networks
Regression problems with time-series predictors are common in banking and many other areas of application.
Traversing the Local Polytopes of ReLU Neural Networks
Our polytope traversing algorithm can be adapted to a wide range of applications related to robustness and interpretability.
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
Although neural networks (NNs) with ReLU activation functions have found success in a wide range of applications, their adoption in risk-sensitive settings has been limited by the concerns on robustness and interpretability.
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
Supervised Machine Learning Techniques: An Overview with Applications to Banking
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.
Adaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results.
Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem.
Explainable Neural Networks based on Additive Index Models
In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features.
