4 citations · 5 across the 13 of their papers we have counts for
13 papers
Learning Invariant Graph Representations Through Redundant Information
Barproda Halder, Pasan Dissanayake, Sanghamitra Dutta
Learning invariant graph representations for out-of-distribution (OOD) generalization remains challenging because the learned representations often retain spurious components. To a…
TabDistill: Distilling Transformers into Neural Nets for Few-Shot Tabular Classification
Pasan Dissanayake, Sanghamitra Dutta
Transformer-based models have shown promising performance on tabular data compared to their classical counterparts such as neural networks and Gradient Boosted Decision Trees (GBDT…
Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations
Faisal Hamman, Pasan Dissanayake, Yanjun Fu +1
Knowledge distillation is a promising approach to transfer capabilities from complex teacher models to smaller, resource-efficient student models that can be deployed easily, parti…
What If, But Privately: Private Counterfactual Retrieval
Shreya Meel, Mohamed Nomeir, Pasan Dissanayake +2
Transparency and explainability are two important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual exp…
Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures
Erfaun Noorani, Pasan Dissanayake, Faisal Hamman +1
Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense int…
Private Counterfactual Retrieval With Immutable Features
Shreya Meel, Pasan Dissanayake, Mohamed Nomeir +2
In a classification task, counterfactual explanations provide the minimum change needed for an input to be classified into a favorable class. We consider the problem of privately r…
Quantifying Knowledge Distillation Using Partial Information Decomposition
Pasan Dissanayake, Faisal Hamman, Barproda Halder +3
Knowledge distillation deploys complex machine learning models in resource-constrained environments by training a smaller student model to emulate internal representations of a com…
Private Counterfactual Retrieval
Mohamed Nomeir, Pasan Dissanayake, Shreya Meel +2
Transparency and explainability are two extremely important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterf…
Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs
Faisal Hamman, Pasan Dissanayake, Saumitra Mishra +2
Fine-tuning LLMs on tabular classification tasks can lead to the phenomenon of fine-tuning multiplicity where equally well-performing models make conflicting predictions on the sam…
Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition
Barproda Halder, Faisal Hamman, Pasan Dissanayake +3
Spuriousness arises when there is an association between two or more variables in a dataset that are not causally related. In this work, we propose an explainability framework to p…
Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory
Pasan Dissanayake, Sanghamitra Dutta
Counterfactual explanations provide ways of achieving a favorable model outcome with minimum input perturbation. However, counterfactual explanations can also be leveraged to recon…
The Eigenvectors of Single-spiked Complex Wishart Matrices: Finite and Asymptotic Analyses
Prathapasinghe Dharmawansa, Pasan Dissanayake, Yang Chen
Let be a {\it single-spiked} Wishart matrix in the class with…
Distribution of the Scaled Condition Number of Single-spiked Complex Wishart Matrices
Pasan Dissanayake, Prathapasinghe Dharmawansa, Yang Chen
Let () be a random matrix with independent columns each distributed as complex multivariate Gaussian with zero mean and {\it single-s…