4 citations · 5 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…