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