4 citations · 17 across the 10 of their papers we have counts for
11 papers
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…
Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards
Faisal Hamman, Chenyang Zhu, Anoop Kumar +4
RAG systems are increasingly deployed in high-stakes domains where users expect outputs to be consistent across semantically equivalent queries. However, existing systems often exh…
T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning
Yanjun Fu, Faisal Hamman, Sanghamitra Dutta
Instruction tuning is essential for Large Language Models (LLMs) to effectively follow user instructions. To improve training efficiency and reduce data redundancy, recent works us…
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 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…
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…
A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition
Faisal Hamman, Sanghamitra Dutta
This paper introduces a novel information-theoretic perspective on the relationship between prominent group fairness notions in machine learning, namely statistical parity, equaliz…
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
Faisal Hamman, Sanghamitra Dutta
This work presents an information-theoretic perspective to group fairness trade-offs in federated learning (FL) with respect to sensitive attributes, such as gender, race, etc. Exi…
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
Faisal Hamman, Erfaun Noorani, Saumitra Mishra +2
There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust coun…
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity
Faisal Hamman, Jiahao Chen, Sanghamitra Dutta
Existing regulations prohibit model developers from accessing protected attributes (gender, race, etc.), often resulting in fairness assessments on populations without knowing thei…