no code implementations • 19 Apr 2024 • Qinyuan Wu, Mohammad Aflah Khan, Soumi Das, Vedant Nanda, Bishwamittra Ghosh, Camila Kolling, Till Speicher, Laurent Bindschaedler, Krishna P. Gummadi, Evimaria Terzi
We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs).
no code implementations • 11 Mar 2024 • Bishwamittra Ghosh, Debabrota Basu, Fu Huazhu, Wang Yuan, Renuga Kanagavelu, Jiang Jin Peng, Liu Yong, Goh Siow Mong Rick, Wei Qingsong
Additionally, to assess client contribution under limited computational budget, we propose a scheduling procedure that considers a two-sided fairness criteria to perform expensive Shapley value computation only in a subset of training epochs.
1 code implementation • 1 Jun 2022 • Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel
In this paper, we aim to quantify the influence of different features in a dataset on the bias of a classifier.
no code implementations • 14 May 2022 • Bishwamittra Ghosh, Dmitry Malioutov, Kuldeep S. Meel
The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable.
1 code implementation • 20 Sep 2021 • Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel
In recent years, machine learning (ML) algorithms have been deployed in safety-critical and high-stake decision-making, where the fairness of algorithms is of paramount importance.
no code implementations • 18 Sep 2020 • Daniel Neider, Bishwamittra Ghosh
We propose a novel approach to understanding the decision making of complex machine learning models (e. g., deep neural networks) using a combination of probably approximately correct learning (PAC) and a logic inference methodology called syntax-guided synthesis (SyGuS).
1 code implementation • 14 Sep 2020 • Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel
We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds.
no code implementations • 12 Jun 2020 • Bishwamittra Ghosh, Daniel Neider
This paper presents LEXR, a framework for explaining the decision making of recurrent neural networks (RNNs) using a formal description language called Linear Temporal Logic (LTL).
no code implementations • 7 Jan 2020 • Bishwamittra Ghosh, Kuldeep S. Meel
While MLIC was shown to achieve accuracy similar to that of other state of the art black-box classifiers while generating small interpretable CNF formulas, the runtime performance of MLIC is significantly lagging and renders approach unusable in practice.