Search Results for author:
Found 9 papers, 3 papers with code
Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction
We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs).
Don't Forget What I did?: Assessing Client Contributions in Federated Learning
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.
How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis
In this paper, we aim to quantify the influence of different features in a dataset on the bias of a classifier.
Efficient Learning of Interpretable Classification Rules
The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable.
Algorithmic Fairness Verification with Graphical Models
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.
Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis
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).
Justicia: A Stochastic SAT Approach to Formally Verify Fairness
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.
A Formal Language Approach to Explaining RNNs
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).
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules
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.
