Search Results for author:
Found 15 papers, 5 papers with code
REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models
Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable LLM outputs through aggregation.
FairEM360: A Suite for Responsible Entity Matching
Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data.
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs
Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications.
Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities
The potential harms of the under-representation of minorities in training data, particularly in multi-modal settings, is a well-recognized concern.
Efficient Strongly Polynomial Algorithms for Quantile Regression
Linear Regression is a seminal technique in statistics and machine learning, where the objective is to build linear predictive models between a response (i. e., dependent) variable and one or more predictor (i. e., independent) variables.
Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching
Entity matching (EM) is a challenging problem studied by different communities for over half a century.
Sampling-Based Techniques for Training Deep Neural Networks with Limited Computational Resources: A Scalability Evaluation
Making a connection between the two research directions as special cases of approximating matrix multiplications in the context of neural networks, we provide a negative theoretical analysis that shows feedforward approximation is an obstacle against scalability.
FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness
Despite the potential benefits of machine learning (ML) in high-risk decision-making domains, the deployment of ML is not accessible to practitioners, and there is a risk of discrimination.
Representation Bias in Data: A Survey on Identification and Resolution Techniques
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately.
Finding Representative Group Fairness Metrics Using Correlation Estimations
Using the estimated correlations, we then find a subset of representative metrics.
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
Existing techniques for producing fair ML models either are limited to the type of fairness constraints they can handle (e. g., preprocessing) or require nontrivial modifications to downstream ML training algorithms (e. g., in-processing).
Fair Active Learning
Collecting accurate labeled data in societal applications is challenging and costly.
Fair Active Learning
Machine learning (ML) is increasingly being used in high-stakes applications impacting society.
Responsible Scoring Mechanisms Through Function Sampling
We provide unbiased samplers for the entire function space, as well as a $\theta$-vicinity around a given function.
Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons
This design is both sufficient and efficient, as it is proven to find a short terminal question sequence.
