Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Paes, Lucas Monteiro"'
Autor:
Oesterling, Alex, Verdun, Claudio Mayrink, Long, Carol Xuan, Glynn, Alex, Paes, Lucas Monteiro, Vithana, Sajani, Cardone, Martina, Calmon, Flavio P.
Image search and retrieval tasks can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current approaches to mitigate these representational harms balance the number of retrieved items across population groups
Externí odkaz:
http://arxiv.org/abs/2407.08571
Feature attribution methods explain black-box machine learning (ML) models by assigning importance scores to input features. These methods can be computationally expensive for large ML models. To address this challenge, there has been increasing effo
Externí odkaz:
http://arxiv.org/abs/2405.19562
Autor:
Paes, Lucas Monteiro, Wei, Dennis, Do, Hyo Jin, Strobelt, Hendrik, Luss, Ronny, Dhurandhar, Amit, Nagireddy, Manish, Ramamurthy, Karthikeyan Natesan, Sattigeri, Prasanna, Geyer, Werner, Ghosh, Soumya
Perturbation-based explanation methods such as LIME and SHAP are commonly applied to text classification. This work focuses on their extension to generative language models. To address the challenges of text as output and long text inputs, we propose
Externí odkaz:
http://arxiv.org/abs/2403.14459
Machine learning (ML) is widely used to moderate online content. Despite its scalability relative to human moderation, the use of ML introduces unique challenges to content moderation. One such challenge is predictive multiplicity: multiple competing
Externí odkaz:
http://arxiv.org/abs/2402.16979
Autor:
Paes, Lucas Monteiro, Suresh, Ananda Theertha, Beutel, Alex, Calmon, Flavio P., Beirami, Ahmad
Machine learning (ML) models used in prediction and classification tasks may display performance disparities across population groups determined by sensitive attributes (e.g., race, sex, age). We consider the problem of evaluating the performance of
Externí odkaz:
http://arxiv.org/abs/2312.03867
Autor:
Lin, Alexander, Paes, Lucas Monteiro, Tanneru, Sree Harsha, Srinivas, Suraj, Lakkaraju, Himabindu
Text-to-image models take a sentence (i.e., prompt) and generate images associated with this input prompt. These models have created award wining-art, videos, and even synthetic datasets. However, text-to-image (T2I) models can generate images that u
Externí odkaz:
http://arxiv.org/abs/2306.05500