- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources5
- Resource Type
-
01000040000
- More
- Availability
-
14
- Author / Contributor
- Filter by Author / Creator
-
-
Das, Saswat (5)
-
Fioretto, Ferdinando (5)
-
Romanelli, Marco (2)
-
Task, Christine (2)
-
Zhu, Keyu (2)
-
Kailkhura, Bhavya (1)
-
Ko, Joonhyuk (1)
-
Reza, Zarreen (1)
-
Tran, Cuong (1)
-
Van Hentenryck, Pascal (1)
-
Van_Hentenryck, Pascal (1)
-
Williams, Matt (1)
-
Ziani, Juba (1)
-
#Tyler Phillips, Kenneth E. (0)
-
#Willis, Ciara (0)
-
& Abreu-Ramos, E. D. (0)
-
& Abramson, C. I. (0)
-
& Abreu-Ramos, E. D. (0)
-
& Adams, S.G. (0)
-
& Ahmed, K. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Sahin. I. (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Free, publicly-accessible full text available August 16, 2025
-
Das, Saswat ; Romanelli, Marco ; Tran, Cuong ; Reza, Zarreen ; Kailkhura, Bhavya ; Fioretto, Ferdinando ( , arXivorg)Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This paper investigates the effectiveness of these methods in capturing the shift of fine-tuning datasets from the initial pre-trained data distribution. Our findings reveal that there are cases in which low-rank fine-tuning falls short in learning such shifts. This, in turn, produces non-negligible side effects, especially when fine-tuning is adopted for toxicity mitigation in pre-trained models, or in scenarios where it is important to provide fair models. Through comprehensive empirical evidence on several models, datasets, and tasks, we show that low-rank fine-tuning inadvertently preserves undesirable biases and toxic behaviors. We also show that this extends to sequential decision-making tasks, emphasizing the need for careful evaluation to promote responsible LLMs development.more » « lessFree, publicly-accessible full text available May 28, 2025
-
Das, Saswat ; Zhu, Keyu ; Task, Christine ; Van_Hentenryck, Pascal ; Fioretto, Ferdinando ( , Proceedings of the AAAI Conference on Artificial Intelligence)
This paper analyzes the privacy of traditional Statistical Disclosure Control (SDC) systems under a differential privacy interpretation. SDCs, such as cell suppression and swapping, promise to safeguard the confidentiality of data and are routinely adopted in data analyses with profound societal and economic impacts. Through a formal analysis and empirical evaluation of demographic data from real households in the U.S., the paper shows that widely adopted SDC systems not only induce vastly larger privacy losses than classical differential privacy mechanisms, but, they may also come at a cost of larger accuracy and fairness.
Free, publicly-accessible full text available March 25, 2025 -
Das, Saswat ; Romanelli, Marco ; Fioretto, Ferdinando ( , Proceedings of Machine Learning Research)Free, publicly-accessible full text available February 6, 2025
-
Zhu, Keyu ; Fioretto, Ferdinando ; Van Hentenryck, Pascal ; Das, Saswat ; Task, Christine ( , arXivorg)