Zobrazeno 1 - 10
of 7 553
pro vyhledávání: '"Rigotti A"'
Autor:
McCart, Jonathan D., Sedler, Andrew R., Versteeg, Christopher, Mifsud, Domenick, Rigotti-Thompson, Mattia, Pandarinath, Chethan
Recent advances in recording technology have allowed neuroscientists to monitor activity from thousands of neurons simultaneously. Latent variable models are increasingly valuable for distilling these recordings into compact and interpretable represe
Externí odkaz:
http://arxiv.org/abs/2407.21195
Stochastic dominance is an important concept in probability theory, econometrics and social choice theory for robustly modeling agents' preferences between random outcomes. While many works have been dedicated to the univariate case, little has been
Externí odkaz:
http://arxiv.org/abs/2406.06425
Autor:
Melnyk, Igor, Mroueh, Youssef, Belgodere, Brian, Rigotti, Mattia, Nitsure, Apoorva, Yurochkin, Mikhail, Greenewald, Kristjan, Navratil, Jiri, Ross, Jerret
Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper Alignment via Optimal Transport (AOT), a novel method for distributio
Externí odkaz:
http://arxiv.org/abs/2406.05882
Instance segmentation datasets play a crucial role in training accurate and robust computer vision models. However, obtaining accurate mask annotations to produce high-quality segmentation datasets is a costly and labor-intensive process. In this wor
Externí odkaz:
http://arxiv.org/abs/2402.16421
The advent of the Transformer has led to the development of large language models (LLM), which appear to demonstrate human-like capabilities. To assess the generality of this class of models and a variety of other base neural network architectures to
Externí odkaz:
http://arxiv.org/abs/2401.15030
Autor:
Benoit, Harold, Jiang, Liangze, Atanov, Andrei, Kar, Oğuzhan Fatih, Rigotti, Mattia, Zamir, Amir
Supervised learning datasets may contain multiple cues that explain the training set equally well, i.e., learning any of them would lead to the correct predictions on the training data. However, many of them can be spurious, i.e., lose their predicti
Externí odkaz:
http://arxiv.org/abs/2312.16313
Autor:
Nitsure, Apoorva, Mroueh, Youssef, Rigotti, Mattia, Greenewald, Kristjan, Belgodere, Brian, Yurochkin, Mikhail, Navratil, Jiri, Melnyk, Igor, Ross, Jerret
We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical relative testing based on first and second order stochastic dominance
Externí odkaz:
http://arxiv.org/abs/2310.07132
Autor:
Ana Carolina Costa Corrêa, Maria Luísa Oliveira Rigotti, Hugo Dilhermando Souza Lacerda, Bruno Pérez Ferreira
Publikováno v:
BMC Health Services Research, Vol 24, Iss 1, Pp 1-9 (2024)
Abstract Background After the establishment of the public health emergency of international concern in 2020, health systems worldwide and in Brazil observed the need to apply more extraordinary logistical efforts and possibly resources to combat the
Externí odkaz:
https://doaj.org/article/c76998ef3de14cd190b6710d9219c1ae
Along with the successful deployment of deep neural networks in several application domains, the need to unravel the black-box nature of these networks has seen a significant increase recently. Several methods have been introduced to provide insight
Externí odkaz:
http://arxiv.org/abs/2307.02094
Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions, leading to non-homogeneous coverage. We address this with a new adaptive method based on rescaling conformal sco
Externí odkaz:
http://arxiv.org/abs/2305.19901