Zobrazeno 1 - 10
of 182
pro vyhledávání: '"Navratil, Jiri"'
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
Autor:
Artman, Conor M., Mate, Aditya, Nwankwo, Ezinne, Heching, Aliza, Idé, Tsuyoshi, Navrátil, Jiří, Shanmugam, Karthikeyan, Sun, Wei, Varshney, Kush R., Goldkind, Lauri, Kroch, Gidi, Sawyer, Jaclyn, Watson, Ian
We developed a common algorithmic solution addressing the problem of resource-constrained outreach encountered by social change organizations with different missions and operations: Breaking Ground -- an organization that helps individuals experienci
Externí odkaz:
http://arxiv.org/abs/2403.10638
Autor:
Wu, Dongxia, Idé, Tsuyoshi, Lozano, Aurélie, Kollias, Georgios, Navrátil, Jiří, Abe, Naoki, Ma, Yi-An, Yu, Rose
We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality i
Externí odkaz:
http://arxiv.org/abs/2402.03726
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
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to an ad-hoc operating point, making eva
Externí odkaz:
http://arxiv.org/abs/2310.03158
Autor:
Belgodere, Brian, Dognin, Pierre, Ivankay, Adam, Melnyk, Igor, Mroueh, Youssef, Mojsilovic, Aleksandra, Navratil, Jiri, Nitsure, Apoorva, Padhi, Inkit, Rigotti, Mattia, Ross, Jerret, Schiff, Yair, Vedpathak, Radhika, Young, Richard A.
Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate unbiased, privacy-preserving data while maintaining fidelity to the o
Externí odkaz:
http://arxiv.org/abs/2304.10819
Autor:
Navratil, Jiri1 (AUTHOR), Kratochvilova, Monika2 (AUTHOR), Raudenska, Martina1,2 (AUTHOR), Balvan, Jan1 (AUTHOR), Vicar, Tomas1,2 (AUTHOR), Petrlakova, Katerina1 (AUTHOR), Suzuki, Kanako1 (AUTHOR), Jadrna, Lucie3 (AUTHOR), Bursa, Jiri3 (AUTHOR), Kräter, Martin4,5 (AUTHOR), Kim, Kyoohyun4 (AUTHOR), Masarik, Michal1,2,6 (AUTHOR), Gumulec, Jaromir1 (AUTHOR) j.gumulec@med.muni.cz
Publikováno v:
Cancer Cell International. 9/11/2024, Vol. 24 Issue 1, p1-17. 17p.
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4131-4138, 2021
This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption from many sensor measurements, we often have a situation where some ob
Externí odkaz:
http://arxiv.org/abs/2208.10679
Autor:
Procházková, Jiřina, Fedr, Radek, Hradilová, Barbora, Kvokačková, Barbora, Slavík, Josef, Kováč, Ondrej, Machala, Miroslav, Fabian, Pavel, Navrátil, Jiří, Kráčalíková, Simona, Levková, Monika, Ovesná, Petra, Bouchal, Jan, Souček, Karel
Publikováno v:
In Journal of Lipid Research September 2024 65(9)
Autor:
Sojka, Antonín, Janíček, Petr, Zich, Jan, Navrátil, Jiří, Ruleová, Pavlína, Plecháček, Tomáš, Kucek, Vladimír, Knížek, Karel, Drašar, Čestmír
Publikováno v:
In Computational Materials Science July 2024 243