Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Kunesch, Markus"'
Autor:
Jurenka, Irina, Kunesch, Markus, McKee, Kevin R., Gillick, Daniel, Zhu, Shaojian, Wiltberger, Sara, Phal, Shubham Milind, Hermann, Katherine, Kasenberg, Daniel, Bhoopchand, Avishkar, Anand, Ankit, Pîslar, Miruna, Chan, Stephanie, Wang, Lisa, She, Jennifer, Mahmoudieh, Parsa, Rysbek, Aliya, Ko, Wei-Jen, Huber, Andrea, Wiltshire, Brett, Elidan, Gal, Rabin, Roni, Rubinovitz, Jasmin, Pitaru, Amit, McAllister, Mac, Wilkowski, Julia, Choi, David, Engelberg, Roee, Hackmon, Lidan, Levin, Adva, Griffin, Rachel, Sears, Michael, Bar, Filip, Mesar, Mia, Jabbour, Mana, Chaudhry, Arslan, Cohan, James, Thiagarajan, Sridhar, Levine, Nir, Brown, Ben, Gorur, Dilan, Grant, Svetlana, Hashimshoni, Rachel, Weidinger, Laura, Hu, Jieru, Chen, Dawn, Dolecki, Kuba, Akbulut, Canfer, Bileschi, Maxwell, Culp, Laura, Dong, Wen-Xin, Marchal, Nahema, Van Deman, Kelsie, Misra, Hema Bajaj, Duah, Michael, Ambar, Moran, Caciularu, Avi, Lefdal, Sandra, Summerfield, Chris, An, James, Kamienny, Pierre-Alexandre, Mohdi, Abhinit, Strinopoulous, Theofilos, Hale, Annie, Anderson, Wayne, Cobo, Luis C., Efron, Niv, Ananda, Muktha, Mohamed, Shakir, Heymans, Maureen, Ghahramani, Zoubin, Matias, Yossi, Gomes, Ben, Ibrahim, Lila
A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every
Externí odkaz:
http://arxiv.org/abs/2407.12687
Autor:
Mao, Yiran, Reinecke, Madeline G., Kunesch, Markus, Duéñez-Guzmán, Edgar A., Comanescu, Ramona, Haas, Julia, Leibo, Joel Z.
Is it possible to evaluate the moral cognition of complex artificial agents? In this work, we take a look at one aspect of morality: `doing the right thing for the right reasons.' We propose a behavior-based analysis of artificial moral cognition whi
Externí odkaz:
http://arxiv.org/abs/2305.18269
Autor:
Grau-Moya, Jordi, Delétang, Grégoire, Kunesch, Markus, Genewein, Tim, Catt, Elliot, Li, Kevin, Ruoss, Anian, Cundy, Chris, Veness, Joel, Wang, Jane, Hutter, Marcus, Summerfield, Christopher, Legg, Shane, Ortega, Pedro
Meta-training agents with memory has been shown to culminate in Bayes-optimal agents, which casts Bayes-optimality as the implicit solution to a numerical optimization problem rather than an explicit modeling assumption. Bayes-optimal agents are risk
Externí odkaz:
http://arxiv.org/abs/2209.15618
Autor:
Brekelmans, Rob, Genewein, Tim, Grau-Moya, Jordi, Delétang, Grégoire, Kunesch, Markus, Legg, Shane, Ortega, Pedro
Publikováno v:
TMLR (2022) https://openreview.net/forum?id=berNQMTYWZ
Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show how this robustness arises from hedging against worst-case perturbati
Externí odkaz:
http://arxiv.org/abs/2203.12592
Autor:
Andrade, Tomas, Salo, Llibert Areste, Aurrekoetxea, Josu C., Bamber, Jamie, Clough, Katy, Croft, Robin, de Jong, Eloy, Drew, Amelia, Duran, Alejandro, Ferreira, Pedro G., Figueras, Pau, Finkel, Hal, França, Tiago, Ge, Bo-Xuan, Gu, Chenxia, Helfer, Thomas, Jäykkä, Juha, Joana, Cristian, Kunesch, Markus, Kornet, Kacper, Lim, Eugene A., Muia, Francesco, Nazari, Zainab, Radia, Miren, Ripley, Justin, Shellard, Paul, Sperhake, Ulrich, Traykova, Dina, Tunyasuvunakool, Saran, Wang, Zipeng, Widdicombe, James Y., Wong, Kaze
Publikováno v:
Journal of Open Source Software, 6(68), 3703, 2021
GRChombo is an open-source code for performing Numerical Relativity time evolutions, built on top of the publicly available Chombo software for the solution of PDEs. Whilst GRChombo uses standard techniques in NR, it focusses on applications in theor
Externí odkaz:
http://arxiv.org/abs/2201.03458
Autor:
Delétang, Grégoire, Grau-Moya, Jordi, Kunesch, Markus, Genewein, Tim, Brekelmans, Rob, Legg, Shane, Ortega, Pedro A.
We extend temporal-difference (TD) learning in order to obtain risk-sensitive, model-free reinforcement learning algorithms. This extension can be regarded as modification of the Rescorla-Wagner rule, where the (sigmoidal) stimulus is taken to be eit
Externí odkaz:
http://arxiv.org/abs/2111.02907
Autor:
Ortega, Pedro A., Kunesch, Markus, Delétang, Grégoire, Genewein, Tim, Grau-Moya, Jordi, Veness, Joel, Buchli, Jonas, Degrave, Jonas, Piot, Bilal, Perolat, Julien, Everitt, Tom, Tallec, Corentin, Parisotto, Emilio, Erez, Tom, Chen, Yutian, Reed, Scott, Hutter, Marcus, de Freitas, Nando, Legg, Shane
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains. One important problem class that has remained relatively elusive h
Externí odkaz:
http://arxiv.org/abs/2110.10819
Autor:
Déletang, Grégoire, Grau-Moya, Jordi, Martic, Miljan, Genewein, Tim, McGrath, Tom, Mikulik, Vladimir, Kunesch, Markus, Legg, Shane, Ortega, Pedro A.
As machine learning systems become more powerful they also become increasingly unpredictable and opaque. Yet, finding human-understandable explanations of how they work is essential for their safe deployment. This technical report illustrates a metho
Externí odkaz:
http://arxiv.org/abs/2103.03938
The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex. Unique challenges arise when a model's input features become high dimensional: on one ha
Externí odkaz:
http://arxiv.org/abs/2010.07384
Autor:
Kunesch, Markus
General relativity, one of the pillars of our understanding of the universe, has been a remarkably successful theory. It has stood the test of time for more than 100 years and has passed all experimental tests so far. Most recently, the LIGO collabor
Externí odkaz:
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763593