Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Craig Boutilier"'
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:12287-12293
The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea90e942429028b92e201e8e4617696e
https://doi.org/10.1609/aaai.v36i11.21492
https://doi.org/10.1609/aaai.v36i11.21492
Autor:
Martin Mladenov, Sanjay Ganapathy Subramaniam, Chih-wei Hsu, Neha Arora, Andrew Tomkins, Craig Boutilier, Carolina Osorio
Publikováno v:
Proceedings of the 30th International Conference on Advances in Geographic Information Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5c81d6a8f11601f3a95483ce38d3ec1
https://doi.org/10.1145/3557915.3560940
https://doi.org/10.1145/3557915.3560940
Autor:
Craig Boutilier, Ariel Procaccia
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 26:1278-1284
Distance rationalizability is an intuitive paradigm for developing and studying voting rules: given a notion of consensus and a distance function on preference profiles, a rationalizable voting rule selects an alternative that is closest to being a c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a286bac398fecea04e056ca3db532674
https://doi.org/10.1609/aaai.v26i1.8240
https://doi.org/10.1609/aaai.v26i1.8240
Autor:
Christina Göpfert, Yinlam Chow, Chih-Wei Hsu, Ivan Vendrov, Tyler Lu, Deepak Ramachandran, Craig Boutilier
Publikováno v:
Proceedings of the ACM Web Conference 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::445dc05b70c989820d94617fe0cfdfa6
https://doi.org/10.1145/3485447.3512113
https://doi.org/10.1145/3485447.3512113
Publikováno v:
Artificial Intelligence. 275:174-203
Social networks play a central role in the transactions and decision making of individuals by correlating the behaviors and preferences of connected agents. We introduce a notion of empathy in social networks, in which individuals derive utility base
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1aea5f809add070476cfe6c07c73493b
https://doi.org/10.1016/j.artint.2019.05.004
https://doi.org/10.1016/j.artint.2019.05.004
Autor:
Ruohan Zhan, Konstantina Christakopoulou, Martin Mladenov, Ed H. Chi, Minmin Chen, Jayden Ooi, Ya Le, Alex Beutel, Craig Boutilier
Publikováno v:
WWW
Most existing recommender systems focus primarily on matching users (content consumers) to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content providers have a critical influence on user satisf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99b9dad025b05f801f21e2a14325a81c
http://arxiv.org/abs/2105.02377
http://arxiv.org/abs/2105.02377
Autor:
Craig Boutilier, Ivan Vendrov, Nicolas Mayoraz, Hubert Pham, Martin Mladenov, Vihan Jain, Eugene Ie, Dustin Tran, Christopher Colby, Hsu Chih-Wei
Publikováno v:
RecSys
We develop RecSim NG, a probabilistic platform that supports natural, concise specification and learning of models for multi-agent recommender systems simulation. RecSim NG is a scalable, modular, differentiable simulator implemented in Edward2 and T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d62225fca2ace3ba35eba7d59eb9e29b
https://doi.org/10.1145/3383313.3411527
https://doi.org/10.1145/3383313.3411527
Autor:
Craig Boutilier, Paolo Viappiani
Publikováno v:
Artificial Intelligence
Artificial Intelligence, Elsevier, 2020, 286, pp.103328. ⟨10.1016/j.artint.2020.103328⟩
Artificial Intelligence, Elsevier, 2020, 286, pp.103328. ⟨10.1016/j.artint.2020.103328⟩
International audience; Preference elicitation is an important component in many AI applications, including decision support and recommender systems. Such systems must assess user preferences, based on interactions with their users, and make recommen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90ee64f6f59d3b8afcc572874fb82645
https://hal.sorbonne-universite.fr/hal-02898192/file/aij_optset-final.pdf
https://hal.sorbonne-universite.fr/hal-02898192/file/aij_optset-final.pdf
Autor:
Honglak Lee, Yinlam Chow, Sungryull Sohn, Ofir Nachum, Ed H. Chi, Craig Boutilier, Jayden Ooi
Publikováno v:
IJCAI
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e.g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9225e491fb093b970d22bc4bbd20ce81
https://doi.org/10.24963/ijcai.2020/391
https://doi.org/10.24963/ijcai.2020/391
Publikováno v:
AAAI
Effective techniques for eliciting user preferences have taken on added importance as recommender systems (RSs) become increasingly interactive and conversational. A common and conceptually appealing Bayesian criterion for selecting queries is expect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36289bfaa6a985a32790151a1ae63f3c
http://arxiv.org/abs/1911.09153
http://arxiv.org/abs/1911.09153