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pro vyhledávání: '"Pásztor, Barna"'
Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopolistic industries with a history of collusion, calls for closer examina
Externí odkaz:
http://arxiv.org/abs/2410.18871
Bandits with preference feedback present a powerful tool for optimizing unknown target functions when only pairwise comparisons are allowed instead of direct value queries. This model allows for incorporating human feedback into online inference and
Externí odkaz:
http://arxiv.org/abs/2406.16745
In various applications, the optimal policy in a strategic decision-making problem depends both on the environmental configuration and exogenous events. For these settings, we introduce Bilevel Optimization with Contextual Markov Decision Processes (
Externí odkaz:
http://arxiv.org/abs/2406.01575
Autor:
Jusup, Matej, Pásztor, Barna, Janik, Tadeusz, Zhang, Kenan, Corman, Francesco, Krause, Andreas, Bogunovic, Ilija
Many applications, e.g., in shared mobility, require coordinating a large number of agents. Mean-field reinforcement learning addresses the resulting scalability challenge by optimizing the policy of a representative agent interacting with the infini
Externí odkaz:
http://arxiv.org/abs/2306.17052
Publikováno v:
P\'asztor, B., Krause, A., & Bogunovic, I. (2023). Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning. Transactions on Machine Learning Research
Learning in multi-agent systems is highly challenging due to several factors including the non-stationarity introduced by agents' interactions and the combinatorial nature of their state and action spaces. In particular, we consider the Mean-Field Co
Externí odkaz:
http://arxiv.org/abs/2107.04050
We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a nonprofit organizati
Externí odkaz:
http://arxiv.org/abs/2010.12002
Stress minimization is among the best studied force-directed graph layout methods because it reliably yields high-quality layouts. It thus comes as a surprise that a novel approach based on stochastic gradient descent (Zheng, Pawar and Goodman, TVCG
Externí odkaz:
http://arxiv.org/abs/2008.10376
Akademický článek
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We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a non-profit organizat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1138239507e794bcaa1f9614e0e8380a