 | 2011 |
| 12 |  | Balázs Csanád Csáji,
Erik Weyer:
System identification with binary observations by stochastic approximation and active learning.
CDC-ECE 2011: 3634-3639 |
| 2010 |
| 11 |  | Balázs Csanád Csáji,
Raphaël M. Jungers,
Vincent D. Blondel:
PageRank Optimization in Polynomial Time by Stochastic Shortest Path Reformulation.
ALT 2010: 89-103 |
| 10 |  | László Monostori,
Balázs Csanád Csáji,
Botond Kádár,
András Pfeiffer,
Elisabeta Ilie Zudor,
Zsolt Kemény,
Marcell Szathmari:
Towards adaptive and digital manufacturing.
Annual Reviews in Control 34(1): 118-128 (2010) |
| 2009 |
| 9 |  | Balázs Csanád Csáji,
Raphaël M. Jungers,
Vincent D. Blondel:
PageRank Optimization by Edge Selection
CoRR abs/0911.2280: (2009) |
| 2008 |
| 8 |  | Balázs Csanád Csáji,
László Monostori:
Adaptive Stochastic Resource Control: A Machine Learning Approach.
J. Artif. Intell. Res. (JAIR) 32: 453-486 (2008) |
| 7 |  | Balázs Csanád Csáji,
László Monostori:
Value Function Based Reinforcement Learning in Changing Markovian Environments.
Journal of Machine Learning Research 9: 1679-1709 (2008) |
| 2006 |
| 6 |  | Balázs Csanád Csáji,
László Monostori:
Adaptive Sampling Based Large-Scale Stochastic Resource Control.
AAAI 2006: 815-820 |
| 5 |  | Balázs Csanád Csáji,
László Monostori,
Botond Kádár:
Reinforcement learning in a distributed market-based production control system.
Advanced Engineering Informatics 20(3): 279-288 (2006) |
| 2005 |
| 4 |  | Balázs Csanád Csáji,
László Monostori:
Stochastic Reactive Production Scheduling by Multi-agent Based Asynchronous Approximate Dynamic Programming.
CEEMAS 2005: 388-397 |
| 2004 |
| 3 |  | Balázs Csanád Csáji,
Josef Küng,
Jürgen Palkoska,
Roland Wagner:
On the Automation of Similarity Information Maintenance in Flexible Query Answering Systems.
DEXA 2004: 130-140 |
| 2 |  | Marco Gillies,
Daniel Ballin,
Balázs Csanád Csáji:
Efficient Clothing Fitting from Data.
WSCG 2004: 129-136 |
| 2003 |
| 1 |  | Balázs Csanád Csáji,
Botond Kádár,
László Monostori:
Improving Multi-agent Based Scheduling by Neurodynamic Programming.
HoloMAS 2003: 110-123 |