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ICML
2008
IEEE
14 years 10 months ago
Laplace maximum margin Markov networks
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
Jun Zhu, Eric P. Xing, Bo Zhang
RECOMB
2004
Springer
14 years 10 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven
MINENET
2005
ACM
14 years 3 months ago
Shrink: a tool for failure diagnosis in IP networks
Faults in an IP network have various causes such as the failure of one or more routers at the IP layer, fiber-cuts, failure of physical elements at the optical layer, or extraneo...
Srikanth Kandula, Dina Katabi, Jean-Philippe Vasse...
NIPS
2007
13 years 11 months ago
Computing Robust Counter-Strategies
Adaptation to other initially unknown agents often requires computing an effective counter-strategy. In the Bayesian paradigm, one must find a good counterstrategy to the inferre...
Michael Johanson, Martin Zinkevich, Michael H. Bow...
UAI
1998
13 years 11 months ago
Tractable Inference for Complex Stochastic Processes
The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system,...
Xavier Boyen, Daphne Koller