Jos Bento Ayres Pereira

Boston College

Computer Science Department

Email: jose.bento at bc dot edu

Address: Maloney Hall,

21 Campanella Way,

Chestnut Hill, MA 02467

Curriculum Vitae

Publications


J. Bento, N. Derbinsky, C. Mathy, J. Yedidia, Proximal operators for multi-agent path planning, AAAI 2015

C. Mathy, N. Derbinsky, J. Bento, J. Rosenthal, J. Yedidia, The BF algorithm for online supervised and unsupervised learning, AAAI 2015

D. Krishnan, B. Freeman, J. Bento, D. Zoran, Shape and Illumination from Shading using the Generic Viewpoint Assumption, NIPS 2014

J. Bento, N. Derbinsky, J. Mora, J. Yedidia, A Message-Passing Algorithm for Multi-Agent Trajectory Planning, NIPS 2013, [Video]

N. Derbinsky, J. Bento, J. Yedidia, Integrating Knowledge with the TWA for Hybrid Cognitive Processing, AAAI 2013

J. Bento, M. Ibrahimi, Support Recovery for the Drift Coefficient of High-Dimensional Diffusions, IEEE IT 2013, [Video]

J. Bento, S. Ioannidis, S. Muthu., J. Yan, A time and space efficient algorithm for contextual linear bandits, ECML 2013

N. Derbinsky, J. Bento, V. Elser, J. Yedidia, An improved three-weight message-passing algorithm, 2013, [Video]

J. Bento, Learning graphical models, fundamental limits and efficient algorithms, PhD Thesis 2012

N. Damera, J. Bento, Ad Insertion in Automatically Composed Documents, DocEng 2012

J. Bento, A. Montanari, On the trade-off between complexity and correlation decay in structural learning algorithms, 2011

J. Bento, N. Fawaz, A. Montanari, S. Ioannidis, Identifying users from their rating patterns, RecSys 2011

N. Damera, J. Bento, E. O'Brien, Probabilistic document model, DocEng 2011

J. Bento, M. Ibrahimi, A. Montanari, Information theoretic limits on learning stochastic differential equations, ISIT 2011

M. Bayati, J. Bento, A. Montanari, The LASSO Risk: asymptotic results and real world examples, NIPS 2010

J. Bento, M. Ibrahimi, A. Montanari, Learning networks of stochastic differential equations, NIPS 2010

J. Bento, A. Montanari, Which graphical models are difficult to learn?, NIPS 2009