Publications


Binary Black-Box Attacks Against Static Malware Detectors with Reinforcement Learning in Discrete Action Space
E. Mohammadreza, J. Pacheco, W. Li, J. Lee Hu, H. Chen
IEEE S&P Deep Learning and Security Workshop, May 2021
( paper )

Lightweight Data Fusion with Conjugate Mappings
C. L. Dean, S. J. Lee, J. Pacheco, J. Fisher III
Arxiv.org, Nov 2020
( paper )

Sequential Bayesian Experimental Design with Variable Cost Structure
S. Zheng, D. S. Hayden, J. Pacheco, J. Fisher III
Advances in Neural Information Processing Systems (NeurIPS), Dec 2020
( paper )   ( supplemental )

Nonparametric Object and Parts Modeling with Lie Group Dynamics
D. S. Hayden, J. Pacheco, J. Fisher III
Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
Oral Presentation
( paper )   ( supplemental )

How vision governs the collective behaviour of dense cycling pelotons
J. Belden, M. M. Mansoor, A. Hellum, S. R. Rahman, A. Meyer, C. Pease, J. Pacheco, S. Koziol and T. T. Truscott
Journal of the Royal Society Interface, July 2019
( paper )   ( phys.org article )

Variational Information Planning for Sequential Decision Making
J. Pacheco and J. Fisher III
International Conference on AI and Statistics (AISTATS), April 2019
( paper )

A Robust Approach to Sequential Information Theoretic Planning
S. Zheng, J. Pacheco, J. Fisher III
International Conference on Machine Learning (ICML), July 2018
( paper )

Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
D. Milstein, J. Pacheco, L. Hochberg, J. Simeral, B. Jarosiewicz, E. Sudderth
Advances in Neural Information Processing Systems (NIPS), Dec. 2017
( paper )   ( video summary )

Variational Approximations with Diverse Applications
Ph.D. Thesis, Dept. of Computer Science, Brown University, Apr. 2016
( paper )

Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
J. Pacheco and E. B. Sudderth
International Conference on Machine Learning (ICML), Jul. 2015
( paper )   ( talk slides )   ( talk video )   ( code )

Preserving Modes and Messages via Diverse Particle Selection
J. Pacheco, S. Zuffi, M. J. Black and E. B. Sudderth
International Conference on Machine Learning (ICML), Jun. 2014
( paper )   (supplement )   ( talk slides )   ( talk video )

Minimization of continuous Bethe approximations: A positive variation
J. Pacheco and E. B. Sudderth
Advances in Neural Information Processing Systems (NIPS), Dec. 2012
( paper )   ( supplement )

Improved variational inference for tracking in clutter
J. Pacheco and E. Sudderth
IEEE Statistical Signal Processing, Aug. 2012
( paper )

Max-product particle Belief Propagation
R. Kothapa, J. Pacheco and E. Sudderth
Technical Report, Brown University, May. 2011
( paper )

© Jason Pacheco, 2019