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ORCIDJason L. Pacheco
Person information
- affiliation: University of Arizona, Tucson, AZ, USA
- affiliation (former): MIT CSAIL, MA, USA
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2020 – today
- 2023
[j1]Xiwen Chen
, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head, Alonso Granados Baca:
Network-Level Safety Metrics for Overall Traffic Safety Assessment: A Case Study. IEEE Access 11: 17755-17778 (2023)- [c16]Caleb Dahlke, Sue Zheng, Jason Pacheco:
Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models. AISTATS 2023: 10262-10278 - [c15]Caleb Dahlke, Jason Pacheco:
On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy. NeurIPS 2023 - 2022
- [c14]Rouzbeh Behnia, MohammadReza Ebrahimi, Jason Pacheco, Balaji Padmanabhan:
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy. ICDM (Workshops) 2022: 560-566 - [c13]MohammadReza Ebrahimi, Weifeng Li, Yidong Chai, Jason Pacheco, Hsinchun Chen:
An Adversarial Reinforcement Learning Framework for Robust Machine Learning-based Malware Detection. ICDM (Workshops) 2022: 567-576 - [i3]Xiwen Chen, Hao Wang, Abolfazl Razi, Brendan Russo, Jason Pacheco, John Roberts, Jeffrey Wishart, Larry Head:
Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study. CoRR abs/2201.13229 (2022) - [i2]Rouzbeh Behnia, Mohammadreza Ebrahimi, Jason Pacheco, Balaji Padmanabhan:
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy. CoRR abs/2210.15042 (2022) - 2021
- [c12]Mohammadreza Ebrahimi, Jason Pacheco, Weifeng Li, James Lee Hu, Hsinchun Chen:
Binary Black-Box Attacks Against Static Malware Detectors with Reinforcement Learning in Discrete Action Spaces. SP (Workshops) 2021: 85-91 - 2020
- [c11]David S. Hayden, Jason Pacheco, John W. Fisher III:
Nonparametric Object and Parts Modeling With Lie Group Dynamics. CVPR 2020: 7424-7433 - [c10]Sue Zheng, David S. Hayden, Jason Pacheco, John W. Fisher III:
Sequential Bayesian Experimental Design with Variable Cost Structure. NeurIPS 2020 - [i1]Christopher L. Dean, Stephen J. Lee, Jason Pacheco, John W. Fisher III:
Lightweight Data Fusion with Conjugate Mappings. CoRR abs/2011.10607 (2020)
2010 – 2019
- 2019
- [c9]Jason Pacheco, John W. Fisher III:
Variational Information Planning for Sequential Decision Making. AISTATS 2019: 2028-2036 - 2018
- [c8]Sue Zheng, Jason Pacheco, John W. Fisher III:
A Robust Approach to Sequential Information Theoretic Planning. ICML 2018: 5936-5944 - 2017
- [c7]Daniel Milstein, Jason Pacheco, Leigh J. Hochberg, John D. Simeral, Beata Jarosiewicz, Erik B. Sudderth:
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces. NIPS 2017: 868-878 - 2016
- [b1]Jason L. Pacheco:
Variational Approximations with Diverse Applications. Brown University, USA, 2016 - 2015
- [c6]Jason Pacheco, Erik B. Sudderth:
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach. ICML 2015: 2200-2208 - 2014
- [c5]Jason Pacheco, Silvia Zuffi, Michael J. Black, Erik B. Sudderth:
Preserving Modes and Messages via Diverse Particle Selection. ICML 2014: 1152-1160 - 2012
- [c4]Jason L. Pacheco, Erik B. Sudderth:
Minimization of Continuous Bethe Approximations: A Positive Variation. NIPS 2012: 2573-2581 - [c3]Jason L. Pacheco, Erik B. Sudderth:
Improved variational inference for tracking in clutter. SSP 2012: 852-855 - 2011
- [c2]Christian G. Hempel, Tod Luginbuhl, Jason Pacheco:
Performance analysis of Adaptive Probabilistic Multi-hypothesis Tracking with the Metron data sets. FUSION 2011: 1-5
2000 – 2009
- 2009
- [c1]Christian G. Hempel, Jason Pacheco:
Performance analysis of the Probabilistic Multi-hypothesis Tracking algorithm on the SEABAR data sets. FUSION 2009: 1830-1836
Coauthor Index
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