Pacheco, Jason

15 publications

ICLR 2025 Flow-Based Variational Mutual Information: Fast and Flexible Approximations Caleb Dahlke, Jason Pacheco

NeurIPS 2025 Reverse-Annealed Sequential Monte Carlo for Efficient Bayesian Optimal Experiment Design Jake Callahan, Andrew Chin, Jason Pacheco, Tommie Catanach

ICLR 2025 Risk-Sensitive Variational Actor-Critic: A Model-Based Approach Alonso Granados, Reza Ebrahimi, Jason Pacheco

NeurIPS 2024 Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or Without Replacement Jeremiah Birrell, Reza Ebrahimi, Rouzbeh Behnia, Jason Pacheco

AISTATS 2024 Efficient Variational Sequential Information Control Jianwei Shen, Jason Pacheco

NeurIPSW 2023 Efficient Variational Sequential Information Control Jianwei Shen, Jason Pacheco

AISTATS 2023 Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models Caleb Dahlke, Sue Zheng, Jason Pacheco

NeurIPS 2023 On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy Caleb Dahlke, Jason Pacheco

NeurIPS 2020 Sequential Bayesian Experimental Design with Variable Cost Structure Sue Zheng, David Hayden, Jason Pacheco, John W. Fisher Iii

AISTATS 2019 Variational Information Planning for Sequential Decision Making Jason Pacheco, John Fisher

ICML 2018 A Robust Approach to Sequential Information Theoretic Planning Sue Zheng, Jason Pacheco, John Fisher

NeurIPS 2017 Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D Simeral, Beata Jarosiewicz, Erik Sudderth

ICML 2015 Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach Jason Pacheco, Erik Sudderth

ICML 2014 Preserving Modes and Messages via Diverse Particle Selection Jason Pacheco, Silvia Zuffi, Michael Black, Erik Sudderth

NeurIPS 2012 Minimization of Continuous Bethe Approximations: A Positive Variation Jason Pacheco, Erik B. Sudderth