All Publications

Brian Jalaian, Ph.D., Meet Vadera, Jinyang Li, Adam Cobb, Tarek Abdelzaher, Benjamin Marlin (2022). URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference Methods. Proceedings of Machine Learning and Systems.

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Yushun Dong, Ninghao Liu, Brian Jalaian, Jundong Li (2022). EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks. Proceedings of the ACM Web Conference 2022.

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Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin (2022). Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning. arXiv:2202.03770 [cs].

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Anirban Roy, Adam Cobb, Nathaniel D Bastian, Brian Jalaian, Susmit Jha (2022). Runtime Monitoring of Deep Neural Networks Using Top-Down Context Models Inspired by Predictive Processing and Dual Process Theory. AAAI.

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Adam D. Cobb, Brian Jalaian (2021). Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting. In UAI 2021.

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Adam D. Cobb, Brian A. Jalaian, Nathaniel D. Bastian, Stephen Russell (2021). Robust Decision-Making in the Internet of Battlefield Things Using Bayesian Neural Networks. 2021 Winter Simulation Conference (WSC).

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Shaoran Li, Chengzhang Li, Yan Huang, Brian A. Jalaian, Y. Thomas Hou, Wenjing Lou (2021). Task Offloading with Uncertain Processing Cycles. Proceedings of the Twenty-second International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing.

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Subhodip Biswas, Debanjan Saha, Shuvodeep De, Adam D Cobb, Swagatam Das, Brian a Jalaian (2021). Improving Differential Evolution through Bayesian Hyperparameter Optimization. 2021 IEEE Congress on Evolutionary Computation (CEC).

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Susmit Jha, Brian Jalaian, Anirban Roy, Gunjan Verma (2021). Trinity: Trust, Resilience and Interpretability of Machine Learning Models. Game Theory and Machine Learning for Cyber Security.

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Adam D Cobb, Brian Jalaian, Nathaniel D Bastian, Stephen Russell (2021). Toward Safe Decision-Making via Uncertainty Quantification in Machine Learning. Systems Engineering and Artificial Intelligence.

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