Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra. - Ajit P. Singh; John T. Halloran; Jeff A. Bilmes; Katrin Kirchhoff; William Stafford Noble (UAI, 2012)

Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. - John T. Halloran; Jeff A. Bilmes; William Stafford Noble (UAI, 2014)

Faster and more accurate graphical model identification of tandem mass spectra using trellises. - Shengjie Wang; John T. Halloran; Jeff A. Bilmes; William S. Noble (Bioinform., 2016)

Analyzing Tandem Mass Spectra: A Graphical Models Perspective. - John T. Halloran (AMBN, 2017)

Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra. - John T. Halloran; David M. Rocke (NIPS, 2017)

Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra. - John T. Halloran; David M. Rocke (NeurIPS, 2018)

Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization. - Rishabh K. Iyer; John T. Halloran; Kai Wei (CoRR, 2018)

GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification. - John T. Halloran; David M. Rocke (NeurIPS, 2020)

Mamba State-Space Models Can Be Strong Downstream Learners. - John T. Halloran; Manbir S. Gulati; Paul F. Roysdon (CoRR, 2024)

Mamba State-Space Models Are Lyapunov-Stable Learners. - John T. Halloran; Manbir S. Gulati; Paul F. Roysdon (Trans. Mach. Learn. Res., 2025)