Multiple Alignment of Continuous Time Series. - Jennifer Listgarten; Radford M. Neal; Sam T. Roweis; Andrew Emili (NIPS, 2004)

Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure. - Jennifer Listgarten; Radford M. Neal; Sam T. Roweis; Rachel Puckrin; Sean Cutler (NIPS, 2006)

Determining the Number of Non-Spurious Arcs in a Learned DAG Model: Investigation of a Bayesian and a Frequentist Approach. - Jennifer Listgarten; David Heckerman (UAI, 2007)

Difference detection in LC-MS data for protein biomarker discovery. - Jennifer Listgarten; Radford M. Neal; Sam T. Roweis; Peter Wong; Andrew Emili (Bioinform., 2007)

Leveraging Information Across HLA Alleles/Supertypes Improves Epitope Prediction. - David Heckerman; Carl Myers Kadie; Jennifer Listgarten (J. Comput. Biol., 2007)

A Statistical Framework for Modeling HLA-Dependent T Cell Response Data. - Jennifer Listgarten; Nicole Frahm; Carl Myers Kadie; Christian Brander; David Heckerman (PLoS Comput. Biol., 2007)

Analysis of sibling time series data: Alignment and difference detection. - Jennifer Listgarten (2007)

Statistical Resolution of Ambiguous HLA Typing Data. - Jennifer Listgarten; Zabrina L. Brumme; Carl Myers Kadie; Gao Xiaojiang; Bruce D. Walker; Mary Carrington; Philip J. R. Goulder; David Heckerman (PLoS Comput. Biol., 2008)

Correction for Hidden Confounders in the Genetic Analysis of Gene Expression (Abstract). - Jennifer Listgarten; Carl Myers Kadie; Eric E. Schadt; David Heckerman (UAI, 2011)

Session introduction. - Oliver Stegle; Frederick P. Roth; Quaid Morris; Jennifer Listgarten (Pacific Symposium on Biocomputing, 2012)

The Future of Genome-Based Medicine. - Quaid Morris; Steven E. Brenner; Jennifer Listgarten; Oliver Stegle (Pacific Symposium on Biocomputing, 2013)

Session introduction. - Oliver Stegle; Steven E. Brenner; Quaid Morris; Jennifer Listgarten (Pacific Symposium on Biocomputing, 2013)

A powerful and efficient set test for genetic markers that handles confounders. - Jennifer Listgarten; Christoph Lippert; Eun Yong Kang; Jing Xiang; Carl Myers Kadie; David Heckerman (Bioinform., 2013)

Session introduction. - Jennifer Listgarten; Oliver Stegle; Quaid Morris; Steven E. Brenner; Leopold Parts (Pacific Symposium on Biocomputing, 2014)

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. - Christoph Lippert; Jing Xiang; Danilo Horta; Christian Widmer; Carl Myers Kadie; David Heckerman; Jennifer Listgarten (Bioinform., 2014)

Session introduction. - Joel T. Dudley; Jennifer Listgarten; Oliver Stegle; Steven E. Brenner; Leopold Parts (Pacific Symposium on Biocomputing, 2015)

Flexible Modelling of Genetic Effects on Function-Valued Traits. - Nicoló Fusi; Jennifer Listgarten (RECOMB, 2016)

Flexible Modeling of Genetic Effects on Function-Valued Traits. - Nicoló Fusi; Jennifer Listgarten (J. Comput. Biol., 2017)

Gaussian Process Prior Variational Autoencoders. - Francesco Paolo Casale; Adrian V. Dalca; Luca Saglietti; Jennifer Listgarten; Nicoló Fusi (NeurIPS, 2018)

Design by adaptive sampling. - David H. Brookes; Jennifer Listgarten (CoRR, 2018)

Conditioning by adaptive sampling for robust design. - David H. Brookes; Hahnbeom Park; Jennifer Listgarten (ICML, 2019)

A view of estimation of distribution algorithms through the lens of expectation-maximization. - David H. Brookes; Akosua Busia; Clara Fannjiang; Kevin Murphy 002; Jennifer Listgarten (GECCO Companion, 2020)

Autofocused oracles for model-based design. - Clara Fannjiang; Jennifer Listgarten (NeurIPS, 2020)

Conformal prediction for the design problem. - Clara Fannjiang; Stephen Bates; Anastasios N. Angelopoulos; Jennifer Listgarten; Michael I. Jordan (CoRR, 2022)

Augmenting Neural Networks with Priors on Function Values. - Hunter Nisonoff; Yixin Wang; Jennifer Listgarten (CoRR, 2022)

Is novelty predictable? - Clara Fannjiang; Jennifer Listgarten (CoRR, 2023)

The perpetual motion machine of AI-generated data and the distraction of ChatGPT-as-scientist. - Jennifer Listgarten (CoRR, 2023)

Machine Learning for Protein-Protein and Protein-Ligand Interactions (Dagstuhl Seminar 24451). - Anne-Florence Bitbol; Jennifer Listgarten; Tomás Pluskal; Anton Bushuiev; Roman Bushuiev (Dagstuhl Reports, 2024)

Unlocking Guidance for Discrete State-Space Diffusion and Flow Models. - Hunter Nisonoff; Junhao Xiong; Stephan Allenspach; Jennifer Listgarten (ICLR, 2025)

Guide your favorite protein sequence generative model. - Junhao Xiong; Hunter Nisonoff; Maria Lukarska; Ishan Gaur; Luke M. Oltrogge; David F. Savage; Jennifer Listgarten (CoRR, 2025)