Entropy-Constrained Scalar Quantization with a Lossy-Compressed Bit. - Melanie F. Pradier; Pablo M. Olmos; Fernando Pérez-Cruz (Entropy, 2016)

General Latent Feature Modeling for Data Exploration Tasks. - Isabel Valera; Melanie F. Pradier; Zoubin Ghahramani (CoRR, 2017)

Sparse Three-Parameter Restricted Indian Buffet Process for Understanding International Trade. - Melanie F. Pradier; Viktor Stojkoski; Zoran Utkovski; Lujupco Kocorev; Fernando Pérez-Cruz (ICASSP, 2018)

Sparse Three-parameter Restricted Indian Buffet Process for Understanding International Trade. - Melanie F. Pradier; Viktor Stojkoski; Zoran Utkovski; Ljupco Kocarev; Fernando Pérez-Cruz (CoRR, 2018)

Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights. - Melanie F. Pradier; Weiwei Pan; Jiayu Yao; Soumya Ghosh; Finale Doshi-Velez (CoRR, 2018)

Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. - Stefan G. Stark; Stephanie L. Hyland; Melanie Fernandes Pradier; Kjong-Van Lehmann; Andreas Wicki; Fernando Pérez-Cruz; Julia E. Vogt; Gunnar Rätsch (CoRR, 2019)

Output-Constrained Bayesian Neural Networks. - Wanqian Yang; Lars Lorch; Moritz A. Graule; Srivatsan Srinivasan; Anirudh Suresh; Jiayu Yao; Melanie F. Pradier; Finale Doshi-Velez (CoRR, 2019)

Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks. - Beau Coker; Melanie F. Pradier; Finale Doshi-Velez (CoRR, 2019)

PoRB-Nets: Poisson Process Radial Basis Function Networks. - Beau Coker; Melanie Fernandes Pradier; Finale Doshi-Velez (UAI, 2020)

General Latent Feature Models for Heterogeneous Datasets. - Isabel Valera; Melanie F. Pradier; Maria Lomeli; Zoubin Ghahramani (J. Mach. Learn. Res., 2020)

Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. - Maia L. Jacobs; Jeffrey He; Melanie F. Pradier; Barbara D. Lam; Andrew C. Ahn; Thomas H. McCoy; Roy H. Perlis; Finale Doshi-Velez; Krzysztof Z. Gajos (CHI, 2021)

Evaluating Approximate Inference in Bayesian Deep Learning. - Andrew Gordon Wilson; Pavel Izmailov; Matthew D. Hoffman; Yarin Gal; Yingzhen Li; Melanie F. Pradier; Sharad Vikram; Andrew Y. K. Foong; Sanae Lotfi; Sebastian Farquhar (NeurIPS (Competition and Demos), 2021)

Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible. - Melanie F. Pradier; Javier Zazo; Sonali Parbhoo; Roy H. Perlis; Maurizio Zazzi; Finale Doshi-Velez (CoRR, 2021)

Repairing Neural Networks by Leaving the Right Past Behind. - Ryutaro Tanno; Melanie F. Pradier; Aditya V. Nori; Yingzhen Li (NeurIPS, 2022)

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. - Melanie F. Pradier; Niranjani Prasad; Paidamoyo Chapfuwa; Sahra Ghalebikesabi; Maximilian Ilse; Steven Woodhouse; Rebecca Elyanow; Javier Zazo; Javier González Hernández; Julia Greissl; Edward Meeds (MLHC, 2023)

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. - Melanie F. Pradier; Niranjani Prasad; Paidamoyo Chapfuwa; Sahra Ghalebikesabi; Max Ilse; Steven Woodhouse; Rebecca Elyanow; Javier Zazo; Javier González Hernández; Julia Greissl; Edward Meeds (CoRR, 2023)

Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation. - Melanie F. Pradier; Javier González (CLeaR, 2025)