Research
Below is a list of my research articles and book chapters, where the symbol * denotes equal contribution. You can also find my research on Google Scholar.
Submitted articles
D. Le*, N. Bariletto*, A. Rinaldo, and N. Ho (2026). “Partial Differential Equation Barriers to Identifiability in Infinite Mixture Models.” arXiv preprint arXiv:2608.08597. [link]
N. Bariletto and S. G. Walker (2026). “On the Geometry of Weak Convergence Without Total Variation Convergence.” arXiv preprint arXiv:2608.03290. [link]
N. Bariletto*, D. Le*, A. Rinaldo, and N. Ho (2026). “Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models.” arXiv preprint arXiv:2605.06845. [link]
N. Bariletto and S. G. Walker (2026). “Scalable Posterior Uncertainty for Flexible Density-Based Clustering.” arXiv preprint arXiv:2603.03188. [link]
N. Bariletto*, H. Nguyen*, N. Ho, and A. Rinaldo (2026). “On Bayesian Softmax-Gated Mixture-of-Experts Models.” arXiv preprint arXiv:2604.20551. [link]
N. Bariletto, N. Ho, and A. Rinaldo (2025). “Conformalized Bayesian Inference, with Applications to Random Partition Models.” arXiv preprint arXiv:2511.05746. [link] — Major revision at Bayesian Analysis
N. Bariletto and S. G. Walker (2025). “On a Necessary Condition for Posterior Inconsistency: New Insights from a Classic Counterexample.” arXiv preprint arXiv:2510.18126. [link] — Minor revision at the Journal of Nonparametric Statistics
N. Bariletto, B. Flores, and S. G. Walker (2025). “Posterior Consistency in Parametric Models via a Tighter Notion of Identifiability.” arXiv preprint arXiv:2504.11360. [link] — Invited resubmission at Statistical Science
Published articles
S. Cremaschi, N. Bariletto, and C. E. De Vries (2025). “Without Roots: The Political Consequences of Collective Economic Shocks.” American Political Science Review, 119(4):1963–1982. [link]
N. Bariletto and N. Ho (2024). “Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization.” Advances in Neural Information Processing Systems (NeurIPS 2024), 38. [link]
K. Nguyen, N. Bariletto, and N. Ho (2024). “Quasi-Monte Carlo for 3D Sliced Wasserstein.” International Conference on Learning Representations (ICLR 2024, Spotlight), 12. [link]
S. Siwakoti, K. Yadav, I. Thange, N. Bariletto, L. Zanotti, A. Ghoneim, and J. N. Shapiro (2021). “Localized Misinformation in a Global Pandemic: Report on COVID-19 Narratives Around the World.” Empirical Studies of Conflict Project, Princeton, NJ: Princeton University. [link]
S. Siwakoti, K. Yadav, N. Bariletto, L. Zanotti, U. Erdogdu, and J. N. Shapiro (2021). “How COVID Drove the Evolution of Fact-Checking.” Harvard Kennedy School Misinformation Review. [link]
I. Thange, N. Bariletto, L. Zanotti, J. Rob, S. Siwakoti, and J. N. Shapiro (2020). “How Russia, China, and Other Governments Use Coronavirus Disinformation to Reshape Geopolitics.” Bulletin of the Atomic Scientists. [link]
Preprints
- N. Bariletto, K. Nguyen, and N. Ho (2024). “Data-Driven DRO and Economic Decision Theory: An Analytical Synthesis with Bayesian Nonparametric Advancements.” arXiv preprint arXiv:2405.13160. [link]
Book chapters
- Agostinelli, C., Aitchison, L., Aldea, E., Allmendinger, R., Ament, S., Anson, B., Arbel, J., Bakshy, E., Balandat, M., Bariletto, N., Bickford Smith, F., Biggio, B., Caprio, M., Chada, N., Chen, W., Chen, W., Da Costa, N., Daheim, N., Damianou, A., … Zhou, Y. (2026). “Handbook of Bayesian Deep Learning.” Zenodo. [link]