Below is a list of my publications. In all likelihood, it’s a bit out of date. For the most up to date list, please check my Google Scholar profile.

In the below lists, an asterisk (*) denotes equal contribution with alphabetical or randomized ordering.

preprints

  1. Nicholas M. Boffi*, Michael S. Albergo*, and Eric Vanden-Eijnden. “Flow map matching.” arXiv:2406.07507 (2024). arxiv
  2. Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, and Saining Xie. “SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.” arXiv:2401.08740 (2024). arxiv
  3. Michael S. Albergo*, N. M. Boffi*, and Eric Vanden-Eijnden. “Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.” arXiv:2303.08797 (2023). arXiv

publications

  1. Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden. “Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes.” International Conference on Machine Learning (2024). arxiv
  2. Michael S. Albergo, Mark Goldstein, N. M. Boffi, Rajesh Ranganath, and Eric Vanden-Eijnden. “Stochastic interpolants with data-dependent couplings.” International Conference on Machine Learning (spotlight) (2024). arXiv
  3. N. M. Boffi and Eric Vanden-Eijnden. “Deep learning probability flows and entropy production rates in active matter.” Proceedings of the National Academy of Sciences 121 (25) e2318106121, 2024. arXiv / journal
  4. Michael S. Albergo, N. M. Boffi, Michael Lindsey, and Eric Vanden-Eijnden. “Multimarginal generative modeling with stochastic interpolants.” International Conference on Learning Representations (2024). arXiv
  5. N. M. Boffi and Eric Vanden-Eijnden. “Probability flow solution of the Fokker-Planck equation,” Machine Learning: Science and Technology (2023). journal / arXiv
  6. N. M. Boffi, Yipei Guo, Chris H. Rycroft, Ariel Amir. “How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution,” eLife 12 (2023). journal / biorXiv
  7. Saminda Abeyruwan, Alex Bewley, N. M. Boffi, Krzysztof Marcin Choromanski, David B D’Ambrosio, Deepali Jain, Pannag R Sanketi, Anish Shankar, Vikas Sindhwani, Sumeet Singh, Jean-Jacques Slotine, Stephen Tu. “Agile Catching with Whole-Body MPC and Blackbox Policy Learning,” Learning for Dynamics and Control (L4DC) 2023. conference
  8. N. M. Boffi*, Stephen Tu*, and Jean-Jacques Slotine. “Non-parametric adaptive control and prediction: theory and randomized algorithms,” Journal of Machine Learning Research 23 (2022) 1-46. journal / conference / arXiv
  9. Thomas Zhang, Stephen Tu, N. M. Boffi, Jean-Jacques Slotine, and Nikolai Matni. “Adversarially robust stability certificates can be sample efficient,” Learning for Dynamics and Control (L4DC 2022). conference / arXiv
  10. N. M. Boffi*, Stephen Tu*, Jean-Jacques E. Slotine, “The role of optimization geometry in single neuron learning,” International Conference on Artificial Intelligence and Statistics (2022). conference / arXiv
  11. Katiana Kontolati, Darius Alix-Williams, N. M. Boffi, Michael L. Falk, Chris H. Rycroft, and Michael D. Shields. “Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids,” Acta Materialia, 215:1170008 (2021). journal / arxiv
  12. N. M. Boffi*, Stephen Tu*, Jean-Jacques E. Slotine, “Regret bounds for adaptive nonlinear control,” Learning for Dynamics and Control (L4DC 2021). selected for oral presentation. conference / arXiv
  13. N. M. Boffi*, Stephen Tu*, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani, “Learning stability certificates from data,” Conference on Robot Learning (CoRL) 2020. conference / arXiv
  14. N. M. Boffi, Jean-Jacques E. Slotine, “Implicit regularization and momentum algorithms in nonlinearly parameterized adaptive control and prediction,” Neural Computation, 33(3):590-673, 03 2021. featured on the cover. journal / arXiv
  15. N. M. Boffi, Chris H. Rycroft, “Coordinate transformation methodology for simulating quasi-static elastoplastic solids,” Physical Review E 101, 053304 (2020). journal / arXiv
  16. N. M. Boffi, Chris H. Rycroft, “Parallel three-dimensional simulations of quasi-static elastoplastic solids,” Computer Physics Communications 257, 107254 (2020). journal / arXiv
  17. N. M. Boffi, Jean-Jacques E. Slotine, “A continuous-time analysis of distributed stochastic gradient,” Neural Computation 32, 36-96 (2020). journal / arXiv
  18. N. M. Boffi, Manish Jain, Amir Natan, “Efficient computation of the Hartree-Fock Exchange in real-space with projection operators,” Journal of Chemical Theory and Computation 12, (8) (2016). journal
  19. N. M. Boffi, Manish Jain, Amir Natan, “Asymptotic behavior and interpretation of virtual states: the effects of confinement and of basis sets,” Journal of Chemical Physics 144, 084104 (2016). journal
  20. N. M. Boffi, Judith C. Hill, Matthew G. Reuter, “Characterizing the inverses of block tridiagonal, block Toeplitz matrices,” Computational Science and Discovery 8, 015001 (2015). journal
  21. Matthew G. Reuter, N. M. Boffi, Mark A. Ratner, Tamar Seideman, “The role of dimensionality in the decay of surface effects,” Journal of Chemical Physics 138, 084707 (2013). journal