preprints

* denotes equal contribution with alphabetical ordering.

  1. Michael S. Albergo, Mark Goldstein, N. M. Boffi, Rajesh Ranganath, and Eric Vanden-Eijnden. “Stochastic interpolants with data-dependent couplings.” arXiv:2310.03725, 2023. arXiv
  2. Michael S. Albergo, N. M. Boffi, Michael Lindsey, and Eric Vanden-Eijnden. “Multimarginal generative modeling with stochastic interpolants.” arXiv:2310.03695, 2023. arXiv
  3. N. M. Boffi and Eric Vanden-Eijnden. “Deep learning probability flows and entropy production rates in active matter.” arXiv:2309.12991, 2023. arXiv
  4. 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. N. M. Boffi and Eric Vanden-Eijnden. “Probability flow solution of the Fokker-Planck equation,” Machine Learning: Science and Technology (2023). journal / arXiv
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. N. M. Boffi, Chris H. Rycroft, “Coordinate transformation methodology for simulating quasi-static elastoplastic solids,” Physical Review E 101, 053304 (2020). journal / arXiv
  12. N. M. Boffi, Chris H. Rycroft, “Parallel three-dimensional simulations of quasi-static elastoplastic solids,” Computer Physics Communications 257, 107254 (2020). journal / arXiv
  13. N. M. Boffi, Jean-Jacques E. Slotine, “A continuous-time analysis of distributed stochastic gradient,” Neural Computation 32, 36-96 (2020). journal / arXiv
  14. 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
  15. 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
  16. 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
  17. 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