Higher-order algorithms for nonlinearly parameterized adaptive control. - Nicholas M. Boffi; Jean-Jacques E. Slotine (CoRR, 2019)

Learning Stability Certificates from Data. - Nicholas M. Boffi; Stephen Tu; Nikolai Matni; Jean-Jacques E. Slotine; Vikas Sindhwani (CoRL, 2020)

The Reflectron: Exploiting geometry for learning generalized linear models. - Nicholas M. Boffi; Jean-Jacques E. Slotine (CoRR, 2020)

Parallel three-dimensional simulations of quasi-static elastoplastic solids. - Nicholas M. Boffi; Chris H. Rycroft (Comput. Phys. Commun., 2020)

A Continuous-Time Analysis of Distributed Stochastic Gradient. - Nicholas M. Boffi; Jean-Jacques E. Slotine (Neural Comput., 2020)

Regret Bounds for Adaptive Nonlinear Control. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (L4DC, 2021)

Random features for adaptive nonlinear control and prediction. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (CoRR, 2021)

Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction. - Nicholas M. Boffi; Jean-Jacques E. Slotine (Neural Comput., 2021)

The role of optimization geometry in single neuron learning. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (AISTATS, 2022)

Adversarially Robust Stability Certificates can be Sample-Efficient. - Thomas T. C. K. Zhang; Stephen Tu; Nicholas M. Boffi; Jean-Jacques E. Slotine; Nikolai Matni (L4DC, 2022)

Nonparametric adaptive control and prediction: theory and randomized algorithms. - Nicholas M. Boffi; Stephen Tu; Jean-Jacques E. Slotine (J. Mach. Learn. Res., 2022)

Agile Catching with Whole-Body MPC and Blackbox Policy Learning. - Saminda Abeyruwan; Alex Bewley; Nicholas Matthew Boffi; Krzysztof Marcin Choromanski; David B. D'Ambrosio; Deepali Jain; Pannag R. Sanketi; Anish Shankar; Vikas Sindhwani; Sumeet Singh; Jean-Jacques E. Slotine; Stephen Tu (L4DC, 2023)

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. - Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2023)

Deep learning probability flows and entropy production rates in active matter. - Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2023)

Probability flow solution of the Fokker-Planck equation. - Nicholas M. Boffi; Eric Vanden-Eijnden (Mach. Learn. Sci. Technol., 2023)

SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. - Nanye Ma; Mark Goldstein; Michael S. Albergo; Nicholas M. Boffi; Eric Vanden-Eijnden; Saining Xie (ECCV (77), 2024)

Multimarginal Generative Modeling with Stochastic Interpolants. - Michael S. Albergo; Nicholas Matthew Boffi; Michael Lindsey; Eric Vanden-Eijnden (ICLR, 2024)

Stochastic Interpolants with Data-Dependent Couplings. - Michael S. Albergo; Mark Goldstein; Nicholas Matthew Boffi; Rajesh Ranganath; Eric Vanden-Eijnden (ICML, 2024)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. - Yifan Chen; Mark Goldstein; Mengjian Hua; Michael S. Albergo; Nicholas Matthew Boffi; Eric Vanden-Eijnden (ICML, 2024)

Flow Map Matching. - Nicholas M. Boffi; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2024)

Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking. - Nicholas M. Boffi; Eric Vanden-Eijnden (CoRR, 2024)

Shallow diffusion networks provably learn hidden low-dimensional structure. - Nicholas Matthew Boffi; Arthur Jacot; Stephen Tu; Ingvar M. Ziemann (ICLR, 2025)

Flow map matching with stochastic interpolants: A mathematical framework for consistency models. - Nicholas Matthew Boffi; Michael Samuel Albergo; Eric Vanden-Eijnden (Trans. Mach. Learn. Res., 2025)

How to build a consistency model: Learning flow maps via self-distillation. - Nicholas M. Boffi; Michael S. Albergo; Eric Vanden-Eijnden (CoRR, 2025)

BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation. - Rishal Aggarwal; Jacky Chen; Nicholas M. Boffi; David Ryan Koes (CoRR, 2025)