Nicholas M. Boffi

According to our database, Nicholas M. Boffi authored at least 20 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Flow Map Matching.

Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden

CoRR, 2024

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

CoRR, 2024

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes.

Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Stochastic Interpolants with Data-Dependent Couplings.

Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multimarginal Generative Modeling with Stochastic Interpolants.

Michael S. Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Probability flow solution of the Fokker-Planck equation.

Nicholas M. Boffi, Eric Vanden-Eijnden

Mach. Learn. Sci. Technol., September, 2023

Deep learning probability flows and entropy production rates in active matter.

Nicholas M. Boffi, Eric Vanden-Eijnden

CoRR, 2023

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden

CoRR, 2023

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

Proceedings of the Learning for Dynamics and Control Conference, 2023

2022

Nonparametric adaptive control and prediction: theory and randomized algorithms.

Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

J. Mach. Learn. Res., 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

Proceedings of the Learning for Dynamics and Control Conference, 2022

The role of optimization geometry in single neuron learning.

Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction.

Nicholas M. Boffi, Jean-Jacques E. Slotine

Neural Comput., 2021

Random features for adaptive nonlinear control and prediction.

Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

CoRR, 2021

Regret Bounds for Adaptive Nonlinear Control.

Nicholas M. Boffi, Stephen Tu, Jean-Jacques E. Slotine

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

2020

A Continuous-Time Analysis of Distributed Stochastic Gradient.

Nicholas M. Boffi, Jean-Jacques E. Slotine

Neural Comput., 2020

Parallel three-dimensional simulations of quasi-static elastoplastic solids.

Nicholas M. Boffi, Chris H. Rycroft

Comput. Phys. Commun., 2020

The Reflectron: Exploiting geometry for learning generalized linear models.

Nicholas M. Boffi, Jean-Jacques E. Slotine

CoRR, 2020

Learning Stability Certificates from Data.

Nicholas M. Boffi, Stephen Tu, Nikolai Matni, Jean-Jacques E. Slotine, Vikas Sindhwani

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Higher-order algorithms for nonlinearly parameterized adaptive control.

Nicholas M. Boffi, Jean-Jacques E. Slotine

CoRR, 2019