Nicholas M. Boffi

According to our database, Nicholas M. Boffi authored at least 34 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance.

Jerry Huang, Justin Lin, Sheel Shah, Kartik Nair, Nicholas M. Boffi

CoRR, April, 2026

One-step Language Modeling via Continuous Denoising.

Chanhyuk Lee, Jaehoon Yoo, Manan Agarwal, Sheel Shah, Jerry Huang, Aditi Raghunathan, Seunghoon Hong, Nicholas M. Boffi, Jinwoo Kim

CoRR, February, 2026

Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps.

Peter Holderrieth, Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong He, Zeynep Akata, Tommi S. Jaakkola, Nicholas Matthew Boffi, Max Simchowitz

CoRR, February, 2026

2025

Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models.

Xinyue Ai, Yutong He, Albert Gu, Ruslan Salakhutdinov, J. Zico Kolter, Nicholas Matthew Boffi, Max Simchowitz

CoRR, December, 2025

Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers.

Naman Choudhary, Vedant Singh, Ameet Talwalkar, Nicholas Matthew Boffi, Mikhail Khodak, Tanya Marwah

CoRR, December, 2025

Test-time scaling of diffusions with flow maps.

Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich, Nicholas M. Boffi, Sanja Fidler, Karsten Kreis, Eric Vanden-Eijnden

CoRR, November, 2025

Sublinear iterations can suffice even for DDPMs.

Matthew S. Zhang, Stephen Huan, Jerry Huang, Nicholas M. Boffi, Sitan Chen, Sinho Chewi

CoRR, November, 2025

Eigenfunction Extraction for Ordered Representation Learning.

Burak Varici, Che-Ping Tsai, Ritabrata Ray, Nicholas M. Boffi, Pradeep Ravikumar

CoRR, October, 2025

Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds.

Oscar Davis, Michael S. Albergo, Nicholas M. Boffi, Michael M. Bronstein, Avishek Joey Bose

CoRR, October, 2025

How to build a consistency model: Learning flow maps via self-distillation.

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

CoRR, May, 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

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

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

J. Mach. Learn. Res., 2025

BoltzNCE: Learning likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation.

Rishal Aggarwal, Jacky Chen, Nicholas M. Boffi, David Koes

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Shallow diffusion networks provably learn hidden low-dimensional structure.

Nicholas Matthew Boffi, Arthur Jacot, Stephen Tu, Ingvar M. Ziemann

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking.

Nicholas M. Boffi, Eric Vanden-Eijnden

CoRR, 2024

Flow Map Matching.

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

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

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

Proceedings of the Computer Vision - ECCV 2024, 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

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

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

Nonparametric Adaptive Control and Prediction: Theory and Randomized Algorithms.

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

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 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