publications

Below is a complete list of my publications. For the most up-to-date list, please check my Google Scholar profile.

An asterisk (*) denotes equal contribution with alphabetical or randomized ordering.

<section class="year-group">
  <div class="year-h">2026</div>
  <div class="paper-list" role="list">

Flow Map Language Models

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

Preprint, 2026

arXiv / blog

Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps

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

Preprint, 2026

arXiv

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

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

International Conference on Learning Representations, 2026

arXiv

Much Ado About Noising: Dispelling the Myths of Generative Robotic Control

Chaoyi Pan, Giri Anantharaman, Nai-Chieh Huang, Claire Jin, Daniel Pfrommer, Chenyang Yuan, Frank Permenter, Guannan Qu, Nicholas M. Boffi†, Guanya Shi†, Max Simchowitz†

International Conference on Learning Representations, 2026

arXiv

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

International Conference on Learning Representations, 2026

arXiv

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2025</div>
  <div class="paper-list" role="list">

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

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

Preprint, 2025

arXiv

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

Preprint, 2025

arXiv

Sublinear iterations can suffice even for DDPMs

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

Preprint, 2025

arXiv

Eigenfunction Extraction for Ordered Representation Learning

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

Preprint, 2025

arXiv

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

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

Neural Information Processing Systems, 2025

arXiv

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

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

Neural Information Processing Systems, 2025

arXiv / code

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

Nicholas M. Boffi, Eric Vanden-Eijnden

Physical Review Letters, 2025

arXiv / code

Shallow diffusion networks provably learn hidden low-dimensional structure

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

International Conference on Learning Representations, 2025

arXiv / code

Flow map matching with stochastic interpolants: A mathematical framework for consistency models

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

Transactions on Machine Learning Research, 2025

arXiv / code

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

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

Journal of Machine Learning Research, 2025

First appeared on arXiv 2023

arXiv / code 1 / code 2

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2024</div>
  <div class="paper-list" role="list">

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes

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

International Conference on Machine Learning, 2024

arXiv / code

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

European Conference on Computer Vision, 2024

arXiv / paper

Stochastic interpolants with data-dependent couplings

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

International Conference on Machine Learning, 2024 (Spotlight)

arXiv

Multimarginal generative modeling with stochastic interpolants

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

International Conference on Learning Representations, 2024

arXiv

Deep learning probability flows and entropy production rates in active matter

Nicholas M. Boffi, Eric Vanden-Eijnden

Proceedings of the National Academy of Sciences, 2024

arXiv / paper / code

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2023</div>
  <div class="paper-list" role="list">

Agile Catching with Whole-Body MPC and Blackbox Policy Learning

Saminda Abeyruwan, Alex Bewley, Nicholas 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

Learning for Dynamics and Control, 2023

paper

Thumbnail for How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

Nicholas M. Boffi, Yipei Guo, Chris H. Rycroft, Ariel Amir

eLife, 2023

paper / code / bioRxiv

Probability flow solution of the Fokker-Planck equation

Nicholas M. Boffi, Eric Vanden-Eijnden

Machine Learning Science and Technology, 2023

arXiv / paper / code

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2022</div>
  <div class="paper-list" role="list">

Adversarially robust stability certificates can be sample efficient

Thomas Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques Slotine, Nikolai Matni

Learning for Dynamics and Control, 2022

arXiv / paper

Non-parametric adaptive control and prediction: theory and randomized algorithms

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

Journal of Machine Learning Research (conference version at CDC 2021), 2022

arXiv / paper

The role of optimization geometry in single neuron learning

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

International Conference on Artificial Intelligence and Statistics, 2022

arXiv / paper

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2021</div>
  <div class="paper-list" role="list">

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

Katiana Kontolati, Darius Alix-Williams, Nicholas M. Boffi, Michael L. Falk, Chris H. Rycroft, Michael D. Shields

Acta Materialia, 2021

arXiv / paper

Regret bounds for adaptive nonlinear control

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

Learning for Dynamics and Control, 2021 (Oral)

arXiv / paper

Implicit regularization and momentum algorithms in nonlinearly parameterized adaptive control and prediction

Nicholas M. Boffi, Jean-Jacques E. Slotine

Neural Computation, 2021 (Cover)

arXiv / paper

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2020</div>
  <div class="paper-list" role="list">

Learning stability certificates from data

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

Conference on Robot Learning, 2020

arXiv / paper

Coordinate transformation methodology for simulating quasi-static elastoplastic solids

Nicholas M. Boffi, Chris H. Rycroft

Physical Review E, 2020

arXiv / paper / code

Parallel three-dimensional simulations of quasi-static elastoplastic solids

Nicholas M. Boffi, Chris H. Rycroft

Computer Physics Communications, 2020

arXiv / paper / code

A continuous-time analysis of distributed stochastic gradient

Nicholas M. Boffi, Jean-Jacques E. Slotine

Neural Computation, 2020

arXiv / paper

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2016</div>
  <div class="paper-list" role="list">

Efficient computation of the Hartree-Fock Exchange in real-space with projection operators

Nicholas M. Boffi, Manish Jain, Amir Natan

Journal of Chemical Theory and Computation, 2016

paper / code

Asymptotic behavior and interpretation of virtual states: the effects of confinement and of basis sets

Nicholas M. Boffi, Manish Jain, Amir Natan

Journal of Chemical Physics, 2016

paper / code

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2014</div>
  <div class="paper-list" role="list">

Characterizing the inverses of block tridiagonal, block Toeplitz matrices

Nicholas M. Boffi, Judith C. Hill, Matthew G. Reuter

Computational Science and Discovery, 2014

paper

  </div>
</section>
  
<section class="year-group">
  <div class="year-h">2013</div>
  <div class="paper-list" role="list">

The role of dimensionality in the decay of surface effects

Matthew G. Reuter, Nicholas M. Boffi, Mark A. Ratner, Tamar Seideman

Journal of Chemical Physics, 2013

paper

  </div>
</section>