40 citations · 160 across the 39 of their papers we have counts for
27 papers · 1 filter
Normalizing Flow Maps
Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng +6
Scalable approaches to learning neural dynamical systems have placed diffusion, flow matching, and few-step generators in flow maps at the forefront of generative modeling. Such st…
WTF?! Simulation-Free Reinforcement Learning with Wasserstein-Tilted Flow Maps
Abbas Mammadov, Jerry Y. Huang, Justin Lin +5
Reward fine-tuning aims to update a pre-trained flow-based generative model to improve the downstream reward of its generated samples. Existing methods typically formulate this pro…
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Few-step Cofolding with All-Atom Flow Maps
Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7
All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating struc…
How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
Jerry Y. Huang, Justin Lin, Sheel Shah +2
In generative modeling, we often wish to produce samples that maximize a user-specified reward such as aesthetic quality or alignment with human preferences, a problem known as \te…
Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events
Rishal Aggarwal, David Ryan Koes, Nicholas M. Boffi +1
Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce…
Are we really tilting? The mechanics of reward guidance in flow and diffusion models
Sanjit Dandapanthula, Nicholas M. Boffi
Reward guidance algorithms steer a learned generative process toward the reward-tilted measure at inference time. While empirically powerful, these methods are prone to reward hack…
Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
Peter Holderrieth, Douglas Chen, Luca Eyring +7
Flow and diffusion models produce high-quality samples, but adapting them to user preferences or constraints post-training remains costly and brittle, a challenge commonly called r…
Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers
Naman Choudhary, Vedant Singh, Ameet Talwalkar +3
A key aspect of learned partial differential equation (PDE) solvers is that the main cost often comes from generating training data with classical solvers rather than learning the…
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
Xinyue Ai, Yutong He, Albert Gu +4
Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet par…
Test-time scaling of diffusions with flow maps
Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich +4
A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics o…
Sublinear iterations can suffice even for DDPMs
Matthew S. Zhang, Stephen Huan, Jerry Huang +3
SDE-based methods such as denoising diffusion probabilistic models (DDPMs) have shown remarkable success in real-world sample generation tasks. Prior analyses of DDPMs have been fo…
Eigenfunction Extraction for Ordered Representation Learning
Burak Varıcı, Che-Ping Tsai, Ritabrata Ray +2
Recent advances in representation learning reveal that widely used objectives, such as contrastive and non-contrastive, implicitly perform spectral decomposition of a contextual ke…
Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
Oscar Davis, Michael S. Albergo, Nicholas M. Boffi +2
Geometric data and purpose-built generative models on them have become ubiquitous in high-impact deep learning application domains, ranging from protein backbone generation and com…
BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation
Rishal Aggarwal, Jacky Chen, Nicholas M. Boffi +1
Efficient sampling from the Boltzmann distribution given its energy function is a key challenge for modeling complex physical systems such as molecules. Boltzmann Generators addres…
How to build a consistency model: Learning flow maps via self-distillation
Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
Flow-based generative models achieve state-of-the-art sample quality, but require the expensive solution of a differential equation at inference time. Flow map models, commonly kno…
Shallow diffusion networks provably learn hidden low-dimensional structure
Nicholas M. Boffi, Arthur Jacot, Stephen Tu +1
Diffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied t…
Flow map matching with stochastic interpolants: A mathematical framework for consistency models
Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
Generative models based on dynamical equations such as flows and diffusions offer exceptional sample quality, but require computationally expensive numerical integration during inf…
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua +3
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasti…
Multimarginal generative modeling with stochastic interpolants
Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey +1
Given a set of probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities as marginals. The…
Stochastic interpolants with data-dependent couplings
Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi +2
Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities. Conventionally, o…
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
A class of generative models that unifies flow-based and diffusion-based methods is introduced. These models extend the framework proposed in Albergo and Vanden-Eijnden (2023), ena…
Probability flow solution of the Fokker-Planck equation
Nicholas M. Boffi, Eric Vanden-Eijnden
The method of choice for integrating the time-dependent Fokker-Planck equation in high-dimension is to generate samples from the solution via integration of the associated stochast…
Adversarially Robust Stability Certificates can be Sample-Efficient
Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi +2
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinea…