50 citations · 164 across the 21 of their papers we have counts for
22 papers
Optimal Control Theoretic Neural Optimizer: From Backpropagation to Dynamic Programming
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
Optimization of deep neural networks (DNNs) has been a driving force in the advancement of modern machine learning and artificial intelligence. With DNNs characterized by a prolong…
Momentum Multi-Marginal Schrödinger Bridge Matching
Panagiotis Theodoropoulos, Augustinos D. Saravanos, Evangelos A. Theodorou +1
Understanding complex systems by inferring trajectories from sparse sample snapshots is a fundamental challenge in a wide range of domains, e.g., single-cell biology, meteorology,…
Deep Generalized Schrödinger Bridges: From Image Generation to Solving Mean-Field Games
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
Generalized Schrödinger Bridges (GSBs) are a fundamental mathematical framework used to analyze the most likely particle evolution based on the principle of least action including…
Feedback Schrödinger Bridge Matching
Panagiotis Theodoropoulos, Nikolaos Komianos, Vincent Pacelli +2
Recent advancements in diffusion bridges for distribution transport problems have heavily relied on matching frameworks, yet existing methods often face a trade-off between scalabi…
React-OT: Optimal Transport for Generating Transition State in Chemical Reactions
Chenru Duan, Guan-Horng Liu, Yuanqi Du +6
Transition states (TSs) are transient structures that are key in understanding reaction mechanisms and designing catalysts but challenging to be captured in experiments. Alternativ…
Augmented Bridge Matching
Valentin De Bortoli, Guan-Horng Liu, Tianrong Chen +2
Flow and bridge matching are a novel class of processes which encompass diffusion models. One of the main aspect of their increased flexibility is that these models can interpolate…
Generalized Schrödinger Bridge Matching
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel +3
Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions…
Mirror Diffusion Models for Constrained and Watermarked Generation
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou +1
Modern successes of diffusion models in learning complex, high-dimensional data distributions are attributed, in part, to their capability to construct diffusion processes with ana…
Improving Generative Model-based Unfolding with Schrödinger Bridges
Sascha Diefenbacher, Guan-Horng Liu, Vinicius Mikuni +2
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area: one base…
Improved sampling via learned diffusions
Lorenz Richter, Julius Berner
Recently, a series of papers proposed deep learning-based approaches to sample from target distributions using controlled diffusion processes, being trained only on the unnormalize…
Deep Momentum Multi-Marginal Schrödinger Bridge
Tianrong Chen, Guan-Horng Liu, Molei Tao +1
It is a crucial challenge to reconstruct population dynamics using unlabeled samples from distributions at coarse time intervals. Recent approaches such as flow-based models or Sch…
ISB: Image-to-Image Schrödinger Bridge
Guan-Horng Liu, Arash Vahdat, De-An Huang +3
We propose Image-to-Image Schrödinger Bridge (ISB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distribu…
Deep Generalized Schrödinger Bridge
Guan-Horng Liu, Tianrong Chen, Oswin So +1
Mean-Field Game (MFG) serves as a crucial mathematical framework in modeling the collective behavior of individual agents interacting stochastically with a large population. In thi…
Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
Tianrong Chen, Guan-Horng Liu, Evangelos A. Theodorou
Schrödinger Bridge (SB) is an entropy-regularized optimal transport problem that has received increasing attention in deep generative modeling for its mathematical flexibility comp…
Second-Order Neural ODE Optimizer
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
We propose a novel second-order optimization framework for training the emerging deep continuous-time models, specifically the Neural Ordinary Differential Equations (Neural ODEs).…
Dynamic Game Theoretic Neural Optimizer
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
The connection between training deep neural networks (DNNs) and optimal control theory (OCT) has attracted considerable attention as a principled tool of algorithmic design. Despit…
Spatio-Temporal Differential Dynamic Programming for Control of Fields
Ethan N. Evans, Oswin So, Andrew P. Kendall +2
We consider the optimal control problem of a general nonlinear spatio-temporal system described by Partial Differential Equations (PDEs). Theory and algorithms for control of spati…
Variational Inference MPC using Tsallis Divergence
Ziyi Wang, Oswin So, Jason Gibson +4
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal Control by using thenon-extensive Tsallis divergence. By incorporating the deformed e…
A Differential Game Theoretic Neural Optimizer for Training Residual Networks
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
Connections between Deep Neural Networks (DNNs) training and optimal control theory has attracted considerable attention as a principled tool of algorithmic design. Differential Dy…
DDPNOpt: Differential Dynamic Programming Neural Optimizer
Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou
Interpretation of Deep Neural Networks (DNNs) training as an optimal control problem with nonlinear dynamical systems has received considerable attention recently, yet the algorith…
Deep Learning Theory Review: An Optimal Control and Dynamical Systems Perspective
Guan-Horng Liu, Evangelos A. Theodorou
Attempts from different disciplines to provide a fundamental understanding of deep learning have advanced rapidly in recent years, yet a unified framework remains relatively limite…
Learning End-to-end Multimodal Sensor Policies for Autonomous Navigation
Guan-Horng Liu, Avinash Siravuru, Sai Prabhakar +2
Multisensory polices are known to enhance both state estimation and target tracking. However, in the space of end-to-end sensorimotor control, this multi-sensor outlook has receive…