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09/17/2023
Efficient Belief Road Map for Planning Under Uncertainty
Robotic systems, particularly in demanding environments like narrow corr...
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09/12/2023
On the Contraction Coefficient of the Schrödinger Bridge for Stochastic Linear Systems
Schrödinger bridge is a stochastic optimal control problem to steer a gi...
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08/29/2023
Stochastic Motion Planning as Gaussian Variational Inference: Theory and Algorithms
We consider the motion planning problem under uncertainty and address it...
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08/16/2023
Online Control for Linear Dynamics: A Data-Driven Approach
This paper considers an online control problem over a linear time-invari...
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08/04/2023
Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling
Efficient differential equation solvers have significantly reduced the s...
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07/11/2023
Control and estimation of multi-commodity network flow under aggregation
A paradigm put forth by E. Schrödinger in 1931/32, known as Schrödinger ...
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06/23/2023
On a Class of Gibbs Sampling over Networks
We consider the sampling problem from a composite distribution whose pot...
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05/25/2023
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Neural networks are known to be susceptible to adversarial samples: smal...
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03/30/2023
DiffCollage: Parallel Generation of Large Content with Diffusion Models
We present DiffCollage, a compositional diffusion model that can generat...
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02/20/2023
Improved dimension dependence of a proximal algorithm for sampling
We propose a sampling algorithm that achieves superior complexity bounds...
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09/13/2022
A Gaussian variational inference approach to motion planning
We propose a Gaussian variational inference framework for the motion pla...
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08/15/2022
Nesterov smoothing for sampling without smoothness
We study the problem of sampling from a target distribution in ℝ^d whose...
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06/11/2022
gDDIM: Generalized denoising diffusion implicit models
Our goal is to extend the denoising diffusion implicit model (DDIM) to g...
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05/20/2022
A Proximal Algorithm for Sampling from Non-convex Potentials
We study sampling problems associated with non-convex potentials that me...
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04/29/2022
Fast Sampling of Diffusion Models with Exponential Integrator
The past few years have witnessed the great success of Diffusion models ...
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03/12/2022
End-to-End Multi-Tab Website Fingerprinting Attack: A Detection Perspective
Website fingerprinting attack (WFA) aims to deanonymize the website a us...
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02/28/2022
A Proximal Algorithm for Sampling
We consider sampling problems with possibly non-smooth potentials (negat...
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02/13/2022
Improved analysis for a proximal algorithm for sampling
We study the proximal sampler of Lee, Shen, and Tian (2021) and obtain n...
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12/04/2021
Variational Wasserstein gradient flow
The gradient flow of a function over the space of probability densities ...
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11/30/2021
Path Integral Sampler: a stochastic control approach for sampling
We present Path Integral Sampler (PIS), a novel algorithm to draw sample...
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10/14/2021
Diffusion Normalizing Flow
We present a novel generative modeling method called diffusion normalizi...
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10/09/2021
A Proximal Algorithm for Sampling from Non-smooth Potentials
Markov chain Monte Carlo (MCMC) is an effective and dominant method to s...
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10/01/2021
On the complexity of the optimal transport problem with graph-structured cost
Multi-marginal optimal transport (MOT) is a generalization of optimal tr...
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07/24/2021
Inference of collective Gaussian hidden Markov models
We consider inference problems for a class of continuous state collectiv...
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06/19/2021
Stein particle filtering
We present a new particle filtering algorithm for nonlinear systems in t...
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06/07/2021
Scalable Computation of Monge Maps with General Costs
Monge map refers to the optimal transport map between two probability di...
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02/05/2021
Learning High Dimensional Wasserstein Geodesics
We propose a new formulation and learning strategy for computing the Was...
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12/21/2020
Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
We consider the optimization problem of minimizing a functional defined ...
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11/23/2020
Learning Hidden Markov Models from Aggregate Observations
In this paper, we propose an algorithm for estimating the parameters of ...
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11/04/2020
Filtering for Aggregate Hidden Markov Models with Continuous Observations
We consider a class of filtering problems for large populations where ea...
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07/08/2020
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Wasserstein Barycenter is a principled approach to represent the weighte...
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06/28/2020
A convex data-driven approach for nonlinear control synthesis
We consider a class of nonlinear control synthesis problems where the un...
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06/26/2020
Incremental inference of collective graphical models
We consider incremental inference problems from aggregate data for colle...
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06/25/2020
Multi-marginal optimal transport and probabilistic graphical models
We study multi-marginal optimal transport problems from a probabilistic ...
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06/08/2020
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory
Temporal-difference and Q-learning play a key role in deep reinforcement...
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05/01/2020
Improving Robustness via Risk Averse Distributional Reinforcement Learning
One major obstacle that precludes the success of reinforcement learning ...
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04/05/2020
Macroscopic network circulation for planar graphs
The analysis of networks, aimed at suitably defined functionality, often...
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03/31/2020
Inference with Aggregate Data: An Optimal Transport Approach
We consider inference problems over probabilistic graphical models with ...
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01/08/2020
Sample-based Distributional Policy Gradient
Distributional reinforcement learning (DRL) is a recent reinforcement le...
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10/16/2019
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
We study discrete-time mean-field Markov games with infinite numbers of ...
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07/14/2019
On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Despite the empirical success of the actor-critic algorithm, its theoret...
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04/14/2019
Probabilistic Kernel Support Vector Machines
We propose a probabilistic enhancement of standard kernel Support Vecto...
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02/21/2019
Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications
The min-max problem, also known as the saddle point problem, is a class ...
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01/11/2019
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator
We study the global convergence of generative adversarial imitation lear...
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06/26/2017
