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01/27/2023
Aleatoric and Epistemic Discrimination in Classification
Machine learning (ML) models can underperform on certain population grou...
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12/30/2022
Node-Element Hypergraph Message Passing for Fluid Dynamics Simulations
A recent trend in deep learning research features the application of gra...
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11/01/2022
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
There is a critical need for efficient and reliable active flow control ...
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10/09/2022
Quasi-Monolithic Graph Neural Network for Fluid-Structure Interaction
Using convolutional neural networks, deep learning-based reduced-order m...
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04/30/2022
A Simple Duality Proof for Wasserstein Distributionally Robust Optimization
We present a short and elementary proof of the duality for Wasserstein d...
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03/19/2022
Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal Velocity Obstacle Shaped Rewards
The challenges to solving the collision avoidance problem lie in adaptiv...
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03/15/2022
Adaptive Environment Modeling Based Reinforcement Learning for Collision Avoidance in Complex Scenes
The major challenges of collision avoidance for robot navigation in crow...
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02/23/2022
Absolute Zero-Shot Learning
Considering the increasing concerns about data copyright and privacy iss...
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01/22/2022
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems
Structured Light Illumination (SLI) systems have been used for reliable ...
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09/24/2021
Sinkhorn Distributionally Robust Optimization
We study distributionally robust optimization with Sinkorn distance – a ...
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05/29/2021
Robust Hypothesis Testing with Wasserstein Uncertainty Sets
We consider a data-driven robust hypothesis test where the optimal test ...
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05/20/2021
Hierarchical Non-Stationary Temporal Gaussian Processes With L^1-Regularization
This paper is concerned with regularized extensions of hierarchical non-...
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02/12/2021
Two-sample Test with Kernel Projected Wasserstein Distance
We develop a kernel projected Wasserstein distance for the two-sample te...
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02/05/2021
Learning While Dissipating Information: Understanding the Generalization Capability of SGLD
Understanding the generalization capability of learning algorithms is at...
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01/11/2021
Generalize Ultrasound Image Segmentation via Instant and Plug Play Style Transfer
Deep segmentation models that generalize to images with unknown appearan...
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11/08/2020
Reliable Off-policy Evaluation for Reinforcement Learning
In a sequential decision-making problem, off-policy evaluation (OPE) est...
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10/22/2020
Two-sample Test using Projected Wasserstein Distance: Breaking the Curse of Dimensionality
We develop a projected Wasserstein distance for the two-sample test, a f...
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10/10/2020
Contrastive Rendering for Ultrasound Image Segmentation
Ultrasound (US) image segmentation embraced its significant improvement ...
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09/09/2020
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality
Wasserstein distributionally robust optimization (DRO) aims to find robu...
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07/22/2020
Autonomous Tracking and State Estimation with Generalised Group Lasso
We address the problem of autonomous tracking and state estimation for m...
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06/07/2020
Distributionally Robust k-Nearest Neighbors for Few-Shot Learning
Learning a robust classifier from a few samples remains a key challenge ...
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05/17/2020
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes
In this letter, we propose a class of efficient, accurate and general me...
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02/14/2020
Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer
Deep Neural Networks (DNNs) suffer from the performance degradation when...
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09/28/2019
Stein Bridging: Enabling Mutual Reinforcement between Explicit and Implicit Generative Models
Deep generative models are generally categorized into explicit models an...
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03/20/2019
Iterated Extended Kalman Smoother-based Variable Splitting for L_1-Regularized State Estimation
In this paper, we propose a new framework for solving state estimation p...
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05/27/2018
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
We develop a novel computationally efficient and general framework for r...
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12/17/2017
Wasserstein Distributional Robustness and Regularization in Statistical Learning
A central question in statistical learning is to design algorithms that ...
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05/30/2017
Multi-Focus Image Fusion Via Coupled Sparse Representation and Dictionary Learning
We address the multi-focus image fusion problem, where multiple images c...
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03/15/2014
