63 citations · 97 across the 21 of their papers we have counts for
21 papers
JWB-DH-V1: Benchmark for Joint Whole-Body Talking Avatar and Speech Generation Version 1
Xinhan Di, Kristin Qi, Pengqian Yu
Recent advances in diffusion-based video generation have enabled photo-realistic short clips, but current methods still struggle to achieve multi-modal consistency when jointly gen…
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
Xinhan Di, Pengqian Yu
In real life, the decoration of 3D indoor scenes through designing furniture layout provides a rich experience for people. In this paper, we explore the furniture layout task as a…
LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction
Xinhan Di, Pengqian Yu
Recent years have witnessed great success for hand reconstruction in real-time applications such as visual reality and augmented reality while interacting with two-hand reconstruct…
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes
Xinhan Di, Pengqian Yu
In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling. In this paper, we explore the interior…
Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes
Xinhan Di, Pengqian Yu
In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling. In this paper, we…
End-to-end Generative Floor-plan and Layout with Attributes and Relation Graph
Xinhan Di, Pengqian Yu, Danfeng Yang +3
In this paper, we propose an end-end model for producing furniture layout for interior scene synthesis from the random vector. This proposed model is aimed to support professional…
Deep Layout of Custom-size Furniture through Multiple-domain Learning
Xinhan Di, Pengqian Yu, Danfeng Yang +3
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene. This model is aimed to support professional interior designers…
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization
Achintya Kundu, Pengqian Yu, Laura Wynter +1
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of me…
Structural Plan of Indoor Scenes with Personalized Preferences
Xinhan Di, Pengqian Yu, Hong Zhu +3
In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferen…
Adversarial Model for Rotated Indoor Scenes Planning
Xinhan Di, Pengqian Yu, Hong Zhu +3
In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated. The proposed model combines a conditio…
Mutual Information Maximization in Graph Neural Networks
Xinhan Di, Pengqian Yu, Rui Bu +1
A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and iteration scheme t…
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization
Pengqian Yu, Joon Sern Lee, Ilya Kulyatin +2
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profi…
Ambient Hidden Space of Generative Adversarial Networks
Xinhan Di, Pengqian Yu, Meng Tian
Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an ac…
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification
Xinhan Di, Pengqian Yu, Meng Tian
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process. The gap between robust deep learning systems…
Distributionally Robust Optimization for Sequential Decision Making
Zhi Chen, Pengqian Yu, William B. Haskell
The distributionally robust Markov Decision Process (MDP) approach asks for a distributionally robust policy that achieves the maximal expected total reward under the most adversar…
An Empirical Dynamic Programming Algorithm for Continuous MDPs
William B. Haskell, Rahul Jain, Hiteshi Sharma +1
We propose universal randomized function approximation-based empirical value iteration (EVI) algorithms for Markov decision processes. The `empirical' nature comes from each iterat…
3D Reconstruction of Simple Objects from A Single View Silhouette Image
Xinhan Di, Pengqian Yu
While recent deep neural networks have achieved promising results for 3D reconstruction from a single-view image, these rely on the availability of RGB textures in images and extra…
Approximate Value Iteration for Risk-aware Markov Decision Processes
Pengqian Yu, William B. Haskell, Huan Xu
We consider large-scale Markov decision processes (MDPs) with a risk measure of variability in cost, under the risk-aware MDPs paradigm. Previous studies showed that risk-aware MDP…
Random constraint sampling and duality for convex optimization
William B. Haskell, Yu Pengqian
We are interested in solving convex optimization problems with large numbers of constraints. Randomized algorithms, such as random constraint sampling, have been very successful in…
Central-limit approach to risk-aware Markov decision processes
Pengqian Yu, Jia Yuan Yu, Huan Xu
Whereas classical Markov decision processes maximize the expected reward, we consider minimizing the risk. We propose to evaluate the risk associated to a given policy over a long-…
Distributionally Robust Counterpart in Markov Decision Processes
Pengqian Yu, Huan Xu
This paper studies Markov Decision Processes under parameter uncertainty. We adapt the distributionally robust optimization framework, and assume that the uncertain parameters are…