14 citations · 68 across the 14 of their papers we have counts for
14 papers
Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Reflection
Wenhang Ge, Tao Hu, Haoyu Zhao +2
Neural implicit surface learning has shown significant progress in multi-view 3D reconstruction, where an object is represented by multilayer perceptrons that provide continuous im…
Do Transformers Parse while Predicting the Masked Word?
Haoyu Zhao, Abhishek Panigrahi, Rong Ge +1
Pre-trained language models have been shown to encode linguistic structures, e.g. dependency and constituency parse trees, in their embeddings while being trained on unsupervised l…
Coresets for Vertical Federated Learning: Regularized Linear Regression and -Means Clustering
Lingxiao Huang, Zhize Li, Jialin Sun +1
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexit…
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression
Zhize Li, Haoyu Zhao, Boyue Li +1
To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient…
BEER: Fast Rate for Decentralized Nonconvex Optimization with Communication Compression
Haoyu Zhao, Boyue Li, Zhize Li +2
Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackl…
Faster Rates for Compressed Federated Learning with Client-Variance Reduction
Haoyu Zhao, Konstantin Burlachenko, Zhize Li +1
Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted significant attention and are wide…
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
Haoyu Zhao, Zhize Li, Peter Richtárik
Federated Averaging (FedAvg, also known as Local-SGD) (McMahan et al., 2017) is a classical federated learning algorithm in which clients run multiple local SGD steps before commun…
Combinatorial Pure Exploration of Dueling Bandit
Wei Chen, Yihan Du, Longbo Huang +1
In this paper, we study combinatorial pure exploration for dueling bandits (CPE-DB): we have multiple candidates for multiple positions as modeled by a bipartite graph, and in each…
Combinatorial Semi-Bandit in the Non-Stationary Environment
Wei Chen, Liwei Wang, Haoyu Zhao +1
In this paper, we investigate the non-stationary combinatorial semi-bandit problem, both in the switching case and in the dynamic case. In the general case where (a) the reward fun…
Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations
Wei Chen, Weizhong Zhang, Haoyu Zhao
Continuous influence maximization (CIM) generalizes the original influence maximization by incorporating general marketing strategies: a marketing strategy mix is a vector $\boldsy…
Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting
Haoyu Zhao, Wei Chen
In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in rounds by the second price auctio…
Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently
Rong Ge, Runzhe Wang, Haoyu Zhao
It has been observed \citep{zhang2016understanding} that deep neural networks can memorize: they achieve 100\% accuracy on training data. Recent theoretical results explained such…
Stochastic One-Sided Full-Information Bandit
Haoyu Zhao, Wei Chen
In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have arms , and playing arm would gain…
An FPTAS for Stochastic Unbounded Min-Knapsack Problem
Zhihao Jiang, Haoyu Zhao
In this paper, we study the stochastic unbounded min-knapsack problem (). The ordinary unbounded min-knapsack problem states that: There are types of items,…