no code implementations • 28 Jan 2024 • Xingyu Zhou, Zhuoyong Shi
With the impact of artificial intelligence on the traditional UAV industry, autonomous UAV flight has become a current hot research field.
1 code implementation • 16 Jan 2024 • Leheng Zhang, Yawei Li, Xingyu Zhou, Xiaorui Zhao, Shuhang Gu
The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step.
Ranked #1 on Image Super-Resolution on Set14
1 code implementation • 15 Jan 2024 • Kexuan Shi, Xingyu Zhou, Shuhang Gu
We evaluate the proposed Fourier reparameterization method on different INR tasks with various MLP architectures, including vanilla MLP, MLP with positional encoding and MLP with advanced activation function, etc.
1 code implementation • 12 Jan 2024 • Xingyu Zhou, Leheng Zhang, Xiaorui Zhao, Keze Wang, Leida Li, Shuhang Gu
The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features.
no code implementations • 30 Oct 2023 • Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan
Within a standard minimax estimation framework, we provide tight upper and lower bounds on the error in estimating $\theta^*$ under both local and central models of DP.
no code implementations • 12 Aug 2023 • Xingyu Zhou, Le Liang, Jing Zhang, Chao-Kai Wen, Shi Jin
However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical.
no code implementations • 16 Jun 2023 • Duo Cheng, Xingyu Zhou, Bo Ji
To design algorithms that can achieve the minimax regret, it is instructive to consider a more general setting where the learner has a budget of $B$ total observations.
no code implementations • 1 Jun 2023 • Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang
Under each framework, we consider both joint differential privacy (JDP) and local differential privacy (LDP) models.
no code implementations • 10 Mar 2023 • Honghao Wei, Arnob Ghosh, Ness Shroff, Lei Ying, Xingyu Zhou
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost).
no code implementations • 27 Feb 2023 • Xingyu Zhou, Sayak Ray Chowdhury
We first establish privacy and regret guarantees under silo-level local differential privacy, which fix the issues present in state-of-the-art algorithm.
no code implementations • 28 Jan 2023 • Fengjiao Li, Xingyu Zhou, Bo Ji
This problem is motivated by several real-world applications (such as dynamic pricing, cellular network configuration, and policy making), where users from a large population contribute to the reward of the action chosen by a central entity, but it is difficult to collect feedback from all users.
no code implementations • 25 Sep 2022 • Zhen Gao, Xingyu Zhou, Jingjing Zhao, Juan Li, Chunli Zhu, Chun Hu, Pei Xiao, Symeon Chatzinotas, Derrick Wing Kwan Ng, Bjorn Ottersten
With the blooming of Internet-of-Things (IoT), we are witnessing an explosion in the number of IoT terminals, triggering an unprecedented demand for ubiquitous wireless access globally.
no code implementations • 12 Jul 2022 • Fengjiao Li, Xingyu Zhou, Bo Ji
To tackle this problem, we consider differentially private distributed linear bandits, where only a subset of users from the population are selected (called clients) to participate in the learning process and the central server learns the global model from such partial feedback by iteratively aggregating these clients' local feedback in a differentially private fashion.
no code implementations • 23 Jun 2022 • Arnob Ghosh, Xingyu Zhou, Ness Shroff
To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping.
no code implementations • 12 Jun 2022 • Sayak Ray Chowdhury, Xingyu Zhou
This protocol achieves ($\epsilon,\delta$) or approximate-DP guarantee by sacrificing an additional additive $O\!\left(\!\frac{K\log T\sqrt{\log(1/\delta)}}{\epsilon}\!\right)\!$ cost in $T$-step cumulative regret.
no code implementations • 6 Jun 2022 • Yuzhen Han, Ruben Solozabal, Jing Dong, Xingyu Zhou, Martin Takac, Bin Gu
To the best of our knowledge, our study establishes the first model-based online algorithm with regret guarantees under LTV dynamical systems.
no code implementations • 29 Mar 2022 • Xingyu Zhou, Bo Ji
Our ultimate goal is to study how to utilize the nature of soft constraints to attain a finer complexity-regret-constraint trade-off in the kernelized bandit setting.
no code implementations • 11 Feb 2022 • Sayak Ray Chowdhury, Xingyu Zhou
Prior work largely focus on two trust models of DP: the central model, where a central server is responsible for protecting users sensitive data, and the (stronger) local model, where information needs to be protected directly on user side.
no code implementations • 18 Jan 2022 • Xingyu Zhou
Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services, where users' sensitive and private information needs to be protected, we study regret minimization in finite-horizon Markov decision processes (MDPs) under the constraints of differential privacy (DP).
1 code implementation • 1 Jan 2022 • Yexin Duan, Junhua Zou, Xingyu Zhou, Wu Zhang, Jin Zhang, Zhisong Pan
Deep neural networks are vulnerable to adversarial examples, which can fool deep models by adding subtle perturbations.
1 code implementation • 20 Dec 2021 • Sayak Ray Chowdhury, Xingyu Zhou
We study regret minimization in finite horizon tabular Markov decision processes (MDPs) under the constraints of differential privacy (DP).
1 code implementation • 19 Dec 2021 • Shiao Liu, Xingyu Zhou, Yuling Jiao, Jian Huang
The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution.
no code implementations • 1 Sep 2021 • Yexin Duan, Jialin Chen, Xingyu Zhou, Junhua Zou, Zhengyun He, Jin Zhang, Wu Zhang, Zhisong Pan
An adversary can fool deep neural network object detectors by generating adversarial noises.
no code implementations • 26 Aug 2021 • Sayak Ray Chowdhury, Xingyu Zhou, Ness Shroff
In this paper, we study the problem of regret minimization in reinforcement learning (RL) under differential privacy constraints.
no code implementations • 2 Aug 2021 • Cheng Gong, Zirui Li, Xingyu Zhou, Jiachen Li, Jianwei Gong, Junhui Zhou
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility.
no code implementations • 6 Jul 2021 • Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness Shroff
To this end, we develop WGP-UCB, a novel UCB-type algorithm based on weighted Gaussian process regression.
no code implementations • 11 Feb 2021 • Xingyu Zhou, Ness Shroff
In this paper, we consider the time-varying Bayesian optimization problem.
no code implementations • 28 Oct 2020 • Junzhe Shi, Bin Xu, Xingyu Zhou, Jun Hou
The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability.
no code implementations • 13 Oct 2020 • Xingyu Zhou, Jian Tan
Motivated by the increasing concern about privacy in nowadays data-intensive online learning systems, we consider a black-box optimization in the nonparametric Gaussian process setting with local differential privacy (LDP) guarantee.
no code implementations • 6 Jul 2020 • Wenbo Ren, Xingyu Zhou, Jia Liu, Ness B. Shroff
To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee.
1 code implementation • 13 Jan 2020 • Xingyu Zhou, Shuxian Du, Gang Li, Chengping Shen
To help analysts obtain the physics process information from the truth information of the samples, we develop a physics process analysis program, TopoAna, with C++, ROOT, and LaTeX.
High Energy Physics - Experiment
no code implementations • ICLR 2019 • Xingyu Zhou, Tengyu Ma, Huahong Zhang
This paper, in contrast, discusses the origin of adversarial examples from a more underlying knowledge representation point of view.