Search Results for author: Fan Zhou

Found 31 papers, 7 papers with code

Rate-Optimal Subspace Estimation on Random Graphs

no code implementations NeurIPS 2021 Zhixin Zhou, Fan Zhou, Ping Li, Cun-Hui Zhang

We show that the performance of estimating the connectivity matrix $M$ depends on the sparsity of the graph.

CCGL: Contrastive Cascade Graph Learning

1 code implementation27 Jul 2021 Xovee Xu, Fan Zhou, Kunpeng Zhang, Siyuan Liu

Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.

Data Augmentation Fine-tuning +3

Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning

no code implementations14 May 2021 Fan Zhou, Zhoufan Zhu, Qi Kuang, Liwen Zhang

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address.

Atari Games Distributional Reinforcement Learning +1

Multi-task Learning by Leveraging the Semantic Information

no code implementations3 Mar 2021 Fan Zhou, Brahim Chaib-Draa, Boyu Wang

To confirm the effectiveness of the proposed method, we first compare the algorithm with several baselines on some benchmarks and then test the algorithms under label space shift conditions.

Multi-Task Learning

Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms

1 code implementation11 Feb 2021 Fan Zhou, Shikai Luo, XiaoHu Qie, Jieping Ye, Hongtu Zhu

How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.

Optimization and Control Applications

Embedding Symbolic Temporal Knowledge into Deep Sequential Models

no code implementations28 Jan 2021 Yaqi Xie, Fan Zhou, Harold Soh

However, when data is limited, simpler models such as logic/rule-based methods work surprisingly well, especially when relevant prior knowledge is applied in their construction.

Action Recognition Imitation Learning +2

Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs

no code implementations1 Jan 2021 Fan Zhou, Yifeng Pan, Shenghua Zhu, Xin He

Directed acyclic graphs (DAGs) are widely used to model the casual relationships among random variables in many disciplines.

Non-Crossing Quantile Regression for Distributional Reinforcement Learning

no code implementations NeurIPS 2020 Fan Zhou, Jianing Wang, Xingdong Feng

Distributional reinforcement learning (DRL) estimates the distribution over future returns instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs.

Atari Games Distributional Reinforcement Learning

Beyond $\mathcal{H}$-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence

no code implementations30 Jul 2020 Changjian Shui, Qi Chen, Jun Wen, Fan Zhou, Christian Gagné, Boyu Wang

We reveal the incoherence between the widely-adopted empirical domain adversarial training and its generally-assumed theoretical counterpart based on $\mathcal{H}$-divergence.

Domain Adaptation Transfer Learning

Domain Generalization with Optimal Transport and Metric Learning

no code implementations21 Jul 2020 Fan Zhou, Zhuqing Jiang, Changjian Shui, Boyu Wang, Brahim Chaib-Draa

Previous domain generalization approaches mainly focused on learning invariant features and stacking the learned features from each source domain to generalize to a new target domain while ignoring the label information, which will lead to indistinguishable features with an ambiguous classification boundary.

Domain Generalization Metric Learning

Interpreting Twitter User Geolocation

no code implementations ACL 2020 Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang

Identifying user geolocation in online social networks is an essential task in many location-based applications.

Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder

no code implementations ACL 2020 Fan Zhou, Shengming Zhang, Yi Yang

To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC).

Classification General Classification +3

Discriminative Active Learning for Domain Adaptation

no code implementations24 May 2020 Fan Zhou, Changjian Shui, Bincheng Huang, Boyu Wang, Brahim Chaib-Draa

To this end, we introduce a discriminative active learning approach for domain adaptation to reduce the efforts of data annotation.

Active Learning Domain Adaptation

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

3 code implementations22 May 2020 Fan Zhou, Xovee Xu, Goce Trajcevski, Kunpeng Zhang

The deluge of digital information in our daily life -- from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.

Feature Engineering

Metric learning by Similarity Network for Deep Semi-Supervised Learning

no code implementations29 Apr 2020 Sanyou Wu, Xingdong Feng, Fan Zhou

Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning.

Metric Learning

A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

1 code implementation26 Mar 2020 Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong, Kunpeng Zhang

Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields.

Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay

no code implementations22 Mar 2020 Fan Zhou, Chengtai Cao

Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks.

Graph Classification Graph Learning +1

Relational State-Space Model for Stochastic Multi-Object Systems

no code implementations ICLR 2020 Fan Yang, Ling Chen, Fan Zhou, Yusong Gao, Wei Cao

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.

Time Series

Graph-Based Semi-Supervised Learning with Non-ignorable Non-response

1 code implementation NeurIPS 2019 Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping

Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled.

Classification General Classification +1

Deep Active Learning: Unified and Principled Method for Query and Training

1 code implementation20 Nov 2019 Changjian Shui, Fan Zhou, Christian Gagné, Boyu Wang

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning.

Active Learning

A Fourier Analytical Approach to Estimation of Smooth Functions in Gaussian Shift Model

no code implementations5 Nov 2019 Fan Zhou, Ping Li

Let $\mathbf{x}_j = \mathbf{\theta} + \mathbf{\epsilon}_j$, $j=1,\dots, n$ be i. i. d.

Quantifying Layerwise Information Discarding of Neural Networks

no code implementations10 Jun 2019 Haotian Ma, Yinqing Zhang, Fan Zhou, Quanshi Zhang

This paper presents a method to explain how input information is discarded through intermediate layers of a neural network during the forward propagation, in order to quantify and diagnose knowledge representations of pre-trained deep neural networks.

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

no code implementations23 May 2019 Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Ji Geng

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.

Few-Shot Learning General Classification +1

A Distributed Hierarchical SGD Algorithm with Sparse Global Reduction

no code implementations12 Mar 2019 Fan Zhou, Guojing Cong

Reducing communication in training large-scale machine learning applications on distributed platform is still a big challenge.

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks

no code implementations3 Dec 2018 Kunal Sankhe, Mauro Belgiovine, Fan Zhou, Shamnaz Riyaz, Stratis Ioannidis, Kaushik Chowdhury

This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical layer.

Classification General Classification

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Nonparametric Estimation of Low Rank Matrix Valued Function

no code implementations17 Feb 2018 Fan Zhou

We also propose another new estimator based on bias-reducing kernels to study the case when $A$ is not necessarily low rank and establish an upper bound on its risk measured by $L_{\infty}$-norm.

Matrix Completion Model Selection

On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization

no code implementations3 Aug 2017 Fan Zhou, Guojing Cong

We establish the convergence results of K-AVG for nonconvex objectives and explain why the K-step delay is necessary and leads to better performance than traditional parallel stochastic gradient descent which is a special case of K-AVG with $K=1$.

The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising

no code implementations5 Jul 2017 Dong Xia, Fan Zhou

In addition, the bounds established for HOSVD also elaborate the one-sided sup-norm perturbation bounds for the singular subspaces of unbalanced (or fat) matrices.

Denoising

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