Program (details keep updating)

* Wednesday, 14 November, 2018: Workshops and Tutorials, Main Conference
* Thursday, 15 November, 2018: Main Conference
* Friday, 16 November, 2018: Main Conference

Venue: International Conference Center (connected to the Mechanical Engineering Building), Beijing Jiaotong University

Oral presentation: a) Long presentation (L) - 17 minutes including 2-minutes Q/A;
b) Short presentation (S) – 10 minutes including 1-minute Q/A.

Poster presentation: The size of poster board is 1.0m (width) * 2.0m (height), and the authors are suggested to prepare their poster no larger than 0.9m (width) * 1.8m (height).

Wednesday, 14 November - Tutorials and Workshops / Main Conference

Venue: International Conference Center, Beijing Jiaotong University

7:30 Registration Open – Foyer
8:30 – 10:00 Tutorial 1: Dynamic System and Optimal Control Perspective of Deep Learning and Beyond (Multi-functional Hall, 1/F)
Workshop 1: ACML 2018 Workshop on Multi-output Learning (ACML-Mol’18) (Meeting Room #1, B1)
Workshop 2: The 3rd Asian Workshop on Reinforcement Learning (AWRL’18) (Meeting Room #4, B1)
Workshop 3: ACML 2018 Workshop on Machine Learning in China (MLChina’18) (Lecture Hall, 2/F)
Workshop 4: ACML 2018 Workshop on Machine Learning in Education (Meeting Room #5, B1)
10:00 – 10:20 Coffee Break
10:20 – 11:50 Workshop 1: ACML 2018 Workshop on Multi-output Learning (ACML-Mol’18) (Meeting Room #1, B1)
Workshop 2: The 3rd Asian Workshop on Reinforcement Learning (AWRL’18) (Meeting Room #4, B1)
Workshop 3: ACML 2018 Workshop on Machine Learning in China (MLChina’18) (Lecture Hall, 2/F)
Workshop 4: ACML 2018 Workshop on Machine Learning in Education (Meeting Room #5, B1)
11:50 – 13:30 Lunch
13:30 – 15:30 Tutorial 2: Dual Learning: Algorithms, Applications and Challenges (Meeting Room #1, B1)
Workshop 2: The 3rd Asian Workshop on Reinforcement Learning (AWRL’18) (Meeting Room #4, B1)
Workshop 3: ACML 2018 Workshop on Machine Learning in China (MLChina’18) (Lecture Hall, 2/F)
Workshop 4: ACML 2018 Workshop on Machine Learning in Education (Meeting Room #5, B1)
15:30 – 15:50 Coffee Break
15:50 – 17:50 Session 1: Bayesian and Probabilistic Machine Learning
(4 long presentations (L) + 5 short presentations (S))
  • Good Arm Identification via Bandit Feedback (L)
    Hideaki Kano, Junya Honda, Kentaro Sakamaki, Kentaro Matsuura, Atsuyoshi Nakamura, Masashi Sugiyama
  • Bayesian Optimistic Kullback-Leibler Exploration (L)
    Kanghoon Lee, Geon-Hyeong Kim, Pedro Ortega, Daniel D. Lee, Kee-Eung Kim
  • Annotation Cost-sensitive Active Learning by Tree Sampling (L)
    Yu-Lin Tsou, Hsuan-Tien Lin
  • Structured Gaussian Processes with Twin Multiple Kernel Learning (L)
    Çiğdem Ak, Önder Ergönül, Mehmet Gönen
  • Feature-correlation-aware Gaussian Process Latent Variable Model (S)
    Ping Li, Songcan Chen
  • Hypernetwork-based Implicit Posterior Estimation and Model Averaging of Convolutional Neural Networks (S)
    Kenya Ukai, Takashi Matsubara, Kuniaki Uehara
  • Profitable Bandits (S)
    Mastane Achab, Stephan Clémençon, Aurélien Garivier
  • Fast Randomized PCA for Sparse Data (S)
    Xu Feng, Yuyang Xie, Mingye Song, Wenjian Yu, Jie Tang
  • Efficient Mechanisms for Peer Grading and Dueling Bandits (S)
    Chuang-Chieh Lin, Chi-Jen Lu
18:30 – 20:30 Welcome Reception – Beijing Friendship Hotel (Ya-Shi Hall, Grand Building)

Thursday, 15 November - Main Conference

Venue: Lecture Hall, 2/F, International Conference Center, Beijing Jiaotong University

8:00 Registration Open – Foyer
8:30 – 8:40 Conference Opening
8:40 – 9:40 Keynote Talk: Clustering - what both Theoreticians and Practitioners are Doing Wrong
Speaker: Prof. Shai Ben-David, University of Waterloo, Canada
9:40 – 10:00 Coffee Break
10:00 – 12:00 Session 2: Multi-label, Multi-Instance and Crowdsourcing
(4 long presentations (L) + 5 short presentations (S))
  • Supervised Representation Learning for Multi-label Classification (L)
    Ming Huang, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang, Qing He
  • Millionaire: A Hint-guided Approach for Crowdsourcing (L)
    Bo Han, Quanming Yao, Yuangang Pan, Ivor W. Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama
  • Knowledge Guided Multi-instance Multi-label Learning via Neural Networks in Medicines Prediction (L)
    Junyuan Shang, Shenda Hong, Yuxi Zhou, Meng Wu, Hongyan Li
  • Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing (L)
    Yuan Jin, Mark Carman, Ye Zhu, Wray Buntine
  • Making Classifier Chains Resilient to Class Imbalance (S)
    Bin Liu, Grigorios Tsoumakas
  • Deep Correlation Structure Preserved Label Space Embedding for Multi-label Classification (S)
    Kaixiang Wang, Ming Yang, Wanqi Yang, YiLong Yin
  • Deep Multi-instance Learning with Dynamic Pooling (S)
    Yongluan Yan, Xinggang Wang, Xiaojie Guo, Jiemin Fang, Wenyu Liu, Junzhou Huang
  • A Joint Selective Mechanism for Abstractive Sentence Summarization (S)
    Junjie Fu, Gongshen Liu
  • A Self-Attentive Hierarchical Model for Jointly Improving Text Summarization and Sentiment Classification (S)
    Hongli Wang, Jiangtao Ren
12:00 – 13:15 Lunch – HongGuoYuan Hotel
Poster Session – Poster presentation for papers in Session #1, #2, #3, #4 (Multi-functional Hall, 1/F)
13:15 – 14:00 Invited Talk: Something Old, Something New, Something Borrowed, ...
Speaker: Prof. Wray Buntine, Monash University, Australia
14:00 – 16:00 Session 3: Optimization and Sparsity
(4 long presentations (L) + 5 short presentations (S))
  • Preconditioned Conjugate Gradient Methods in Truncated Newton Frameworks for Large-scale Linear Classification (L)
    Chih-Yang Hsia, Wei-Lin Chiang, Chih-Jen Lin
  • N-ary Decomposition for Multi-class Classification (L)
    Joey Tianyi Zhou, Ivor W. Tsang, Shen-Shyang Ho, Klaus-Robert Müller
  • Supporting Non-Smooth Loss Functions: An Accelerated Stochastic Gradient Method for Regularized Empirical Risk Minimization (L)
    Jingchang Liu, Linli Xu, Shuheng Shen, Qing Ling
  • CHS-NET: A Cascaded Neural Network with Semi-Focal Loss for Mitosis Detection (L)
    Yanbo Ma, Jiarui Sun, Qiuhao Zhou, Kaili Cheng, Xuesong Chen, Yong Zhao
  • Construction of Incoherent Dictionaries via Direct Babel Function Minimization (S)
    Huan Li, Zhouchen Lin
  • Joint Patch-Group Based Sparse Representation for Image Inpainting (S)
    Zhiyuan Zha, Xin Yuan, Bihan Wen, Jiantao Zhou, Ce Zhu
  • A Scalable Heterogeneous Parallel SOM Based on MPI/CUDA (S)
    Yao Liu, Jun Sun, Qing Yao, Su Wang, Kai Zheng, Yan Liu
  • Optimization Algorithm Inspired Deep Neural Network Structure Design (S)
    Huan Li, Yibo Yang, Dongmin Chen, Zhouchen Lin
  • ASVRG: Accelerated Proximal SVRG (S)
    Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin
16:00 – 16:20 Coffee Break
16:20 – 18:20 Session 4: Deep Learning
(4 long presentations (L) + 5 short presentations (S))
  • Boosting Dynamic Programming with Neural Networks for Solving NP-hard Problems (L)
    Feidiao Yang, Tiancheng Jin, Tie-Yan Liu, Xiaoming Sun, Jialin Zhang
  • Learning Selfie-Friendly Abstraction from Artistic Style Images (L)
    Yicun Liu, Jimmy Ren, Jianbo Liu, Jiawei Zhang, Xiaohao Chen
  • Batch Normalized Deep Boltzmann Machines (L)
    Hung Vu, Tu Dinh Nguyen, Trung Le, Wei Luo, Dinh Phung
  • Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification (L)
    Shaoning Zeng, Bob Zhang, Yanghao Zhang, Jianping Gou
  • Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss (S)
    Guopeng Zhang, Jinhua Xu
  • Multidimensional Time Series Anomaly Detection: A GRU-based Gaussian Mixture Variational Autoencoder Approach (S)
    Yifan Guo, Weixian Liao, Qianlong Wang, Lixing Yu, Tianxi Ji, Pan Li
  • ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks (S)
    Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov
  • ZoomNet: Deep Aggregation Learning for High-Performance Small Pedestrian Detection (S)
    Chong Shang, Haizhou Ai, Zijie Zhuang, Long Chen, Junliang Xing
  • RICAP: Random Image Cropping and Patching Data Augmentation for Deep CNNs (S)
    Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
19:00 – 21:00 Conference Banquet

Friday, 16 November - Main Conference

Venue: Lecture Hall, 2/F, International Conference Center, Beijing Jiaotong University

8:00 Registration Open – Foyer
8:40 – 9:40 Keynote Talk: Machine Learning in Autonomous Systems: Theory and Practice
Speaker: Prof. Daniel D. Lee, Cornell Tech & Samsung Research, USA
9:40 – 10:00 Coffee Break
10:00 – 12:00 Session 5: Deep/Adversarial/Reinforcement Learning and Privacy
(4 long presentations (L) + 5 short presentations (S))
  • Deep Fully-Connected Part-Based Models for Human Pose Estimation (L)
    Rodrigo de Bem, Anurag Arnab, Stuart Golodetz, Michael Sapienza, Philip Torr
  • Adversarial TableQA: Attention Supervision for Question Answering on Tables (L)
    Minseok Cho, Reinald Kim Amplayo, Seung-won Hwang, Jonghyuck Park
  • Adversarial Neural Machine Translation (L)
    Lijun Wu, Yingce Xia, Fei Tian, Li Zhao, Tao Qin, Jianhuang Lai, Tie-Yan Liu
  • Person Re-identification by Mid-level Attribute and Part-based Identity Learning (L)
    Guopeng Zhang, Jinhua Xu
  • Cartoon-to-Photo Facial Translation with Generative Adversarial Networks(S)
    Junhong Huang, Mingkui Tan, Yuguang Yan, Chunmei Qing, Qingyao Wu, Zhuliang Yu
  • Refining Synthetic Images with Semantic Layouts by Adversarial Training (S)
    Tongtong Zhao, Yuxiao Yan, JinJia Peng, HaoHui Wei, Xianping Fu
  • CCNet: Cluster-Coordinated Net for Learning Multi-agent Communication Protocols with Reinforcement Learning (S)
    Xin Wen, Zheng-Jun Zha, Zilei Wang, Liansheng Zhuang, Houqiang Li
  • SecureNets: Secure Inference of Deep Neural Networks on an Untrusted Cloud (S)
    Xuhui Chen, Jinlong Ji, Lixing Yu, Changqing Luo, Pan Li
  • TVT: Two-View Transformer Network for Video Captioning (S)
    Ming Chen, Yingming Li, Zhongfei Zhang, Siyu Huang
12:00 – 13:15 Lunch – HongGuoYuan Hotel
Poster Session – Poster presentation for papers in Session #5, #6, #7 (Multi-functional Hall, 1/F)
13:15 – 14:00 Invited Talk: AI for Transportation
Speaker: Dr. Jieping Ye, Didi AI Labs & University of Michigan, China
14:00 – 16:00 Session 6: Weakly-supervised or Unsupervised Learning
(4 long presentations (L) + 5 short presentations (S))
  • RDEC: Integrating Regularization into Deep Embedded Clustering for Imbalanced Datasets (L)
    Yaling Tao, Kentaro Takagi, Kouta Nakata
  • Clustering Uncertain Graphs with Node Attributes (L)
    Yafang Li, Xiangnan Kong, Caiyan Jia, Jianqiang Li
  • Clustering Induced Kernel Learning (L)
    Khanh Nguyen, Nhan Dam, Trung Le, Tu Dinh Nguyen, Dinh Phung
  • Extracting Invariant Features From Images Using An Equivariant Autoencoder (L)
    Denis Kuzminykh, Daniil Polykovskiy, Alexander Zhebrak
  • Co-regularized Multi-view Subspace Clustering (S)
    Hong Yu, Tiantian Zhang, Yahong Lian, Yu Cai
  • Unsupervised Heterogeneous Domain Adaptation with Sparse Feature Transformation (S)
    Chen Shen, Yuhong Guo
  • A Faster Sampling Algorithm for Spherical k-means (S)
    Rameshwar Pratap, Anup Deshmukh, Pratheeksha Nair, Tarun Dutt
  • Deep Embedded Clustering with Data Augmentation (S)
    Xifeng Guo, En Zhu, Xinwang Liu, Jianping Yin
  • An Empirical Evaluation of Sketched SVD and its Application to Leverage Score Ordering (S)
    Hui Han Chin, Paul Pu Liang
16:00 – 16:20 Coffee Break
16:20 – 18:20 Session 7: Machine Learning Application
(3 long presentations (L) + 7 short presentations (S))
  • End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching (L)
    Li Zhang, Quanhong Wang, Haihua Lu, Yong Zhao
  • End-to-End Time Series Imputation via Resdidual Short Pants (L)
    Lifeng Shen, Qianli Ma, Sen Li
  • Relative Attribute Learning with Deep Attentive Cross-image Representation (L)
    Zeshang Zhang, Yingming Li, Zhongfei Zhang
  • Underwater Image Restoration Based on Convolutional Neural Network (S)
    Yan Hu, Keyan Wang, Xi Zhao, Hui Wang, Yunsong Li
  • Stock Price Prediction Using Attention-based Multi-Input LSTM (S)
    Hao Li, Yanyan Shen, Yanmin Zhu
  • Who Are Raising Their Hands? Hand-Raiser Seeking Based on Object Detection and Pose Estimation (S)
    Huayi Zhou, Fei Jiang, Ruimin Shen
  • Concorde: Morphological Agreement in Conversational Models (S)
    Daniil Polykovskiy, Dmitry Soloviev, Sergey Nikolenko
  • Character-based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction (S)
    Yanzeng Li, Tingwen Liu, Diying Li, Quangang Li, Jinqiao Shi, Yanqiu Wang
  • A Data Driven Approach to Predicting Rating Scores for New Restaurants (S)
    Xiaochen Wang, Yanyan Shen, Yanmin Zhu
  • Fast Dynamic Convolutional Neural Networks for Visual Tracking (S)
    Zhiyan Cui, Na Lu, Xue Jing, Xiahao Shi
18:20 – 18:30 Conference Closing