Timezone: »
- [ 64973 ] Provable Generalization of Overparameterized Meta-learning Trained with SGD
- [ 64974 ] Elucidating the Design Space of Diffusion-Based Generative Models
- [ 64975 ] HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
- [ 64977 ] Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
- [ 64978 ] Fast Instrument Learning with Faster Rates
- [ 64980 ] Multi-view Subspace Clustering on Topological Manifold
- [ 64981 ] Distributional Reinforcement Learning for Risk-Sensitive Policies
Q&A on RocketChat immediately following Lightning Talks
Author Information
Yu Huang (Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University)
Tero Karras (NVIDIA)
Maxim Kodryan (HSE University)
Shiau Hong Lim (IBM)
Shudong Huang (Sichuan University)
Ziyu Wang (Tsinghua University)
Siqiao Xue (Ant Group)
Staff engineer at Alibaba / Ant Group, China.
ILYAS MALIK (IBM)
Ekaterina Lobacheva (HSE University)
Miika Aittala (NVIDIA)
Hongjie Wu (Sichuan University)

Hongjie Wu is currently a Ph.D. candidate with the Data Intelligence Laboratory, College of Computer Science, Sichuan University, China. He received his M.S. degree in Computer Science from Sichuan University in 2022. His research interests include machine learning and computer vision.
Yuhao Zhou (Tsinghua University)
Yingbin Liang (The Ohio State University)
Xiaoming Shi (Ant Group)
Jun Zhu (Tsinghua University)
Maksim Nakhodnov (Moscow State University, Lomonosov Moscow State University)
Timo Aila (NVIDIA)
Yazhou Ren (University of Electronic Science and Technology of China)
James Zhang
Longbo Huang (IIIS, Tsinghua Univeristy)
Dmitry Vetrov (Higher School of Economics, AI Research Institute)
Ivor Tsang (University of Technology Sydney)
Hongyuan Mei (Toyota Technological Institute at Chicago)
I am a second-year Ph.D. student (2016-) in Department of Computer Science at Johns Hopkins University, affiliated with the Center for Language and Speech Processing, where I am advised by Jason Eisner. My research interests are rooted in designing models and algorithms to solve challenging machine learning problems. I am currently working on continuous-time sequential modelling (e.g., neural Hawkes process).
Samuli Laine (NVIDIA)
Zenglin Xu (Harbin Institute of Technology Shenzhen)
Wentao Feng (Sichuan University)
Jiancheng Lv (Machine Intelligence Laboratory College of Computer Science, Sichuan University)
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2010 Poster: Efficient Relational Learning with Hidden Variable Detection »
Ni Lao · Jun Zhu · Liu Xinwang · Yandong Liu · William Cohen -
2009 Poster: Adaptive Regularization for Transductive Support Vector Machine »
Zenglin Xu · Rong Jin · Jianke Zhu · Irwin King · Michael R Lyu · Zhirong Yang -
2009 Spotlight: Adaptive Regularization for Transductive Support Vector Machine »
Zenglin Xu · Rong Jin · Jianke Zhu · Irwin King · Michael R Lyu · Zhirong Yang -
2009 Poster: Heavy-Tailed Symmetric Stochastic Neighbor Embedding »
Zhirong Yang · Irwin King · Zenglin Xu · Erkki Oja -
2009 Spotlight: Heavy-Tailed Symmetric Stochastic Neighbor Embedding »
Zhirong Yang · Irwin King · Zenglin Xu · Erkki Oja -
2008 Poster: An Extended Level Method for Efficient Multiple Kernel Learning »
Zenglin Xu · Rong Jin · Irwin King · Michael R Lyu -
2008 Poster: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
2007 Poster: Efficient Convex Relaxation for Transductive Support Vector Machine »
Zenglin Xu · Rong Jin · Jianke Zhu · Irwin King · Michael R Lyu