Hanxiao’s Homepage

Hanxiao Liu 6227 Gates-Hillman Center Carnegie Mellon University

Readme

I have joined Google Brain as a research scientist. Please refer to my new homepage.

I received my Ph.D. from the Language Technologies Institute, School of Computer Science at Carnegie Mellon University in 2018, working with Professor Yiming Yang. Prior to that, I received my B.E. from Department of Automation at Tsinghua University in 2013.

My research interests include deep learning, meta-learning and language understanding. Recently, I’m working on the automatic design of neural architectures.

Work Experience

Research Intern, DeepMind, London, Summer 2017.
Neural architecture search for image recognition, with Karen Simonyan.
Quantitative Research Intern, Citadel LLC, Chicago, Summer 2016.
Statistical arbitrage and high-frequency trading.
Research Intern, Microsoft Research Asia, Beijing, Spring 2013.
Game theory in computational advertising, with Wei Chen, Tao Qin and Tie-Yan Liu.

Publications — —

DARTS: Differentiable Architecture Search |Code|
Hanxiao Liu, Karen Simonyan, Yiming Yang.
ICLR 2019.
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks |Code|
Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu.
SIGIR 2018.
Hierarchical Representations for Efficient Architecture Search
Hanxiao Liu, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, Koray Kavukcuoglu.
ICLR 2018.
RACE: Large-Scale Reading Comprehension Dataset from Examinations |Data,Leaderboard|
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, Eduard Hovy.
EMNLP 2017.
Analogical Inference for Multi-Relational Embeddings |Slides,Code|
Hanxiao Liu, Yuexin Wu, Yiming Yang.
ICML 2017.
Gated-Attention Readers for Text Comprehension |Code|
Bhuwan Dhingra*, Hanxiao Liu*, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov.
ACL 2017.
Cross-Domain Kernel Induction for Transfer Learning
Wei-Cheng Chang, Yuexin Wu, Hanxiao Liu, Yiming Yang.
AAAI 2017.
Adaptive Smoothed Online Multi-Task Learning
Hanxiao Liu*, Keerthiram Murugesan*, Jaime G. Carbonell, Yiming Yang.
NeurIPS 2016.
Cross-Lingual Text Classification via Model Translation with Limited Dictionaries
Ruochen Xu, Yiming Yang, Hanxiao Liu, Andrew Hsi.
CIKM 2016.
Cross-Graph Learning of Multi-Relational Associations |Slides|
Hanxiao Liu and Yiming Yang.
ICML 2016.
Semi-Supervised Learning with Adaptive Spectral Transform
Hanxiao Liu and Yiming Yang.
AISTATS 2016.
Learning Concept Graphs from Online Educational Data |Code|
Hanxiao Liu, Wanli Ma, Yiming Yang, Jaime G. Carbonell.
Journal of AI Research.
Bipartite Edge Prediction via Transductive Learning over Product Graphs |Slides,Supp|
Hanxiao Liu and Yiming Yang.
ICML 2015.
Concept Graph Learning from Educational Data |Talk,Code|
Yiming Yang, Hanxiao Liu, Jaime G. Carbonell, Wanli Ma.
WSDM 2015.

“*” indicates equal contribution. Co-first authors are ordered alphabetically.

Professional Activities

Reviewer, International Conference on Machine Learning (ICML), 2016, 2017, 2018, 2019.
Reviewer, Neural Information Processing Systems (NeurIPS), 2016, 2017, 2018, 2019.
Reviewer, International Conference on Learning Representations (ICLR), 2019.
Reviewer, AAAI Conference on Artificial Intelligence (AAAI), 2017, 2018, 2019.
Reviewer, Journal of Machine Learning Research (JMLR).
Reviewer, Neural Computation.

Notes & Lectures

1.: Reinforcement Learning, Basics
2.: Reinforcement Learning, Policy Optimization
3.: Adversarial Examples
4.: Rademacher Complexity and VC Dimension
5.: Generative Adversarial Networks
6.: Spectral Analysis of Stationary Stochastic Process
7.: Screening Tests for the Lasso
8.: Large-Scale Stochastic Optimization
9.: Adaptive Stochastic Subgradient Methods
10.: Variational Inference for Bayes von Mises-Fisher Mixture
11.: The EM algorithm and Variational Bayes
12.: Spectral Algorithms