I’m currently a 4th-year Ph.D. candidate at UCLA CS, advised by Prof. Cho-Jui Hsieh. My research interest lies in automated and efficient machine learning. Prior to UCLA, I received my B.Eng. degree in 2019 from the Department of Electronic Engineering, Tsinghua University.
[Publications] [Awards] [Experience] [Education] [Service] [Teaching] [Contact]
[02/2023] Check out our Lion optimizer, discovered by symbolic program search.
[01/2022] Three papers (1 spotlight) were accepted to ICLR'22.
[06/2021] I joined Google Brain, AutoML Team as a student researcher.
[02/2021] Our paper on “robust and accurate object detection” got accepted to CVPR'21.
[01/2021] Two papers (1 oral) got accepted to ICLR'21 with DARTS-PT won the Outstanding Paper Award.
[07/2020] I started my internship at Google Research, Perception Team.
[05/2020] Our paper on “stabilizing neural architecture search” got accepted to ICML'20.
[01/2020] One paper on “searching for neural interaction function” was accepted to WWW'20.
^indicates equal contribution
Symbolic Discovery of Optimization Algorithms
Xiangning Chen^, Chen Liang^, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le
[Code] [Implementation by @lucidrains] [Timm] [Optax] [Praxis] [Twitter #1] [Twitter #2] [SyncedReview]
Random Sharpness-Aware Minimization
Yong Liu, Siqi Mai, Minhao Cheng, Xiangning Chen, Cho-Jui Hsieh, Yang You
Towards Efficient and Scalable Sharpness-Aware Minimization
Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You
CVPR 2022 [Twitter]
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations
Xiangning Chen, Cho-Jui Hsieh, Boqing Gong
ICLR 2022 (spotlight) [JAX Checkpoint] [PyTorch Checkpoint] [Twitter]
Concurrent Adversarial Learning for Large-Batch Training
Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You
Learning to Schedule Learning Rate with Graph Neural Networks
Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving
Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh
Robust and Accurate Object Detection via Adversarial Learning
Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong
CVPR 2021 [TensorFlow Checkpoint] [Colab] [Twitter]
Rethinking Architecture Selection in Differentiable NAS
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh
ICLR 2021 (oral, outstanding paper award) [Code]
DrNAS: Dirichlet Neural Architecture Search
Xiangning Chen^, Ruochen Wang^, Minhao Cheng^, Xiaocheng Tang, Cho-Jui Hsieh
ICLR 2021 [Code]
Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
Xiangning Chen, Cho-Jui Hsieh
ICML 2020 [Code]
Efficient Neural Interaction Function Search for Collaborative Filtering
Quanming Yao^, Xiangning Chen^, James T. Kwok, Yong Li, Cho-Jui Hsieh
WWW 2020 [Code]
Cross-domain Recommendation Without Sharing User-relevant Data
Chen Gao, Xiangning Chen, Fuli Feng, Kai Zhao, Xiangnan He, Yong Li, Depeng Jin
Neural Multi-task Recommendation from Multi-behavior Data
Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Depeng Jin
[02/2022] Meta Fellowship Finalist
[01/2022] Amazon Science Fellowship
[03/2021] ICLR Outstanding Paper Award
[06/2019] Outstanding Graduate & Bachelor Thesis, Tsinghua University
[11/2018] 2nd place (feedback phase), NIPS AutoML Challenge
[06/2018] Qualcomm Scholarship
[06/2017] Guangzhou Pharmaceutical Corporation Scholarship
[06/2016] Geru Zheng Scholarship
[07/2021 - present] Student Researcher, Google Brain, AutoML Team, Mountain View, CA
[07/2020 - 06/2021] Student Researcher, Google Research, Perception Team, Seattle, WA
[02/2019 - 08/2019] Research Intern, 4Paradigm, Beijing, China
[07/2018 - 11/2018] Research Assistant, Massachusetts Institute of Technology, Cambridge, MA
[01/2018 - 06/2018] Research Intern, Microsoft Research Asia, Beijing, China
[09/2019 - present] Ph.D. student in Computer Science, University of California, Los Angeles
[09/2016 - 07/2019] B.Ec. in Economics (2nd Degree), Tsinghua University
[09/2015 - 07/2019] B.Eng. in Electronic Engineering, Tsinghua University
PC Member / Reviewer: ICML (2021-), ICLR (2021-), NeurIPS (2020-), JMLR (2022-), TMLR (2022-), CVPR (2021-), ICCV (2021-), ECCV (2020-), AAAI (2021-)
Teaching Assistant, UCLA CS 260C: Deep Learning (Winter 2022)
Teaching Assistant, UCLA CS 180: Algorithms & Complexity (Spring 2021, Fall 2021)
Email: xiangning at cs dot ucla dot edu