Search Results for author: Yuanhao Xiong

Found 12 papers, 6 papers with code

Efficient Non-Parametric Optimizer Search for Diverse Tasks

no code implementations27 Sep 2022 Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh

Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems.

AutoML

FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning

1 code implementation20 Jul 2022 Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh

Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communication constraints.

Federated Learning Image Classification

Extreme Zero-Shot Learning for Extreme Text Classification

1 code implementation NAACL 2022 Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit Dhillon

To learn the semantic embeddings of instances and labels with raw text, we propose to pre-train Transformer-based encoders with self-supervised contrastive losses.

Classification Multi Label Text Classification +3

Adversarial Attack across Datasets

no code implementations13 Oct 2021 Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Lihong Cao, Cho-Jui Hsieh

In this paper, we define a Generalized Transferable Attack (GTA) problem where the attacker doesn't know this information and is acquired to attack any randomly encountered images that may come from unknown datasets.

Adversarial Attack Image Classification

Learning to Schedule Learning rate with Graph Neural Networks

no code implementations ICLR 2022 Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh

By constructing a directed graph for the underlying neural network of the target problem, GNS encodes current dynamics with a graph message passing network and trains an agent to control the learning rate accordingly via reinforcement learning.

Image Classification Stochastic Optimization

Training Meta-Surrogate Model for Transferable Adversarial Attack

1 code implementation5 Sep 2021 Yunxiao Qin, Yuanhao Xiong, JinFeng Yi, Cho-Jui Hsieh

In this paper, we tackle this problem from a novel angle -- instead of using the original surrogate models, can we obtain a Meta-Surrogate Model (MSM) such that attacks to this model can be easier transferred to other models?

Adversarial Attack

Learning to Learn with Smooth Regularization

no code implementations1 Jan 2021 Yuanhao Xiong, Cho-Jui Hsieh

Recent decades have witnessed great prosperity of deep learning in tackling various problems such as classification and decision making.

Decision Making Few-Shot Learning

How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers

no code implementations19 Oct 2020 Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan, Yang You, Si Si, Cho-Jui Hsieh

For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.

Graph Mining

Learning to Learn by Zeroth-Order Oracle

1 code implementation ICLR 2020 Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh

In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules.

Adversarial Attack

Learning Phase Competition for Traffic Signal Control

1 code implementation12 May 2019 Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li

Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions.

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

5 code implementations ICLR 2019 Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu sun

Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.

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