Search Results for author: Yufeng Zhang

Found 17 papers, 3 papers with code

Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate

no code implementations ICML 2020 Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks.

Imitation Learning

Relation-aware Heterogeneous Graph for User Profiling

no code implementations14 Oct 2021 Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang

User profiling has long been an important problem that investigates user interests in many real applications.

Node Classification

Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation

no code implementations19 Aug 2021 Zhihan Liu, Yufeng Zhang, Zuyue Fu, Zhuoran Yang, Zhaoran Wang

In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set.

Imitation Learning

Deep Active Learning for Text Classification with Diverse Interpretations

no code implementations15 Aug 2021 Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu

To train high-performing models with the minimal annotation cost, active learning is proposed to select and label the most informative samples, yet it is still challenging to measure informativeness of samples used in DNNs.

Active Learning Classification +1

Prevalent Behavior of Smooth Strongly Monotone Discrete-Time Dynamical Systems

no code implementations8 Mar 2021 Yi Wang, Jinxiang Yao, Yufeng Zhang

For C1-smooth strongly monotone discrete-time dynamical systems, it is shown that ``convergence to linearly stable cycles" is a prevalent asymptotic behavior in the measuretheoretic sense.

Dynamical Systems

On the Properties of Kullback-Leibler Divergence Between Gaussians

no code implementations10 Feb 2021 Yufeng Zhang, Wanwei Liu, Zhenbang Chen, Kenli Li, Ji Wang

Secondly, for any three $n$-dimensional Gaussians $\mathcal{N}_1, \mathcal{N}_2$ and $\mathcal{N}_3$, we find a bound of $KL(\mathcal{N}_1||\mathcal{N}_3)$ if $KL(\mathcal{N}_1||\mathcal{N}_2)$ and $KL(\mathcal{N}_2||\mathcal{N}_3)$ are bounded.

A Graph-based Relevance Matching Model for Ad-hoc Retrieval

1 code implementation28 Jan 2021 Yufeng Zhang, Jinghao Zhang, Zeyu Cui, Shu Wu, Liang Wang

To retrieve more relevant, appropriate and useful documents given a query, finding clues about that query through the text is crucial.

Document-level

No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness

no code implementations1 Jan 2021 Yufeng Zhang, Yunan Zhang, ChengXiang Zhai

To classify images, neural networks extract features from raw inputs and then sum them up with fixed weights via the fully connected layer.

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization

no code implementations21 Dec 2020 Zhuoran Yang, Yufeng Zhang, Yongxin Chen, Zhaoran Wang

Specifically, we prove that moving along the geodesic in the direction of functional gradient with respect to the second-order Wasserstein distance is equivalent to applying a pushforward mapping to a probability distribution, which can be approximated accurately by pushing a set of particles.

Variational Inference

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory

no code implementations NeurIPS 2020 Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang

Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks.

Q-Learning

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory

no code implementations8 Jun 2020 Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang

We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve?

Q-Learning

Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

2 code implementations ACL 2020 Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, Liang Wang

We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.

Classification Document Embedding +2

Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate

no code implementations8 Mar 2020 Yufeng Zhang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Generative adversarial imitation learning (GAIL) demonstrates tremendous success in practice, especially when combined with neural networks.

Imitation Learning

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