411 citations · 615 across the 21 of their papers we have counts for
21 papers
Navigating Sparsities in High-Dimensional Linear Contextual Bandits
Rui Zhao, Zihan Chen, Zemin Zheng
High-dimensional linear contextual bandit problems remain a significant challenge due to the curse of dimensionality. Existing methods typically consider either the model parameter…
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
Jingqing Ruan, Yihong Chen, Bin Zhang +8
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the…
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
Bin Zhang, Hangyu Mao, Lijuan Li +4
Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of exist…
Transformer in Transformer as Backbone for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Hao Chen +5
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network…
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning
Dongsheng Zhu, Zhenyu Mao, Jinghui Lu +2
Contrastive learning has recently achieved compelling performance in unsupervised sentence representation. As an essential element, data augmentation protocols, however, have not b…
What Makes Pre-trained Language Models Better Zero-shot Learners?
Jinghui Lu, Dongsheng Zhu, Weidong Han +3
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a post…
Auto-Encoding Adversarial Imitation Learning
Kaifeng Zhang, Rui Zhao, Ziming Zhang +1
Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imit…
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
Rui Zhao, Jinming Song, Yufeng Yuan +5
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data. Although such agents can be obtained through se…
MST: Masked Self-Supervised Transformer for Visual Representation
Zhaowen Li, Zhiyang Chen, Fan Yang +8
Transformer has been widely used for self-supervised pre-training in Natural Language Processing (NLP) and achieved great success. However, it has not been fully explored in visual…
Mutual Information State Intrinsic Control
Rui Zhao, Yang Gao, Pieter Abbeel +2
Reinforcement learning has been shown to be highly successful at many challenging tasks. However, success heavily relies on well-shaped rewards. Intrinsically motivated RL attempts…
The Vulnerability of the Neural Networks Against Adversarial Examples in Deep Learning Algorithms
Rui Zhao
With further development in the fields of computer vision, network security, natural language processing and so on so forth, deep learning technology gradually exposed certain secu…
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Yongchao Liu, Yue Jin, Yong Chen +4
Accelerating deep model training and inference is crucial in practice. Existing deep learning frameworks usually concentrate on optimizing training speed and pay fewer attentions t…
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Zhiliang Wu, Yinchong Yang, Yunpu Ma +4
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups. With the advance of electron…
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
Rui Zhao, Yang Gao, Pieter Abbeel +2
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal. In the natural world, intelligent organisms learn from internal drives, by…
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Rui Zhao, Xudong Sun, Volker Tresp
In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a re…
Neural Networks for Modeling Source Code Edits
Rui Zhao, David Bieber, Kevin Swersky +1
Programming languages are emerging as a challenging and interesting domain for machine learning. A core task, which has received significant attention in recent years, is building…
Curiosity-Driven Experience Prioritization via Density Estimation
Rui Zhao, Volker Tresp
In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning. However, the collected trajectories can easil…
Efficient Dialog Policy Learning via Positive Memory Retention
Rui Zhao, Volker Tresp
This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning. Training such agents with policy gradients typic…
Energy-Based Hindsight Experience Prioritization
Rui Zhao, Volker Tresp
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was…
Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
Rui Zhao, Volker Tresp
Learning goal-oriented dialogues by means of deep reinforcement learning has recently become a popular research topic. However, commonly used policy-based dialogue agents often end…
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong +4
Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models…