Author name cluster

Guangjian Tian

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

ICRA Conference 2024 Conference Paper

Safe Table Tennis Swing Stroke with Low-Cost Hardware

Francesco Cursi
Marcus Kalander
Shuang Wu 0005
Xidi Xue
Yu Tian
Guangjian Tian
Xingyue Quan
Jianye Hao

Playing table tennis with a human player is a challenging robotic task due to its dynamic nature. Despite a number of researches being devoted to developing robotic table tennis systems, most of the works have demanding hardware requirements and ignore safety measures when generating the swing stoke. To address these issues, we propose a safe motion planning framework that fully pushes the robotic hardware performance limits to play table tennis. In particular, we propose a pipeline to generate manipulator joint trajectories with environmental safety constraints and scale the trajectories to satisfy joint movement limitations. We use three different agents to validate the planning algorithm with our handmade robot platform in both simulation and real-world environments.

Details

ICLR Conference 2023 Conference Paper

Cross-Layer Retrospective Retrieving via Layer Attention

Yanwen Fang
Yuxi Cai
Jintai Chen
Jingyu Zhao 0001
Guangjian Tian
Guodong Li

More and more evidence has shown that strengthening layer interactions can enhance the representation power of a deep neural network, while self-attention excels at learning interdependencies by retrieving query-activated information. Motivated by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields. A light-weighted version of MRLA is also proposed to reduce the quadratic computation cost. The proposed layer attention mechanism can enrich the representation power of many state-of-the-art vision networks, including CNNs and vision transformers. Its effectiveness has been extensively evaluated in image classification, object detection and instance segmentation tasks, where improvements can be consistently observed. For example, our MRLA can improve 1.6% Top-1 accuracy on ResNet-50, while only introducing 0.16M parameters and 0.07B FLOPs. Surprisingly, it can boost the performances by a large margin of 3-4% box AP and mask AP in dense prediction tasks. Our code is available at https://github.com/joyfang1106/MRLA.

Details

ICLR Conference 2023 Conference Paper

Encoding Recurrence into Transformers

Feiqing Huang
Kexin Lu
Yuxi Cai
Zhen Qin
Yanwen Fang
Guangjian Tian
Guodong Li

This paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be encapsulated into the positional encodings of a multihead self-attention, and this makes it possible to seamlessly incorporate these recurrent dynamics into a Transformer, leading to a new module, Self-Attention with Recurrence (RSA). The proposed module can leverage the recurrent inductive bias of REMs to achieve a better sample efficiency than its corresponding baseline Transformer, while the self-attention is used to model the remaining non-recurrent signals. The relative proportions of these two components are controlled by a data-driven gated mechanism, and the effectiveness of RSA modules are demonstrated by four sequential learning tasks.

Details

IJCAI Conference 2022 Conference Paper

Understanding and Mitigating Data Contamination in Deep Anomaly Detection: A Kernel-based Approach

Shuang Wu
Jingyu Zhao
Guangjian Tian

Deep anomaly detection has become popular for its capability of handling complex data. However, training a deep detector is fragile to data contamination due to overfitting. In this work, we study the performance of the anomaly detectors under data contamination and construct a data-efficient countermeasure against data contamination. We show that training a deep anomaly detector induces an implicit kernel machine. We then derive an information-theoretic bound of performance degradation with respect to the data contamination ratio. To mitigate the degradation, we propose a contradicting training approach. Apart from learning normality on the contaminated dataset, our approach discourages learning an additional small auxiliary dataset of labeled anomalies. Our approach is much more affordable than constructing a completely clean training dataset. Experiments on public datasets show that our approach significantly improves anomaly detection in the presence of contamination and outperforms some recently proposed detectors.

PDF Details DOI

ICML Conference 2022 Conference Paper

Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach

Shuang Wu 0005
Ling Shi 0001
Jun Wang 0012
Guangjian Tian

The REINFORCE algorithm \cite{williams1992simple} is popular in policy gradient (PG) for solving reinforcement learning (RL) problems. Meanwhile, the theoretical form of PG is from \cite{sutton1999policy}. Although both formulae prescribe PG, their precise connections are not yet illustrated. Recently, \citeauthor{nota2020policy} (\citeyear{nota2020policy}) have found that the ambiguity causes implementation errors. Motivated by the ambiguity and implementation incorrectness, we study PG from a perturbation perspective. In particular, we derive PG in a unified framework, precisely clarify the relation between PG implementation and theory, and echos back the findings by \citeauthor{nota2020policy}. Diving into factors contributing to empirical successes of the existing erroneous implementations, we find that small approximation error and the experience replay mechanism play critical roles.

Details

IJCAI Conference 2021 Conference Paper

State-Aware Value Function Approximation with Attention Mechanism for Restless Multi-armed Bandits

Shuang Wu
Jingyu Zhao
Guangjian Tian
Jun Wang

The restless multi-armed bandit (RMAB) problem is a generalization of the multi-armed bandit with non-stationary rewards. Its optimal solution is intractable due to exponentially large state and action spaces with respect to the number of arms. Existing approximation approaches, e. g. , Whittle's index policy, have difficulty in capturing either temporal or spatial factors such as impacts from other arms. We propose considering both factors using the attention mechanism, which has achieved great success in deep learning. Our state-aware value function approximation solution comprises an attention-based value function approximator and a Bellman equation solver. The attention-based coordination module capture both spatial and temporal factors for arm coordination. The Bellman equation solver utilizes the decoupling structure of RMABs to acquire solutions with significantly reduced computation overheads. In particular, the time complexity of our approximation is linear in the number of arms. Finally, we illustrate the effectiveness and investigate the properties of our proposed method with numerical experiments.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

Longyuan Li
Jihai Zhang
Junchi Yan
Yaohui Jin
Yunhao Zhang
Yanjie Duan
Guangjian Tian

Time-series is ubiquitous across applications, such as transportation, finance and healthcare. Time-series is often influenced by external factors, especially in the form of asynchronous events, making forecasting difficult. However, existing models are mainly designated for either synchronous time-series or asynchronous event sequence, and can hardly provide a synthetic way to capture the relation between them. We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. In addition, an aligned time coding and an auxiliary transition scheme are carefully devised for batched training on unaligned sequences. Our model can be trained effectively using stochastic variational inference and generates probabilistic predictions with Monte-Carlo simulation. Furthermore, our model produces accurate, sharp and more realistic probabilistic forecasts. We also show that modeling asynchronous event sequences is crucial for multi-horizon time-series forecasting.

PDF Details

AAAI Conference 2020 Conference Paper

Compact Autoregressive Network

Di Wang
Feiqing Huang
Jingyu Zhao
Guodong Li
Guangjian Tian

Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence. However, when handling high-dimensional inputs and outputs, the massive amount of parameters in the network leads to expensive computational cost and low learning efﬁciency. The problem can be alleviated slightly by introducing one more narrow hidden layer to the network, but the sample size required to achieve a certain training error is still substantial. To address this challenge, we rearrange the weight matrices of a linear autoregressive network into a tensor form, and then make use of Tucker decomposition to represent lowrank structures. This leads to a novel compact autoregressive network, called Tucker AutoRegressive (TAR) net. Interestingly, the TAR net can be applied to sequences with longrange dependence since the dimension along the sequential order is reduced. Theoretical studies show that the TAR net improves the learning efﬁciency, and requires much fewer samples for model training. Experiments on synthetic and real-world datasets demonstrate the promising performance of the proposed compact network.

PDF Details

ICML Conference 2020 Conference Paper

Do RNN and LSTM have Long Memory?

Jingyu Zhao 0001
Feiqing Huang
Jia Lv
Yanjie Duan
Zhen Qin
Guodong Li
Guangjian Tian

The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in mind, this paper raises the question - do RNN and LSTM have long memory? We answer it partially by proving that RNN and LSTM do not have long memory from a statistical perspective. A new definition for long memory networks is further introduced, and it requires the model weights to decay at a polynomial rate. To verify our theory, we convert RNN and LSTM into long memory networks by making a minimal modification, and their superiority is illustrated in modeling long-term dependence of various datasets.

Details

IJCAI Conference 2019 Conference Paper

Ensemble-based Ultrahigh-dimensional Variable Screening

Wei Tu
Dong Yang
Linglong Kong
Menglu Che
Qian Shi
Guodong Li
Guangjian Tian

Since the sure independence screening (SIS) method by Fan and Lv, many different variable screening methods have been proposed based on different measures under different models. However, most of these methods are designed for specific models. In practice, we often have very little information about the data generating process and different methods can result in very different sets of features. The heterogeneity presented here motivates us to combine various screening methods simultaneously. In this paper, we introduce a general ensemble-based framework to efficiently combine results from multiple variable screening methods. The consistency and sure screening property of proposed framework has been established. Extensive simulation studies confirm our intuition that the proposed ensemble-based method is more robust against model specification than using single variable screening method. The proposed ensemble-based method is used to predict attention deficit hyperactivity disorder (ADHD) status using brain function connectivity (FC).

PDF Details