Search Results for author: Yan Li

Found 55 papers, 10 papers with code

Deep Reinforcement Learning with Smooth Policy

no code implementations ICML 2020 Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao

In contrast to policy parameterized by linear/reproducing kernel functions, where simple regularization techniques suffice to control smoothness, for neural network based reinforcement learning algorithms, there is no readily available solution to learn a smooth policy.

Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL

1 code implementation NeurIPS 2021 Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao

Theoretically, under a weak coverage assumption that the experience dataset contains enough information about the optimal policy, we prove that for an episodic mean-field MDP with a horizon $H$ and $N$ training trajectories, SAFARI attains a sub-optimality gap of $\mathcal{O}(H^2d_{\rm eff} /\sqrt{N})$, where $d_{\rm eff}$ is the effective dimension of the function class for parameterizing the value function, but independent on the number of agents.

Multi-agent Reinforcement Learning

Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits

no code implementations10 Oct 2021 Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan

We show that incorporating frequency information of tokens in the embedding learning problems leads to provably efficient algorithms, and demonstrate that common adaptive algorithms implicitly exploit the frequency information to a large extent.

Language Modelling Recommendation Systems

Single Image Dehazing with An Independent Detail-Recovery Network

1 code implementation22 Sep 2021 Yan Li, De Cheng, Jiande Sun, Dingwen Zhang, Nannan Wang, Xinbo Gao

In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.

Image Dehazing Single Image Dehazing

MutualGraphNet: A novel model for motor imagery classification

no code implementations2 Sep 2021 Yan Li, Ning Zhong, David Taniar, Haolan Zhang

Experiments are conducted on motor imagery EEG data set and we compare our model with the current state-of-the-art approaches and the results suggest that MutualGraphNet is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.

Classification EEG

Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data

no code implementations15 Aug 2021 Yan Li, Caleb Ju, Ethan X. Fang, Tuo Zhao

We show that BPPA attains non-trivial margin, which closely depends on the condition number of the distance generating function inducing the Bregman divergence.

Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach

no code implementations18 May 2021 Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha

To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.

Multi-agent Reinforcement Learning

Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results

no code implementations2 May 2021 Harish Panneer Selvam, Yan Li, Pengyue Wang, William F. Northrop, Shashi Shekhar

Given an on-board diagnostics (OBD) dataset and a physics-based emissions prediction model, this paper aims to develop an accurate and computational-efficient AI (Artificial Intelligence) method that predicts vehicle emissions.

Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

2 code implementations22 Mar 2021 Yiqun Xie, Shashi Shekhar, Yan Li

Mapping of spatial hotspots, i. e., regions with significantly higher rates of generating cases of certain events (e. g., disease or crime cases), is an important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc.

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization

no code implementations24 Feb 2021 Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao

Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems.

IFoodCloud: A Platform for Real-time Sentiment Analysis of Public Opinion about Food Safety in China

no code implementations17 Feb 2021 Dachuan Zhang, Haoyang Zhang, Zhisheng Wei, Yan Li, Zhiheng Mao, Chunmeng He, Haorui Ma, Xin Zeng, Xiaoling Xie, Xingran Kou, Bingwen Zhang

The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues.

Sentiment Analysis

Estimates of the early EM emission from compact binary mergers

no code implementations9 Feb 2021 Yan Li, Rong-Feng Shen

We estimate their luminosities and time scales as functions of the chirp mass which is the most readily constrained parameter from the gravitational wave detections of these events.

High Energy Astrophysical Phenomena

Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

no code implementations25 Aug 2020 David Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga S. Mannem, Tuo Zhao, Hua Wang

We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits.

Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles

no code implementations1 Jun 2020 Pengyue Wang, Yan Li, Shashi Shekhar, William F. Northrop

Adversarial examples are firstly investigated in the area of computer vision: by adding some carefully designed ''noise'' to the original input image, the perturbed image that cannot be distinguished from the original one by human, can fool a well-trained classifier easily.

A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles

no code implementations1 Jun 2020 Pengyue Wang, Yan Li, Shashi Shekhar, William F. Northrop

A physics model-guided online Bayesian framework is described and validated on large number of in-use driving samples of EREVs used for last-mile package delivery.

How to Retrain Recommender System? A Sequential Meta-Learning Method

1 code implementation27 May 2020 Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang

Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.

Meta-Learning Recommendation Systems

Implicit Bias of Gradient Descent based Adversarial Training on Separable Data

no code implementations ICLR 2020 Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao

Specifically, we show that for any fixed iteration $T$, when the adversarial perturbation during training has proper bounded L2 norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum L2 norm margin classifier at the rate of $O(1/\sqrt{T})$, significantly faster than the rate $O(1/\log T}$ of training with clean data.

Deep Reinforcement Learning with Robust and Smooth Policy

no code implementations21 Mar 2020 Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao

Deep reinforcement learning (RL) has achieved great empirical successes in various domains.

Pursuing Sources of Heterogeneity in Modeling Clustered Population

no code implementations10 Mar 2020 Yan Li, Chun Yu, Yize Zhao, Robert H. Aseltine, Weixin Yao, Kun Chen

We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously.

Feature Selection

Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head

no code implementations12 Feb 2020 Yang Li, Yan Li, Hua Tian

To the best of our knowledge, this study is the first research on the prospective use of a deep learning-based diagnosis system for AVNFH by conducting two pilot studies representing real-world application scenarios.

Decision Making Head Detection

Bilinear Graph Neural Network with Neighbor Interactions

1 code implementation10 Feb 2020 Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang

We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.

General Classification Node Classification

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

9 code implementations6 Feb 2020 Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

Collaborative Filtering Graph Classification +1

Towards Understanding the Importance of Noise in Training Neural Networks

no code implementations7 Sep 2019 Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao

Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks.

Inductive Bias of Gradient Descent based Adversarial Training on Separable Data

no code implementations7 Jun 2019 Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao

Specifically, we show that when the adversarial perturbation during training has bounded $\ell_2$-norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum $\ell_2$-norm margin classifier at the rate of $\tilde{\mathcal{O}}(1/\sqrt{T})$, significantly faster than the rate $\mathcal{O}(1/\log T)$ of training with clean data.

Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction

no code implementations13 May 2019 Yan Li, Ryan Wen Liu, Zhao Liu, Jingxian Liu

Reliable and accurate prediction of time series plays a crucial role in maritime industry, such as economic investment, transportation planning, port planning and design, etc.

Time Series Time Series Prediction

Strain engineering of epitaxial oxide heterostructures beyond substrate limitations

no code implementations3 May 2019 Xiong Deng, Chao Chen, Deyang Chen, Xiangbin Cai, Xiaozhe Yin, Chao Xu, Fei Sun, Caiwen Li, Yan Li, Han Xu, Mao Ye, Guo Tian, Zhen Fan, Zhipeng Hou, Minghui Qin, Yu Chen, Zhenlin Luo, Xubing Lu, Guofu Zhou, Lang Chen, Ning Wang, Ye Zhu, Xingsen Gao, Jun-Ming Liu

The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.

Materials Science

Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification

no code implementations10 Apr 2019 Jiajie Tian, Zhu Teng, Rui Li, Yan Li, Baopeng Zhang, Jianping Fan

Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e. g. completely different identities and backgrounds) and the intra-dataset difference (e. g. camera invariance).

Person Re-Identification Transfer Learning

Transductive Zero-Shot Learning with Visual Structure Constraint

1 code implementation NeurIPS 2019 Zi-Yu Wan, Dong-Dong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao

Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i. e. alleviate the above domain shift problem).

Zero-Shot Learning

Improving Gated Recurrent Unit Based Acoustic Modeling with Batch Normalization and Enlarged Context

no code implementations26 Nov 2018 Jie Li, Yahui Shan, Xiaorui Wang, Yan Li

The use of future contextual information is typically shown to be helpful for acoustic modeling.

Object detection and tracking benchmark in industry based on improved correlation filter

no code implementations11 Jun 2018 Shangzhen Luan, Yan Li, Xiaodi Wang, Baochang Zhang

Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4. 0 applications.

Real-Time Object Detection

Projection-Free Algorithms in Statistical Estimation

no code implementations20 May 2018 Yan Li, Chao Qu, Huan Xu

Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective.

Communication-Efficient Projection-Free Algorithm for Distributed Optimization

no code implementations20 May 2018 Yan Li, Chao Qu, Huan Xu

We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral.

Distributed Optimization Matrix Completion

Gated Recurrent Unit Based Acoustic Modeling with Future Context

no code implementations18 May 2018 Jie Li, Xiaorui Wang, Yuan-Yuan Zhao, Yan Li

The use of future contextual information is typically shown to be helpful for acoustic modeling.

Discriminative Learning of Latent Features for Zero-Shot Recognition

1 code implementation CVPR 2018 Yan Li, Junge Zhang, Jian-Guo Zhang, Kaiqi Huang

In this work, we retrospect existing methods and demonstrate the necessity to learn discriminative representations for both visual and semantic instances of ZSL.

Zero-Shot Learning

Mixed Supervised Object Detection with Robust Objectness Transfer

no code implementations27 Feb 2018 Yan Li, Junge Zhang, Kaiqi Huang, Jian-Guo Zhang

Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD.

Multiple Instance Learning Object Detection

A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

no code implementations25 Feb 2018 Kun Hu, Zhe Li, Ying Liu, Luyin Cheng, Qi Yang, Yan Li

In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn.

Causal Inference

Fast Global Convergence via Landscape of Empirical Loss

no code implementations13 Feb 2018 Chao Qu, Yan Li, Huan Xu

While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing interests recently, due to many desirable properties such as superior robustness and classification accuracy, compared with their convex counterparts.

General Classification

Machine Learning for Survival Analysis: A Survey

no code implementations15 Aug 2017 Ping Wang, Yan Li, Chandan K. Reddy

We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

Survival Analysis

Solving Multi-Objective MDP with Lexicographic Preference: An application to stochastic planning with multiple quantile objective

no code implementations10 May 2017 Yan Li, Zhaohan Sun

In most common settings of Markov Decision Process (MDP), an agent evaluate a policy based on expectation of (discounted) sum of rewards.

Autonomous Driving

SAGA and Restricted Strong Convexity

no code implementations19 Feb 2017 Chao Qu, Yan Li, Huan Xu

SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications.

Linear Convergence of SVRG in Statistical Estimation

no code implementations7 Nov 2016 Chao Qu, Yan Li, Huan Xu

SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems.

Skipping Word: A Character-Sequential Representation based Framework for Question Answering

no code implementations2 Sep 2016 Lingxun Meng, Yan Li, Mengyi Liu, Peng Shu

Recent works using artificial neural networks based on word distributed representation greatly boost the performance of various natural language learning tasks, especially question answering.

Answer Selection

M$^2$S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection

1 code implementation19 Apr 2016 Lingxun Meng, Yan Li

Recent works using artificial neural networks based on distributed word representation greatly boost performance on various natural language processing tasks, especially the answer selection problem.

Answer Selection Metric Learning

Audio Recording Device Identification Based on Deep Learning

no code implementations18 Feb 2016 Simeng Qi, Zheng Huang, Yan Li, Shaopei Shi

The identification result shows that the method of getting feature vector from the noise of each device and identifying them with deep learning techniques is viable, and well-preformed.

Speech Enhancement

Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction

no code implementations ICCV 2015 Yan Li, Ruiping Wang, Haomiao Liu, Huajie Jiang, Shiguang Shan, Xilin Chen

In this way, the learned binary codes can be applied to not only fine-grained face image retrieval, but also facial attributes prediction, which is the very innovation of this work, just like killing two birds with one stone.

Face Image Retrieval

A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model

no code implementations6 Aug 2015 Yan Li, Kristofer G. Reyes, Jorge Vazquez-Anderson, Yingfei Wang, Lydia M. Contreras, Warren B. Powell

We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules.

Face Video Retrieval With Image Query via Hashing Across Euclidean Space and Riemannian Manifold

no code implementations CVPR 2015 Yan Li, Ruiping Wang, Zhiwu Huang, Shiguang Shan, Xilin Chen

Retrieving videos of a specific person given his/her face image as query becomes more and more appealing for applications like smart movie fast-forwards and suspect searching.

Video Retrieval

The Knowledge Gradient Policy Using A Sparse Additive Belief Model

no code implementations18 Mar 2015 Yan Li, Han Liu, Warren Powell

We propose a sequential learning policy for noisy discrete global optimization and ranking and selection (R\&S) problems with high dimensional sparse belief functions, where there are hundreds or even thousands of features, but only a small portion of these features contain explanatory power.

Global Optimization

Comment on "Clustering by fast search and find of density peaks"

no code implementations18 Jan 2015 Shuliang Wang, Dakui Wang, Caoyuan Li, Yan Li

For any data set to be clustered, the most reasonable value of d_c can be objectively calculated from the data set by using our proposed method.

Sparse Additive Model using Symmetric Nonnegative Definite Smoothers

no code implementations8 Sep 2014 Yan Li

We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers.

Variable Selection

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