Search Results for author: Rong Jin

Found 159 papers, 50 papers with code

Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models

1 code implementation12 Jun 2024 Yi-Fan Zhang, Qingsong Wen, Chaoyou Fu, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin

Seeing clearly with high resolution is a foundation of Large Multimodal Models (LMMs), which has been proven to be vital for visual perception and reasoning.

Image Compression

Towards Unified Robustness Against Both Backdoor and Adversarial Attacks

1 code implementation28 May 2024 Zhenxing Niu, Yuyao Sun, Qiguang Miao, Rong Jin, Gang Hua

Specifically, our PUD has a progressive model purification scheme to jointly erase backdoors and enhance the model's adversarial robustness.

Adversarial Defense Adversarial Robustness +2

Debiasing Multimodal Large Language Models

1 code implementation8 Mar 2024 Yi-Fan Zhang, Weichen Yu, Qingsong Wen, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.

Fairness Question Answering

Attention as Robust Representation for Time Series Forecasting

no code implementations8 Feb 2024 Peisong Niu, Tian Zhou, Xue Wang, Liang Sun, Rong Jin

Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV.

Multivariate Time Series Forecasting Time Series

FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting

no code implementations8 Feb 2024 Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin

Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities.

Jailbreaking Attack against Multimodal Large Language Model

1 code implementation4 Feb 2024 Zhenxing Niu, Haodong Ren, Xinbo Gao, Gang Hua, Rong Jin

This paper focuses on jailbreaking attacks against multi-modal large language models (MLLMs), seeking to elicit MLLMs to generate objectionable responses to harmful user queries.

Language Modelling Large Language Model +1

Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization

no code implementations CVPR 2024 Zhi-Fan Wu, Chaojie Mao, Wue Wang, Jianwen Jiang, Yiliang Lv, Rong Jin

Despite encouraging results from recent developments in transfer learning for adapting pre-trained model to downstream tasks the performance of model probing is still lagging behind the state-of-the-art parameter efficient tuning methods.

Computational Efficiency feature selection +1

Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors

1 code implementation20 Dec 2023 Yi-Fan Zhang, Zhang Zhang, Liang Wang, Tieniu Tan, Rong Jin

In an effort to address these issues, we delve into the realm of zero-shot machine-generated text detection.

Binary Classification Text Detection +1

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting

1 code implementation6 Dec 2023 Chao Chen, Tian Zhou, Yanjun Zhao, Hui Liu, Liang Sun, Rong Jin

Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.

Computational Efficiency Quantization +6

Model-free Test Time Adaptation for Out-Of-Distribution Detection

no code implementations28 Nov 2023 Yifan Zhang, Xue Wang, Tian Zhou, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

We demonstrate the effectiveness of \abbr through comprehensive experiments on multiple OOD detection benchmarks, extensive empirical studies show that \abbr significantly improves the performance of OOD detection over state-of-the-art methods.

Decision Making Out-of-Distribution Detection +2

One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors

1 code implementation24 Nov 2023 Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin

Despite the impressive achievements of pre-trained models in the fields of natural language processing (NLP) and computer vision (CV), progress in the domain of time series analysis has been limited.

Anomaly Detection Few-Shot Learning +2

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

1 code implementation NeurIPS 2023 Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.

Time Series Time Series Forecasting

CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting

1 code implementation20 May 2023 Wang Xue, Tian Zhou, Qingsong Wen, Jinyang Gao, Bolin Ding, Rong Jin

In this work, we design a special Transformer, i. e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting.

Time Series Time Series Forecasting

AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation

1 code implementation25 Apr 2023 Yi-Fan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than advanced TTA methods.

Domain Generalization Test-time Adaptation

Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks

4 code implementations CVPR 2023 Weihua Chen, Xianzhe Xu, Jian Jia, Hao Luo, Yaohua Wang, Fan Wang, Rong Jin, Xiuyu Sun

Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation.

Human Parsing Pedestrian Attribute Recognition +6

Making Vision Transformers Efficient from A Token Sparsification View

1 code implementation CVPR 2023 Shuning Chang, Pichao Wang, Ming Lin, Fan Wang, David Junhao Zhang, Rong Jin, Mike Zheng Shou

In this work, we propose a novel Semantic Token ViT (STViT), for efficient global and local vision transformers, which can also be revised to serve as backbone for downstream tasks.

Efficient ViTs Instance Segmentation +4

One Fits All:Power General Time Series Analysis by Pretrained LM

3 code implementations23 Feb 2023 Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin

The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training.

Anomaly Detection Few-Shot Learning +2

GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs

no code implementations1 Nov 2022 Marios Papachristou, Rishab Goel, Frank Portman, Matthew Miller, Rong Jin

On the other hand, shallow (or node-level) models using ego features and adjacency embeddings work well in heterophilous graphs.

Graph Learning Knowledge Graph Embeddings

FeDXL: Provable Federated Learning for Deep X-Risk Optimization

1 code implementation26 Oct 2022 Zhishuai Guo, Rong Jin, Jiebo Luo, Tianbao Yang

To this end, we propose an active-passive decomposition framework that decouples the gradient's components with two types, namely active parts and passive parts, where the active parts depend on local data that are computed with the local model and the passive parts depend on other machines that are communicated/computed based on historical models and samples.

Federated Learning

Grow and Merge: A Unified Framework for Continuous Categories Discovery

no code implementations9 Oct 2022 Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, Yue Gao

Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.

Self-Supervised Learning

Robust Graph Structure Learning via Multiple Statistical Tests

1 code implementation8 Oct 2022 Yaohua Wang, Fangyi Zhang, Ming Lin, Senzhang Wang, Xiuyu Sun, Rong Jin

A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges.

Face Clustering Graph structure learning

Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks

no code implementations19 Sep 2022 Yunwen Lei, Rong Jin, Yiming Ying

While significant theoretical progress has been achieved, unveiling the generalization mystery of overparameterized neural networks still remains largely elusive.

TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting

no code implementations24 Jun 2022 Tian Zhou, Jianqing Zhu, Xue Wang, Ziqing Ma, Qingsong Wen, Liang Sun, Rong Jin

Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).

Computational Efficiency feature selection +2

An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation

no code implementations25 May 2022 Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin, Xiangyang Ji, Antoni B. Chan

With our empirical result obtained from 1, 330 models, we provide the following main observations: 1) ERM combined with data augmentation can achieve state-of-the-art performance if we choose a proper pre-trained model respecting the data property; 2) specialized algorithms further improve the robustness on top of ERM when handling a specific type of distribution shift, e. g., GroupDRO for spurious correlation and CORAL for large-scale out-of-distribution data; 3) Comparing different pre-training modes, architectures and data sizes, we provide novel observations about pre-training on distribution shift, which sheds light on designing or selecting pre-training strategy for different kinds of distribution shifts.

Data Augmentation

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

3 code implementations18 May 2022 Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.

Dimensionality Reduction Time Series +1

Hybrid Relation Guided Set Matching for Few-shot Action Recognition

1 code implementation CVPR 2022 Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, Nong Sang

To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.

Few Shot Action Recognition Relation +1

Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation

2 code implementations ACL 2022 Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Weihua Luo, Jun Xie, Rong Jin

Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.

Data Augmentation Machine Translation +3

CHEX: CHannel EXploration for CNN Model Compression

1 code implementation CVPR 2022 Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung

However, conventional pruning methods have limitations in that: they are restricted to pruning process only, and they require a fully pre-trained large model.

Image Classification Instance Segmentation +4

Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks

no code implementations CVPR 2023 Bingxu Mu, Zhenxing Niu, Le Wang, Xue Wang, Rong Jin, Gang Hua

Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks.

backdoor defense

Entroformer: A Transformer-based Entropy Model for Learned Image Compression

2 code implementations ICLR 2022 Yichen Qian, Ming Lin, Xiuyu Sun, Zhiyu Tan, Rong Jin

One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules.

Image Classification Image Compression +1

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

3 code implementations30 Jan 2022 Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin

Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).

Time Series Time Series Analysis

ELSA: Enhanced Local Self-Attention for Vision Transformer

1 code implementation23 Dec 2021 Jingkai Zhou, Pichao Wang, Fan Wang, Qiong Liu, Hao Li, Rong Jin

Self-attention is powerful in modeling long-range dependencies, but it is weak in local finer-level feature learning.

Image Classification Instance Segmentation +2

A Novel Convergence Analysis for Algorithms of the Adam Family

no code implementations7 Dec 2021 Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

Although rigorous convergence analysis exists for Adam, they impose specific requirements on the update of the adaptive step size, which are not generic enough to cover many other variants of Adam.

Bilevel Optimization

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

1 code implementation2 Dec 2021 Zhaoyuan Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, Rong Jin

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations.

Ranked #2 on Unsupervised Semantic Segmentation on COCO-Stuff-171 (using extra training data)

Segmentation Self-Supervised Learning +1

MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection

1 code implementation26 Nov 2021 Zhenhong Sun, Ming Lin, Xiuyu Sun, Zhiyu Tan, Hao Li, Rong Jin

Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS).

Neural Architecture Search Object +2

Improved Fine-Tuning by Better Leveraging Pre-Training Data

no code implementations24 Nov 2021 Ziquan Liu, Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Xiangyang Ji, Antoni Chan, Rong Jin

The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning.

Image Classification Learning Theory

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

2 code implementations23 Nov 2021 Hao Luo, Pichao Wang, Yi Xu, Feng Ding, Yanxin Zhou, Fan Wang, Hao Li, Rong Jin

We first investigate self-supervised learning (SSL) methods with Vision Transformer (ViT) pretrained on unlabelled person images (the LUPerson dataset), and empirically find it significantly surpasses ImageNet supervised pre-training models on ReID tasks.

 Ranked #1 on Unsupervised Person Re-Identification on Market-1501 (using extra training data)

Self-Supervised Learning Unsupervised Domain Adaptation +1

BINAS: Bilinear Interpretable Neural Architecture Search

1 code implementation24 Oct 2021 Niv Nayman, Yonathan Aflalo, Asaf Noy, Rong Jin, Lihi Zelnik-Manor

Practical use of neural networks often involves requirements on latency, energy and memory among others.

Neural Architecture Search

Rethinking Supervised Pre-training for Better Downstream Transferring

no code implementations ICLR 2022 Yutong Feng, Jianwen Jiang, Mingqian Tang, Rong Jin, Yue Gao

Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks.

Open-Ended Question Answering

Hierarchical Cross Contrastive Learning of Visual Representations

no code implementations29 Sep 2021 Hesen Chen, Ming Lin, Xiuyu Sun, Rong Jin

In this work, we propose a novel approach termed Hierarchical Cross Contrastive Learning(HCCL) to further distill the information mismatched by the conventional contrastive loss.

Contrastive Learning Few-Shot Learning +1

ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search

no code implementations29 Sep 2021 Zhenhong Sun, Ming Lin, Zhiyu Tan, Xiuyu Sun, Rong Jin

Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS).

Neural Architecture Search Object +2

Unsupervised Domain Adaptation By Optimal Transportation Of Clusters Between Domains

no code implementations29 Sep 2021 Yang Liu, Zhipeng Zhou, Lei Shang, Baigui Sun, Hao Li, Rong Jin

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain.

Attribute Clustering +2

CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation

2 code implementations ICLR 2022 Tongkun Xu, Weihua Chen, Pichao Wang, Fan Wang, Hao Li, Rong Jin

Along with the pseudo labels, a weight-sharing triple-branch transformer framework is proposed to apply self-attention and cross-attention for source/target feature learning and source-target domain alignment, respectively.

Unsupervised Domain Adaptation

Scaled ReLU Matters for Training Vision Transformers

no code implementations8 Sep 2021 Pichao Wang, Xue Wang, Hao Luo, Jingkai Zhou, Zhipeng Zhou, Fan Wang, Hao Li, Rong Jin

In this paper, we further investigate this problem and extend the above conclusion: only early convolutions do not help for stable training, but the scaled ReLU operation in the \textit{convolutional stem} (\textit{conv-stem}) matters.


Dash: Semi-Supervised Learning with Dynamic Thresholding

no code implementations1 Sep 2021 Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, Rong Jin

In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models.

Semi-Supervised Image Classification

ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning

1 code implementation24 Aug 2021 Zhiwu Qing, Ziyuan Huang, Shiwei Zhang, Mingqian Tang, Changxin Gao, Marcelo H. Ang Jr, Rong Jin, Nong Sang

The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation.

Contrastive Learning

Graph Convolution for Re-ranking in Person Re-identification

1 code implementation5 Jul 2021 Yuqi Zhang, Qian Qi, Chong Liu, Weihua Chen, Fan Wang, Hao Li, Rong Jin

In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric.

Person Re-Identification Re-Ranking +1

Communication Efficient SGD via Gradient Sampling With Bayes Prior

no code implementations CVPR 2021 Liuyihan Song, Kang Zhao, Pan Pan, Yu Liu, Yingya Zhang, Yinghui Xu, Rong Jin

Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively.

Image Classification object-detection +2

Effective Model Sparsification by Scheduled Grow-and-Prune Methods

1 code implementation ICLR 2022 Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie

It addresses the shortcomings of the previous works by repeatedly growing a subset of layers to dense and then pruning them back to sparse after some training.

Image Classification

KVT: k-NN Attention for Boosting Vision Transformers

1 code implementation28 May 2021 Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Hao Li, Rong Jin

A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies.

Why Does Multi-Epoch Training Help?

no code implementations13 May 2021 Yi Xu, Qi Qian, Hao Li, Rong Jin

Stochastic gradient descent (SGD) has become the most attractive optimization method in training large-scale deep neural networks due to its simplicity, low computational cost in each updating step, and good performance.

A Novel Convergence Analysis for Algorithms of the Adam Family and Beyond

no code implementations30 Apr 2021 Zhishuai Guo, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang

Our analysis exhibits that an increasing or large enough "momentum" parameter for the first-order moment used in practice is sufficient to ensure Adam and its many variants converge under a mild boundness condition on the adaptive scaling factor of the step size.

Bilevel Optimization

A Theoretical Analysis of Learning with Noisily Labeled Data

no code implementations8 Apr 2021 Yi Xu, Qi Qian, Hao Li, Rong Jin

Noisy labels are very common in deep supervised learning.

Self-supervised Video Representation Learning by Context and Motion Decoupling

1 code implementation CVPR 2021 Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, Rong Jin

A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias.

Action Recognition Decoder +4

Self-supervised Motion Learning from Static Images

1 code implementation CVPR 2021 Ziyuan Huang, Shiwei Zhang, Jianwen Jiang, Mingqian Tang, Rong Jin, Marcelo Ang

We furthermore introduce a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification. We demonstrate that MoSI can discover regions with large motion even without fine-tuning on the downstream datasets.

Action Recognition Self-Supervised Learning

Large-Scale Visual Search with Binary Distributed Graph at Alibaba

no code implementations9 Feb 2021 Kang Zhao, Pan Pan, Yun Zheng, Yanhao Zhang, Changxu Wang, Yingya Zhang, Yinghui Xu, Rong Jin

For a deployed visual search system with several billions of online images in total, building a billion-scale offline graph in hours is essential, which is almost unachievable by most existing methods.

graph construction

Visual Search at Alibaba

no code implementations9 Feb 2021 Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Yingya Zhang, Xiaofeng Ren, Rong Jin

We hope visual search at Alibaba becomes more widely incorporated into today's commercial applications.

Image Retrieval

Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance

no code implementations9 Feb 2021 Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Jianmin Wu, Yinghui Xu, Rong Jin

Benefiting from exploration of user click data, our networks are more effective to encode richer supervision and better distinguish real-shot images in terms of category and feature.

Large Scale Long-tailed Product Recognition System at Alibaba

no code implementations9 Feb 2021 Xiangzeng Zhou, Pan Pan, Yun Zheng, Yinghui Xu, Rong Jin

In this paper, we present a novel side information based large scale visual recognition co-training~(SICoT) system to deal with the long tail problem by leveraging the image related side information.

Object Recognition

Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning

no code implementations9 Feb 2021 Yu Liu, Lianghua Huang, Pan Pan, Bin Wang, Yinghui Xu, Rong Jin

However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy.

Classification General Classification +2

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition

2 code implementations1 Feb 2021 Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.

Image Classification Neural Architecture Search

A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks

no code implementations12 Jan 2021 Asaf Noy, Yi Xu, Yonathan Aflalo, Lihi Zelnik-Manor, Rong Jin

We show that convergence to a global minimum is guaranteed for networks with widths quadratic in the sample size and linear in their depth at a time logarithmic in both.

Schemes of Propagation Models and Source Estimators for Rumor Source Detection in Online Social Networks: A Short Survey of a Decade of Research

no code implementations4 Jan 2021 Rong Jin, Weili Wu

Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network.

Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition

2 code implementations ICCV 2021 Ming Lin, Pichao Wang, Zhenhong Sun, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures.

Neural Architecture Search Vocal Bursts Intensity Prediction

Attentional-Biased Stochastic Gradient Descent

1 code implementation13 Dec 2020 Qi Qi, Yi Xu, Rong Jin, Wotao Yin, Tianbao Yang

In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning.

Classification General Classification +2

Price Suggestion for Online Second-hand Items with Texts and Images

no code implementations10 Dec 2020 Liang Han, Zhaozheng Yin, Zhurong Xia, Mingqian Tang, Rong Jin

The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms.

Binary Classification regression

Vision-based Price Suggestion for Online Second-hand Items

no code implementations10 Dec 2020 Liang Han, Zhaozheng Yin, Zhurong Xia, Li Guo, Mingqian Tang, Rong Jin

Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model.

Binary Classification Decision Making +1

Learning Accurate Entropy Model with Global Reference for Image Compression

2 code implementations ICLR 2021 Yichen Qian, Zhiyu Tan, Xiuyu Sun, Ming Lin, Dongyang Li, Zhenhong Sun, Hao Li, Rong Jin

In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate.

Image Compression

WeMix: How to Better Utilize Data Augmentation

no code implementations3 Oct 2020 Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin

To this end, we develop two novel algorithms, termed "AugDrop" and "MixLoss", to correct the data bias in the data augmentation.

Data Augmentation

Neural Architecture Design for GPU-Efficient Networks

2 code implementations24 Jun 2020 Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, Rong Jin

To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies.

Neural Architecture Search

Towards Understanding Label Smoothing

no code implementations20 Jun 2020 Yi Xu, Yuanhong Xu, Qi Qian, Hao Li, Rong Jin

Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants.

An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives

1 code implementation NeurIPS 2021 Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, Tianbao Yang

In this paper, we propose a practical online method for solving a class of distributionally robust optimization (DRO) with non-convex objectives, which has important applications in machine learning for improving the robustness of neural networks.

Weakly Supervised Representation Learning with Coarse Labels

1 code implementation ICCV 2021 Yuanhong Xu, Qi Qian, Hao Li, Rong Jin, Juhua Hu

To mitigate this challenge, we propose an algorithm to learn the fine-grained patterns for the target task, when only its coarse-class labels are available.

Learning with coarse labels Representation Learning

DR Loss: Improving Object Detection by Distributional Ranking

1 code implementation CVPR 2020 Qi Qian, Lei Chen, Hao Li, Rong Jin

This architecture is efficient but can suffer from the imbalance issue with respect to two aspects: the inter-class imbalance between the number of candidates from foreground and background classes and the intra-class imbalance in the hardness of background candidates, where only a few candidates are hard to be identified.

Object object-detection +1

XNAS: Neural Architecture Search with Expert Advice

2 code implementations NeurIPS 2019 Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor

This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.

Image Classification Neural Architecture Search

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

no code implementations3 Jun 2019 Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin

We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.


On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization

no code implementations10 May 2019 Hao Yu, Rong Jin

We show that for stochastic non-convex optimization under the P-L condition, the classical data-parallel SGD with exponentially increasing batch sizes can achieve the fastest known $O(1/(NT))$ convergence with linear speedup using only $\log(T)$ communication rounds.

Stochastic Optimization

On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

no code implementations9 May 2019 Hao Yu, Rong Jin, Sen yang

Recent developments on large-scale distributed machine learning applications, e. g., deep neural networks, benefit enormously from the advances in distributed non-convex optimization techniques, e. g., distributed Stochastic Gradient Descent (SGD).

BIG-bench Machine Learning

Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses

no code implementations19 Mar 2019 Rong Jin, David Simchi-Levi, Li Wang, Xinshang Wang, Sen Yang

In this paper, we study algorithms for dynamically identifying a large number of products (i. e., SKUs) with top customer purchase probabilities on the fly, from an ocean of potential products to offer on retailers' ultra-fast delivery platforms.

Stagewise Training Accelerates Convergence of Testing Error Over SGD

no code implementations NeurIPS 2019 Zhuoning Yuan, Yan Yan, Rong Jin, Tianbao Yang

For convex loss functions and two classes of "nice-behaviored" non-convex objectives that are close to a convex function, we establish faster convergence of stagewise training than the vanilla SGD under the PL condition on both training error and testing error.

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence

no code implementations28 Nov 2018 Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang

In this paper, we propose new stochastic optimization algorithms and study their first-order convergence theories for solving a broad family of DC functions.

Stochastic Optimization

Large-scale Distance Metric Learning with Uncertainty

no code implementations CVPR 2018 Qi Qian, Jiasheng Tang, Hao Li, Shenghuo Zhu, Rong Jin

Furthermore, we can show that the metric is learned from latent examples only, but it can preserve the large margin property even for the original data.

Metric Learning

Learning with Non-Convex Truncated Losses by SGD

no code implementations21 May 2018 Yi Xu, Shenghuo Zhu, Sen yang, Chi Zhang, Rong Jin, Tianbao Yang

Learning with a {\it convex loss} function has been a dominating paradigm for many years.

Robust Optimization over Multiple Domains

no code implementations19 May 2018 Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, Hao Li

Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency.

BIG-bench Machine Learning Cloud Computing +1

Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions

no code implementations NeurIPS 2018 Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang, Rong Jin, Tianbao Yang

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set.

Multinomial Logit Bandit with Linear Utility Functions

no code implementations8 May 2018 Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin

In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items.

NEON+: Accelerated Gradient Methods for Extracting Negative Curvature for Non-Convex Optimization

no code implementations4 Dec 2017 Yi Xu, Rong Jin, Tianbao Yang

Accelerated gradient (AG) methods are breakthroughs in convex optimization, improving the convergence rate of the gradient descent method for optimization with smooth functions.

Open-Ended Question Answering

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

no code implementations NeurIPS 2018 Yi Xu, Rong Jin, Tianbao Yang

Two classes of methods have been proposed for escaping from saddle points with one using the second-order information carried by the Hessian and the other adding the noise into the first-order information.

Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM

no code implementations24 Jul 2017 Cong Leng, Hao Li, Shenghuo Zhu, Rong Jin

Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computational resources are limited.

object-detection Object Detection +1

Missing Modalities Imputation via Cascaded Residual Autoencoder

no code implementations CVPR 2017 Luan Tran, Xiaoming Liu, Jiayu Zhou, Rong Jin

To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities.

Imputation Object Recognition

Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds

no code implementations7 Feb 2017 Lijun Zhang, Tianbao Yang, Rong Jin

First, we establish an $\widetilde{O}(d/n + \sqrt{F_*/n})$ risk bound when the random function is nonnegative, convex and smooth, and the expected function is Lipschitz continuous, where $d$ is the dimensionality of the problem, $n$ is the number of samples, and $F_*$ is the minimal risk.

Image Classification

Dynamic Regret of Strongly Adaptive Methods

no code implementations ICML 2018 Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently.

Improved Dynamic Regret for Non-degenerate Functions

no code implementations NeurIPS 2017 Lijun Zhang, Tianbao Yang, Jin-Feng Yi, Rong Jin, Zhi-Hua Zhou

When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the squared path-length.

Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient

no code implementations16 May 2016 Tianbao Yang, Lijun Zhang, Rong Jin, Jin-Feng Yi

Secondly, we present a lower bound with noisy gradient feedback and then show that we can achieve optimal dynamic regrets under a stochastic gradient feedback and two-point bandit feedback.

Similarity Learning via Adaptive Regression and Its Application to Image Retrieval

no code implementations6 Dec 2015 Qi Qian, Inci M. Baytas, Rong Jin, Anil Jain, Shenghuo Zhu

The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning.

Image Retrieval Metric Learning +2

Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach

no code implementations12 Nov 2015 Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

In this paper, we develop a randomized algorithm and theory for learning a sparse model from large-scale and high-dimensional data, which is usually formulated as an empirical risk minimization problem with a sparsity-inducing regularizer.

Sparse Learning

Stochastic Proximal Gradient Descent for Nuclear Norm Regularization

no code implementations5 Nov 2015 Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

In this paper, we utilize stochastic optimization to reduce the space complexity of convex composite optimization with a nuclear norm regularizer, where the variable is a matrix of size $m \times n$.

Stochastic Optimization

Online Stochastic Linear Optimization under One-bit Feedback

no code implementations25 Sep 2015 Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is revealed to the learner at each round.

Towards Making High Dimensional Distance Metric Learning Practical

no code implementations15 Sep 2015 Qi Qian, Rong Jin, Lijun Zhang, Shenghuo Zhu

In this work, we present a dual random projection frame for DML with high dimensional data that explicitly addresses the limitation of dimensionality reduction for DML.

Dimensionality Reduction Metric Learning +1

Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees

no code implementations18 Jul 2015 Tianbao Yang, Lijun Zhang, Qihang Lin, Rong Jin

In this paper, we study a fast approximation method for {\it large-scale high-dimensional} sparse least-squares regression problem by exploiting the Johnson-Lindenstrauss (JL) transforms, which embed a set of high-dimensional vectors into a low-dimensional space.


Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion

no code implementations26 Apr 2015 Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou

To the best of our knowledge, this is the first relative bound that has been proved for the regularized formulation of matrix completion.

Low-Rank Matrix Completion

Theory of Dual-sparse Regularized Randomized Reduction

no code implementations15 Apr 2015 Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu

In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e. g., random projection, random hashing), for large-scale high-dimensional classification.

General Classification

Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities

no code implementations NeurIPS 2014 Tianbao Yang, Rong Jin

In this work, we study the problem of transductive pairwise classification from pairwise similarities~\footnote{The pairwise similarities are usually derived from some side information instead of the underlying class labels.}.

General Classification

CUR Algorithm for Partially Observed Matrices

no code implementations4 Nov 2014 Miao Xu, Rong Jin, Zhi-Hua Zhou

In particular, the proposed algorithm computes the low rank approximation of the target matrix based on (i) the randomly sampled rows and columns, and (ii) a subset of observed entries that are randomly sampled from the matrix.

Matrix Completion

Top Rank Optimization in Linear Time

no code implementations NeurIPS 2014 Nan Li, Rong Jin, Zhi-Hua Zhou

Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list.

On Data Preconditioning for Regularized Loss Minimization

no code implementations13 Aug 2014 Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin

In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization.

CUR Algorithm with Incomplete Matrix Observation

no code implementations22 Mar 2014 Rong Jin, Shenghuo Zhu

Our goal is to develop a low rank approximation algorithm, similar to CUR, based on (i) randomly sampled rows and columns from A, and (ii) randomly sampled entries from A.

Matrix Completion

Scalable Kernel Clustering: Approximate Kernel k-means

no code implementations16 Feb 2014 Radha Chitta, Rong Jin, Timothy C. Havens, Anil K. Jain

Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data.


Binary Excess Risk for Smooth Convex Surrogates

no code implementations7 Feb 2014 Mehrdad Mahdavi, Lijun Zhang, Rong Jin

In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in.

Learning Theory

Fine-Grained Visual Categorization via Multi-stage Metric Learning

no code implementations CVPR 2015 Qi Qian, Rong Jin, Shenghuo Zhu, Yuanqing Lin

To this end, we proposed a multi-stage metric learning framework that divides the large-scale high dimensional learning problem to a series of simple subproblems, achieving $\mathcal{O}(d)$ computational complexity.

Fine-Grained Visual Categorization Metric Learning

Excess Risk Bounds for Exponentially Concave Losses

no code implementations18 Jan 2014 Mehrdad Mahdavi, Rong Jin

The overarching goal of this paper is to derive excess risk bounds for learning from exp-concave loss functions in passive and sequential learning settings.


Analysis of Distributed Stochastic Dual Coordinate Ascent

no code implementations4 Dec 2013 Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin

Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates.

Mixed Optimization for Smooth Functions

no code implementations NeurIPS 2013 Mehrdad Mahdavi, Lijun Zhang, Rong Jin

It is well known that the optimal convergence rate for stochastic optimization of smooth functions is $[O(1/\sqrt{T})]$, which is same as stochastic optimization of Lipschitz continuous convex functions.

Stochastic Optimization

Linear Convergence with Condition Number Independent Access of Full Gradients

no code implementations NeurIPS 2013 Lijun Zhang, Mehrdad Mahdavi, Rong Jin

For smooth and strongly convex optimization, the optimal iteration complexity of the gradient-based algorithm is $O(\sqrt{\kappa}\log 1/\epsilon)$, where $\kappa$ is the conditional number.

Stochastic Convex Optimization with Multiple Objectives

no code implementations NeurIPS 2013 Mehrdad Mahdavi, Tianbao Yang, Rong Jin

It leverages on the theory of Lagrangian method in constrained optimization and attains the optimal convergence rate of $[O(1/ \sqrt{T})]$ in high probability for general Lipschitz continuous objectives.

Stochastic Optimization

Speedup Matrix Completion with Side Information: Application to Multi-Label Learning

no code implementations NeurIPS 2013 Miao Xu, Rong Jin, Zhi-Hua Zhou

In standard matrix completion theory, it is required to have at least $O(n\ln^2 n)$ observed entries to perfectly recover a low-rank matrix $M$ of size $n\times n$, leading to a large number of observations when $n$ is large.

Matrix Completion Multi-Label Learning

Stochastic Optimization of Smooth Loss

no code implementations30 Nov 2013 Rong Jin

In this paper, we first prove a high probability bound rather than an expectation bound for stochastic optimization with smooth loss.

Stochastic Optimization

Beating the Minimax Rate of Active Learning with Prior Knowledge

no code implementations19 Nov 2013 Lijun Zhang, Mehrdad Mahdavi, Rong Jin

Under the assumption that the norm of the optimal classifier that minimizes the convex risk is available, our analysis shows that the introduction of the convex surrogate loss yields an exponential reduction in the label complexity even when the parameter $\kappa$ of the Tsybakov noise is larger than $1$.

Active Learning

MixedGrad: An O(1/T) Convergence Rate Algorithm for Stochastic Smooth Optimization

no code implementations26 Jul 2013 Mehrdad Mahdavi, Rong Jin

It is well known that the optimal convergence rate for stochastic optimization of smooth functions is $O(1/\sqrt{T})$, which is same as stochastic optimization of Lipschitz continuous convex functions.

Stochastic Optimization

Compressed Hashing

no code implementations CVPR 2013 Yue Lin, Rong Jin, Deng Cai, Shuicheng Yan, Xuelong. Li

Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search.

One-Pass AUC Optimization

no code implementations7 May 2013 Wei Gao, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou

AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set.

Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)

no code implementations3 Apr 2013 Qi Qian, Rong Jin, Jin-Feng Yi, Lijun Zhang, Shenghuo Zhu

Although stochastic gradient descent (SGD) has been successfully applied to improve the efficiency of DML, it can still be computationally expensive because in order to ensure that the solution is a PSD matrix, it has to, at every iteration, project the updated distance metric onto the PSD cone, an expensive operation.

Computational Efficiency Metric Learning

O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions

no code implementations2 Apr 2013 Lijun Zhang, Tianbao Yang, Rong Jin, Xiaofei He

Traditional algorithms for stochastic optimization require projecting the solution at each iteration into a given domain to ensure its feasibility.

Stochastic Optimization

Passive Learning with Target Risk

no code implementations8 Feb 2013 Mehrdad Mahdavi, Rong Jin

In this paper we consider learning in passive setting but with a slight modification.

Generalization Bounds Learning Theory +1

Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison

no code implementations NeurIPS 2012 Tianbao Yang, Yu-Feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou

Both random Fourier features and the Nyström method have been successfully applied to efficient kernel learning.

Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning

no code implementations NeurIPS 2012 Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil K. Jain

One difficulty in learning the pairwise similarity measure is that there is a significant amount of noise and inter-worker variations in the manual annotations obtained via crowdsourcing.

Clustering Computational Efficiency +2

Stochastic Gradient Descent with Only One Projection

no code implementations NeurIPS 2012 Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jin-Feng Yi

Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of them require projecting the solution at {\it each} iteration to ensure that the obtained solution stays within the feasible domain.

Online Stochastic Optimization with Multiple Objectives

no code implementations26 Nov 2012 Mehrdad Mahdavi, Tianbao Yang, Rong Jin

We first propose a projection based algorithm which attains an $O(T^{-1/3})$ convergence rate.

Stochastic Optimization

Robust Metric Learning by Smooth Optimization

no code implementations15 Mar 2012 Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints.

Combinatorial Optimization Metric Learning

An Efficient Primal-Dual Prox Method for Non-Smooth Optimization

no code implementations24 Jan 2012 Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu

We study the non-smooth optimization problems in machine learning, where both the loss function and the regularizer are non-smooth functions.

BIG-bench Machine Learning

Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition

no code implementations NeurIPS 2010 Serhat Bucak, Rong Jin, Anil K. Jain

Recent studies have shown that multiple kernel learning is very effective for object recognition, leading to the popularity of kernel learning in computer vision problems.

Object Object Recognition

Active Learning by Querying Informative and Representative Examples

no code implementations NeurIPS 2010 Sheng-Jun Huang, Rong Jin, Zhi-Hua Zhou

Most active learning approaches select either informative or representative unlabeled instances to query their labels.

Active Learning Informativeness

Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering

no code implementations NeurIPS 2009 Lei Wu, Rong Jin, Steven C. Hoi, Jianke Zhu, Nenghai Yu

Learning distance functions with side information plays a key role in many machine learning and data mining applications.


DUOL: A Double Updating Approach for Online Learning

no code implementations NeurIPS 2009 Peilin Zhao, Steven C. Hoi, Rong Jin

This is clearly insufficient since when a new misclassified example is added to the pool of support vectors, we generally expect it to affect the weights for the existing support vectors.

Learning to Rank by Optimizing NDCG Measure

no code implementations NeurIPS 2009 Hamed Valizadegan, Rong Jin, Ruofei Zhang, Jianchang Mao

Learning to rank is a relatively new field of study, aiming to learn a ranking function from a set of training data with relevancy labels.

Information Retrieval Learning-To-Rank +1

Adaptive Regularization for Transductive Support Vector Machine

no code implementations NeurIPS 2009 Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, Zhirong Yang

In this framework, SVM and TSVM can be regarded as a learning machine without regularization and one with full regularization from the unlabeled data, respectively.

An Extended Level Method for Efficient Multiple Kernel Learning

no code implementations NeurIPS 2008 Zenglin Xu, Rong Jin, Irwin King, Michael Lyu

We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem.

Multi-label Multiple Kernel Learning

no code implementations NeurIPS 2008 Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye

We present a multi-label multiple kernel learning (MKL) formulation, in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph.

Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization

no code implementations NeurIPS 2008 Liu Yang, Rong Jin, Rahul Sukthankar

For empirical evaluation, we present a direct comparison with a number of state-of-the-art methods for inductive semi-supervised learning and text categorization; and we show that SSLW results in a significant improvement in categorization accuracy, equipped with a small training set and an unlabeled resource that is weakly related to the test beds."

General Classification Text Categorization

Efficient Convex Relaxation for Transductive Support Vector Machine

no code implementations NeurIPS 2007 Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu

We consider the problem of Support Vector Machine transduction, which involves a combinatorial problem with exponential computational complexity in the number of unlabeled examples.

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