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no code implementations • ICML 2020 • Hang Zhang, Ping Li

Unlabeled linear regression, or ``linear regression with an unknown permutation'', has attracted increasing attentions due to its applications in linkage record and de-anonymization.

no code implementations • EMNLP 2020 • Mingming Sun, Wenyue Hua, Zoey Liu, Xin Wang, Kangjie Zheng, Ping Li

Based on the same platform of OIX, the OIE strategies are reusable, and people can select a set of strategies to assemble their algorithm for a specific task so that the adaptability may be significantly increased.

no code implementations • ECCV 2020 • Tan Yu, Yunfeng Cai, Ping Li

To boost the efficiency in the GPU platform, recent methods rely on Newton-Schulz (NS) iteration to approximate the matrix square-root.

no code implementations • 17 Sep 2021 • Yingzhen Yang, Ping Li

Similarity-based clustering methods separate data into clusters according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance.

no code implementations • 10 Sep 2021 • Xiangyi Chen, Xiaoyun Li, Ping Li

While adaptive gradient methods have been proven effective for training neural nets, the study of adaptive gradient methods in federated learning is scarce.

no code implementations • 10 Sep 2021 • Xiaoyun Li, Ping Li

That is, one single permutation is used for both the initial pre-processing step to break the structures in the data and the circulant hashing step to generate $K$ hashes.

no code implementations • 9 Sep 2021 • Sujay Bhatt, Ping Li, Gennady Samorodnitsky

We consider a multi-armed bandit problem motivated by situations where only the extreme values, as opposed to expected values in the classical bandit setting, are of interest.

no code implementations • 7 Sep 2021 • Xiaoyun Li, Ping Li

Unlike classical MinHash, these $K$ hashes are obviously correlated, but we are able to provide rigorous proofs that we still obtain an unbiased estimate of the Jaccard similarity and the theoretical variance is uniformly smaller than that of the classical MinHash with $K$ independent permutations.

no code implementations • 7 Sep 2021 • Xiangyi Chen, Belhal Karimi, Weijie Zhao, Ping Li

Adaptive gradient methods including Adam, AdaGrad, and their variants have been very successful for training deep learning models, such as neural networks.

no code implementations • 16 Aug 2021 • Weiwei Li, Mostafa Rahmani, Ping Li

It is shown that in contrast to most of the existing methods which require the subspaces to be sufficiently incoherent with each other, Innovation Pursuit only requires the innovative components of the subspaces to be sufficiently incoherent with each other.

no code implementations • 16 Aug 2021 • Mostafa Rahmani, Rasoul Shafipour, Ping Li

The proposed approach is used to design several novel global feature aggregation methods based on the choice of the LFDS.

no code implementations • 2 Aug 2021 • Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

More recently, using smaller patches with a pyramid structure, Vision Permutator (ViP) and Global Filter Network (GFNet) achieve better performance than S$^2$-MLP.

no code implementations • 28 Jun 2021 • Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone.

1 code implementation • 24 Jun 2021 • Johann Li, Guangming Zhu, Cong Hua, Mingtao Feng, BasheerBennamoun, Ping Li, Xiaoyuan Lu, Juan Song, Peiyi Shen, Xu Xu, Lin Mei, Liang Zhang, Syed Afaq Ali Shah, Mohammed Bennamoun

Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research.

no code implementations • 23 Jun 2021 • Mostafa Rahmani, Ping Li

In the application of Innovation Search for outlier detection, the directions of innovation were utilized to measure the innovation of the data points.

no code implementations • CVPR 2021 • Gang Fu, Qing Zhang, Lei Zhu, Ping Li, Chunxia Xiao

Specular highlight detection and removal are fundamental and challenging tasks.

no code implementations • CVPR 2021 • Zilong Zheng, Jianwen Xie, Ping Li

Exploiting internal statistics of a single natural image has long been recognized as a significant research paradigm where the goal is to learn the distribution of patches within the image without relying on external training data.

no code implementations • CVPR 2021 • Dongsheng An, Jianwen Xie, Ping Li

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables.

no code implementations • 14 Jun 2021 • Tan Yu, Xu Li, Yunfeng Cai, Mingming Sun, Ping Li

We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration.

no code implementations • NAACL 2021 • Hongliang Fei, Tan Yu, Ping Li

Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English.

no code implementations • 1 Apr 2021 • Jingfeng Lu, Shuo Wang, Ping Li, Dong Ye

Low-dose computed tomography (CT) allows the reduction of radiation risk in clinical applications at the expense of image quality, which deteriorates the diagnosis accuracy of radiologists.

no code implementations • 25 Feb 2021 • Xiaoyun Li, Ping Li

Closely related to RP, the method of random Fourier features (RFF) has also become popular, for approximating the Gaussian kernel.

no code implementations • 16 Feb 2021 • Mengzhi Wu, Qian Liu, Ping Li, Shi Chen, Binlong Wang, Wenhan Shen, Shiping Chen, Yangheng Zheng, Yigang Xie, Jin Li

The IBF and the transparent rate of electrons are two essential indicators of TPC, which affect the energy resolution and counting rate respectively.

Instrumentation and Detectors High Energy Physics - Experiment

no code implementations • ICLR 2021 • Yang Zhao, Jianwen Xie, Ping Li

Energy-based models (EBMs) for generative modeling parametrize a single net and can be directly trained by maximum likelihood estimation.

no code implementations • 1 Jan 2021 • Xiangyi Chen, Belhal Karimi, Weijie Zhao, Ping Li

Specifically, we propose a general algorithmic framework that can convert existing adaptive gradient methods to their decentralized counterparts.

no code implementations • 1 Jan 2021 • Belhal Karimi, Hoi To Wai, Eric Moulines, Ping Li

Many constrained, nonconvex and nonsmooth optimization problems can be tackled using the majorization-minimization (MM) method which alternates between constructing a surrogate function which upper bounds the objective function, and then minimizing this surrogate.

no code implementations • 1 Jan 2021 • Tan Yu, Hongliang Fei, Ping Li

Inspired by the great success of BERT in NLP tasks, many text-vision BERT models emerged recently.

no code implementations • 29 Dec 2020 • Jianwen Xie, Zilong Zheng, Ping Li

In this paper, we propose to learn a variational auto-encoder (VAE) to initialize the finite-step MCMC, such as Langevin dynamics that is derived from the energy function, for efficient amortized sampling of the EBM.

no code implementations • NeurIPS 2020 • Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li

Adaptive gradient methods such as AdaGrad, RMSprop and Adam have been optimizers of choice for deep learning due to their fast training speed.

no code implementations • NeurIPS 2020 • Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li

We reformulate the Wasserstein Discriminant Analysis (WDA) as a ratio trace problem and present an eigensolver-based algorithm to compute the discriminative subspace of WDA.

1 code implementation • NeurIPS 2020 • Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin

In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image.

no code implementations • NeurIPS 2020 • Yi Hao, Ping Li

Based on a sample of size $n$, we consider estimating the number of symbols that appear at least $\mu$ times in an independent sample of size $a \cdot n$, where $a$ is a given parameter.

1 code implementation • NeurIPS 2020 • Shaogang Ren, Weijie Zhao, Ping Li

L1 regularization has been broadly employed to pursue model sparsity.

no code implementations • 2 Nov 2020 • Yunfeng Cai, Ping Li

This paper considers the identification problem for BJBDP, that is, under what conditions and by what means, we can identify the diagonalizer $A$ and the block diagonal structure of $\Sigma_i$, especially when there is noise in $C_i$'s.

no code implementations • 29 Oct 2020 • Yunfeng Cai, Ping Li

In this paper, we propose to solve LRTC via tensor networks with a Tucker wrapper.

no code implementations • 23 Sep 2020 • Ping Li, Qinghao Ye, Luming Zhang, Li Yuan, Xianghua Xu, Ling Shao

In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames.

no code implementations • 18 Sep 2020 • Ruoxin Chen, Jie Li, Chentao Wu, Bin Sheng, Ping Li

Random selection based defenses can achieve certified robustness by averaging the classifiers' predictions on the sub-datasets sampled from the training set.

no code implementations • 27 Aug 2020 • Jerry Chee, Ping Li

We construct a statistical diagnostic test for convergence to the stationary phase using the inner product between successive gradients and demonstrate that the proposed diagnostic works well.

no code implementations • 11 Aug 2020 • Farzin Haddadpour, Belhal Karimi, Ping Li, Xiaoyun Li

Communication complexity and privacy are the two key challenges in Federated Learning where the goal is to perform a distributed learning through a large volume of devices.

no code implementations • 12 Jul 2020 • Liang Zhang, Johann Li, Ping Li, Xiaoyuan Lu, Peiyi Shen, Guangming Zhu, Syed Afaq Shah, Mohammed Bennarmoun, Kun Qian, Björn W. Schuller

To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side.

no code implementations • ACL 2020 • Hongliang Fei, Ping Li

Recent neural network models have achieved impressive performance on sentiment classification in English as well as other languages.

no code implementations • ACL 2020 • Jingyuan Zhang, Mingming Sun, Yue Feng, Ping Li

Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.

no code implementations • 3 Jun 2020 • Ping Li, Ming Chen, Yijie Mao, Zhaohui Yang, Bruno Clerckx, Mohammad Shikh-Bahaei

In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes.

1 code implementation • 20 Apr 2020 • Shaogang Ren, Dingcheng Li, Zhixin Zhou, Ping Li

The thriving of deep models and generative models provides approaches to model high dimensional distributions.

no code implementations • 2 Apr 2020 • Xiaoyun Li, Chengxi Wu, Ping Li

Feature selection is an important tool to deal with high dimensional data.

no code implementations • 2 Apr 2020 • Xiaoyun Li, Jie Gui, Ping Li

In this paper, we propose the kernel version of multi-view discriminant analysis, called kernel multi-view discriminant analysis (KMvDA).

no code implementations • 2 Apr 2020 • Peng Yang, Ping Li

Conventional online multi-task learning algorithms suffer from two critical limitations: 1) Heavy communication caused by delivering high velocity of sequential data to a central machine; 2) Expensive runtime complexity for building task relatedness.

no code implementations • 24 Mar 2020 • Yunfeng Cai, Ping Li

Particularly, a new truncation strategy is proposed, which is able to find the support set of the leading eigenvector effectively.

no code implementations • 24 Mar 2020 • Yunfeng Cai, Ping Li

We consider the problem of robust matrix completion, which aims to recover a low rank matrix $L_*$ and a sparse matrix $S_*$ from incomplete observations of their sum $M=L_*+S_*\in\mathbb{R}^{m\times n}$.

no code implementations • 12 Mar 2020 • Haiyan Yin, Dingcheng Li, Xu Li, Ping Li

To this end, we introduce a cooperative training paradigm, where a language model is cooperatively trained with the generator and we utilize the language model to efficiently shape the data distribution of the generator against mode collapse.

no code implementations • 12 Mar 2020 • Weijie Zhao, Deping Xie, Ronglai Jia, Yulei Qian, Ruiquan Ding, Mingming Sun, Ping Li

For example, a sponsored online advertising system can contain more than $10^{11}$ sparse features, making the neural network a massive model with around 10 TB parameters.

no code implementations • 4 Feb 2020 • Hefei Ling, Yangyang Qin, Li Zhang, Yuxuan Shi, Ping Li

It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors.

no code implementations • 30 Jan 2020 • Liang Zhang, Xudong Wang, Hongsheng Li, Guangming Zhu, Peiyi Shen, Ping Li, Xiaoyuan Lu, Syed Afaq Ali Shah, Mohammed Bennamoun

To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes.

no code implementations • 30 Dec 2019 • Mostafa Rahmani, Ping Li

In this paper, we present a new discovery that the directions of innovation can be used to design a provable and strong robust (to outlier) PCA method.

no code implementations • NeurIPS 2019 • Zhiqiang Xu, Ping Li

To promote the practical use of ALS for CCA, we propose truly alternating least-squares.

no code implementations • NeurIPS 2019 • Xiaoyun Li, Ping Li

The method of random projection has been a popular tool for data compression, similarity search, and machine learning.

no code implementations • NeurIPS 2019 • Mostafa Rahmani, Ping Li

In other word, an outlier carries some innovation with respect to most of the other data points.

no code implementations • NeurIPS 2019 • Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li

We present a fast search on graph algorithm for Maximum Inner Product Search (MIPS).

no code implementations • NeurIPS 2019 • Xiaoyun Li, Ping Li

In this paper, we consider the learning problem where the projected data is further compressed by scalar quantization, which is called quantized compressive learning.

no code implementations • NeurIPS 2019 • Ping Li, Xiaoyun Li, Cun-Hui Zhang

Jaccard similarity is widely used as a distance measure in many machine learning and search applications.

no code implementations • 5 Nov 2019 • Fan Zhou, Ping Li

Let $\mathbf{x}_j = \mathbf{\theta} + \mathbf{\epsilon}_j$, $j=1,\dots, n$ be i. i. d.

no code implementations • IJCNLP 2019 • Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li

Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks.

no code implementations • IJCNLP 2019 • Miao Fan, Chao Feng, Mingming Sun, Ping Li

Given a product, a selector (agent) learns from both the keys in the product metadata and one of its reviews to take an action that selects the correct value, and a successive predictor (network) makes the free-text review attend to this value to obtain better neural representations for helpfulness assessment.

no code implementations • 3 Oct 2019 • Mostafa Rahmani, Ping Li

The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph.

no code implementations • 5 Sep 2019 • Hang Zhang, Martin Slawski, Ping Li

For the case in which both the signal and permutation are unknown, the problem is reformulated as a bi-convex optimization problem with an auxiliary variable, which can be solved by the Alternating Direction Method of Multipliers (ADMM).

no code implementations • 23 Aug 2019 • Weichen Dai, Yu Zhang, Donglei Sun, Naira Hovakimyan, Ping Li

Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.

no code implementations • 6 Aug 2019 • Xiao-Tong Yuan, Ping Li

We first introduce a simple variant of DANE equipped with backtracking line search, for which global asymptotic convergence and sharper local non-asymptotic convergence rate guarantees can be proved for both quadratic and non-quadratic strongly convex functions.

no code implementations • 16 Jul 2019 • Martin Slawski, Emanuel Ben-David, Ping Li

A tacit assumption in linear regression is that (response, predictor)-pairs correspond to identical observational units.

no code implementations • ACL 2019 • Hongliang Fei, Xu Li, Dingcheng Li, Ping Li

Recent neural network models have significantly advanced the task of coreference resolution.

Ranked #7 on Coreference Resolution on OntoNotes

no code implementations • NAACL 2019 • Dingcheng Li, Siamak Zamani, Jingyuan Zhang, Ping Li

Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models.

no code implementations • ICLR 2019 • Ping Li, Phan-Minh Nguyen

We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights.

no code implementations • ICLR 2019 • Mostafa Rahmani, Ping Li

In the second step, the GNN is applied to the point-cloud representation of the graph provided by the embedding method.

no code implementations • ICLR 2019 • Jun-Kun Wang, Xiaoyun Li, Ping Li

We consider new variants of optimization algorithms.

no code implementations • 29 Apr 2019 • Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.

no code implementations • 17 Apr 2019 • Li Yuan, Francis EH Tay, Ping Li, Li Zhou, Jiashi Feng

The evaluator defines a learnable information preserving metric between original video and summary video and "supervises" the selector to identify the most informative frames to form the summary video.

Ranked #5 on Unsupervised Video Summarization on SumMe

no code implementations • ICLR 2020 • Jun-Kun Wang, Xiaoyun Li, Belhal Karimi, Ping Li

We propose a new variant of AMSGrad, a popular adaptive gradient based optimization algorithm widely used for training deep neural networks.

no code implementations • 8 Nov 2018 • Weichen Dai, Yu Zhang, Ping Li, Zheng Fang, Sebastian Scherer

This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups.

no code implementations • EMNLP 2018 • Mingming Sun, Xu Li, Ping Li

We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain.

no code implementations • ICML 2018 • Jing Wang, Jie Shen, Ping Li

As a remedy, online feature selection has attracted increasing attention in recent years.

no code implementations • 8 May 2018 • Ping Li

In this study, we propose a series of "tunable GMM kernels" which are simple and perform largely comparably to tree methods on the same datasets.

no code implementations • 26 Apr 2018 • Ping Li

At high similarity ($\rho\rightarrow1$), the asymptotic variance of recommended estimator is only $\frac{4}{3\pi} \approx 0. 4$ of the estimator for sign-sign projections.

no code implementations • NeurIPS 2017 • Ping Li, Martin Slawski

Random projections have been increasingly adopted for a diverse set of tasks in machine learning involving dimensionality reduction.

no code implementations • NeurIPS 2017 • Jie Shen, Ping Li

In machine learning and compressed sensing, it is of central importance to understand when a tractable algorithm recovers the support of a sparse signal from its compressed measurements.

no code implementations • 27 Oct 2017 • Ping Li, Tingyan Duan, Yongfeng Cao

Image matting is an important vision problem.

no code implementations • ICML 2017 • Jie Shen, Ping Li

Recovering the support of a sparse signal from its compressed samples has been one of the most important problems in high dimensional statistics.

no code implementations • 5 Mar 2017 • Yongwei Nie, Xu Cao, Chengjiang Long, Ping Li, Guiqing Li

Current face alignment algorithms can robustly find a set of landmarks along face contour.

no code implementations • 9 Jan 2017 • Ping Li

The linearized GMM kernel was extensively compared in with linearized radial basis function (RBF) kernel.

no code implementations • 29 Dec 2016 • Ping Li

Following the very recent line of work on the ``generalized min-max'' (GMM) kernel, this study proposes the ``generalized intersection'' (GInt) kernel and the related ``normalized generalized min-max'' (NGMM) kernel.

no code implementations • NeurIPS 2016 • Xiaotong Yuan, Ping Li, Tong Zhang

In this paper, we bridge this gap by showing, for the first time, that exact recovery of the global sparse minimizer is possible for HTP-style methods under restricted strong condition number bounding conditions.

no code implementations • NeurIPS 2016 • Ping Li, Michael Mitzenmacher, Martin Slawski

Random projections constitute a simple, yet effective technique for dimensionality reduction with applications in learning and search problems.

no code implementations • NeurIPS 2016 • Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu

We investigate a subclass of exponential family graphical models of which the sufficient statistics are defined by arbitrary additive forms.

no code implementations • 16 Nov 2016 • Ping Li, Jiajun Bu, Chun Chen, Zhanying He, Deng Cai

In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces.

no code implementations • 15 Nov 2016 • Ping Li, Jun Yu, Meng Wang, Luming Zhang, Deng Cai, Xuelong. Li

To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization.

no code implementations • 1 Aug 2016 • Ping Li, Cun-Hui Zhang

We prove the theoretical limit of GMM and the consistency result, assuming that the data follow an elliptical distribution, which is a very general family of distributions and includes the multivariate $t$-distribution as a special case.

no code implementations • 12 Jul 2016 • Ping Li

In order to use the GMM kernel for large-scale datasets, the prior work resorted to the (generalized) consistent weighted sampling (GCWS) to convert the GMM kernel to linear kernel.

no code implementations • 25 May 2016 • Yanxiang Chen, Yuxing Hu, Luming Zhang, Ping Li, Chao Zhang

To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting.

no code implementations • 18 May 2016 • Ping Li

The variance of RFF is actually large.

no code implementations • 5 May 2016 • Jie Shen, Ping Li

This paper is concerned with the hard thresholding operator which sets all but the $k$ largest absolute elements of a vector to zero.

no code implementations • 2 May 2016 • Ping Li, Syama Sundar Rangapuram, Martin Slawski

The de-facto standard approach of promoting sparsity by means of $\ell_1$-regularization becomes ineffective in the presence of simplex constraints, i. e.,~the target is known to have non-negative entries summing up to a given constant.

no code implementations • 21 Mar 2016 • Ping Li

In this paper, we compare 5 different nonlinear kernels: min-max, RBF, fRBF (folded RBF), acos, and acos-$\chi^2$, on a wide range of publicly available datasets.

no code implementations • 21 Feb 2016 • Ping Li, Michael Mitzenmacher, Anshumali Shrivastava

In this paper, we focus on a simple 2-bit coding scheme.

no code implementations • NeurIPS 2015 • Martin Slawski, Ping Li

We consider the problem of sparse signal recovery from $m$ linear measurements quantized to $b$ bits.

no code implementations • 27 Apr 2015 • Ping Li

When $\alpha =2$, it is known that the corresponding nonlinear kernel is the arc-cosine kernel.

no code implementations • NeurIPS 2015 • Martin Slawski, Ping Li, Matthias Hein

Over the past few years, trace regression models have received considerable attention in the context of matrix completion, quantum state tomography, and compressed sensing.

no code implementations • 28 Mar 2015 • Jie Shen, Ping Li, Huan Xu

Low-rank representation~(LRR) has been a significant method for segmenting data that are generated from a union of subspaces.

no code implementations • 8 Mar 2015 • Ping Li

Based on $\alpha$-stable random projections with small $\alpha$, we develop a simple algorithm for compressed sensing (sparse signal recovery) by utilizing only the signs (i. e., 1-bit) of the measurements.

no code implementations • 5 Mar 2015 • Ping Li

Via an extensive empirical study, we show that this 0-bit scheme does not lose essential information.

no code implementations • 5 Feb 2015 • Jing Wang, Jie Shen, Ping Li

In order to determine a small set of proposals with a high recall, a common scheme is extracting multiple features followed by a ranking algorithm which however, incurs two major challenges: {\bf 1)} The ranking model often imposes pairwise constraints between each proposal, rendering the problem away from an efficient training/testing phase; {\bf 2)} Linear kernels are utilized due to the computational and memory bottleneck of training a kernelized model.

no code implementations • NeurIPS 2014 • Jie Shen, Huan Xu, Ping Li

The key technique in our algorithm is to reformulate the max-norm into a matrix factorization form, consisting of a basis component and a coefficients one.

1 code implementation • 14 Nov 2014 • Anshumali Shrivastava, Ping Li

Minwise hashing (Minhash) is a widely popular indexing scheme in practice.

no code implementations • 20 Oct 2014 • Anshumali Shrivastava, Ping Li

In the prior work, the authors use asymmetric transformations which convert the problem of approximate MIPS into the problem of approximate near neighbor search which can be efficiently solved using hashing.

no code implementations • 9 Oct 2014 • Jian Wang, Ping Li

We study the problem of recovering sparse signals from compressed linear measurements.

no code implementations • 11 Aug 2014 • Ping Li, Cun-Hui Zhang

We have developed two estimators: (i) the {\em tie estimator}, and (ii) the {\em absolute minimum estimator}.

no code implementations • 16 Jul 2014 • Anshumali Shrivastava, Ping Li

To provide a common basis for comparison, we evaluate retrieval results in terms of $\mathcal{S}$ for both MinHash and SimHash.

1 code implementation • 18 Jun 2014 • Anshumali Shrivastava, Ping Li

The existing work on densification of one permutation hashing reduces the query processing cost of the $(K, L)$-parameterized Locality Sensitive Hashing (LSH) algorithm with minwise hashing, from $O(dKL)$ to merely $O(d + KL)$, where $d$ is the number of nonzeros of the data vector, $K$ is the number of hashes in each hash table, and $L$ is the number of hash tables.

no code implementations • 12 Jun 2014 • Jie Shen, Huan Xu, Ping Li

Max-norm regularizer has been extensively studied in the last decade as it promotes an effective low-rank estimation for the underlying data.

no code implementations • NeurIPS 2014 • Anshumali Shrivastava, Ping Li

Our proposal is based on an interesting mathematical phenomenon in which inner products, after independent asymmetric transformations, can be converted into the problem of approximate near neighbor search.

no code implementations • 24 Apr 2014 • Ping Li

The term "CoRE kernel" stands for correlation-resemblance kernel.

no code implementations • 21 Apr 2014 • Anshumali Shrivastava, Ping Li

We propose a representation of graph as a functional object derived from the power iteration of the underlying adjacency matrix.

no code implementations • 17 Apr 2014 • Guangcan Liu, Ping Li

To better handle non-uniform data, in this paper we propose a method termed Low-Rank Factor Decomposition (LRFD), which imposes an additional restriction that the data points must be represented as linear combinations of the bases in a dictionary constructed or learnt in advance.

no code implementations • 17 Apr 2014 • Anshumali Shrivastava, Ping Li

We show that the proposed matrix representation encodes the spectrum of the underlying adjacency matrix and it also contains information about the counts of small sub-structures present in the graph such as triangles and small paths.

no code implementations • NeurIPS 2014 • Guangcan Liu, Ping Li

More precisely, we mathematically prove that if the dictionary itself is low-rank then LRR is immune to the coherence parameter which increases with the underlying cluster number.

no code implementations • 31 Mar 2014 • Ping Li, Michael Mitzenmacher, Anshumali Shrivastava

This technical note compares two coding (quantization) schemes for random projections in the context of sub-linear time approximate near neighbor search.

no code implementations • 8 Mar 2014 • Ping Li, Hong Li, Min Wu

For the boosting-like strategy, we employ both the variable pairwise constraints and the bootstrap steps to diversify the base classifiers.

no code implementations • 31 Dec 2013 • Ping Li, Cun-Hui Zhang, Tong Zhang

In this paper, we adopt very sparse Compressed Counting for nonnegative signal recovery.

no code implementations • 16 Dec 2013 • Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li

The Alternating Direction Method of Multipliers (ADMM) has been studied for years.

no code implementations • NeurIPS 2013 • Anshumali Shrivastava, Ping Li

We go beyond the notion of pairwise similarity and look into search problems with $k$-way similarity functions.

no code implementations • NeurIPS 2013 • Ping Li, Gennady Samorodnitsk, John Hopcroft

The method of Cauchy random projections is popular for computing the $l_1$ distance in high dimension.

no code implementations • 22 Nov 2013 • Xiao-Tong Yuan, Ping Li, Tong Zhang

Numerical evidences show that our method is superior to the state-of-the-art greedy selection methods in sparse logistic regression and sparse precision matrix estimation tasks.

no code implementations • 21 Nov 2013 • Xiao-Tong Yuan, Ping Li, Tong Zhang

We investigate a generic problem of learning pairwise exponential family graphical models with pairwise sufficient statistics defined by a global mapping function, e. g., Mercer kernels.

no code implementations • 3 Oct 2013 • Ping Li, Cun-Hui Zhang, Tong Zhang

In particular, when p->0 the required number of measurements is essentially M=K\log N, where K is the number of nonzero coordinates of the signal.

no code implementations • 9 Aug 2013 • Ping Li, Michael Mitzenmacher, Anshumali Shrivastava

The method of random projections has become very popular for large-scale applications in statistical learning, information retrieval, bio-informatics and other applications.

no code implementations • 5 Aug 2013 • Ping Li, Gennady Samorodnitsky, John Hopcroft

The method of stable random projections is popular for efficiently computing the Lp distances in high dimension (where 0<p<=2), using small space.

no code implementations • NeurIPS 2012 • Ping Li, Cun-Hui Zhang

Methods for efficiently estimating the Shannon entropy of data streams have important applications in learning, data mining, and network anomaly detections (e. g., the DDoS attacks).

no code implementations • NeurIPS 2012 • Ping Li, Art Owen, Cun-Hui Zhang

While minwise hashing is promising for large-scale learning in massive binary data, the preprocessing cost is prohibitive as it requires applying (e. g.,) $k=500$ permutations on the data.

no code implementations • NeurIPS 2011 • Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd C. König

Minwise hashing is a standard technique in the context of search for efficiently computing set similarities.

1 code implementation • 23 May 2011 • Ping Li, Joshua Moore, Christian Konig

Interestingly, our proof for the positive definiteness of the b-bit minwise hashing kernel naturally suggests a simple strategy to integrate b-bit hashing with linear SVM.

no code implementations • NeurIPS 2010 • Ping Li, Arnd Konig, Wenhao Gui

Computing two-way and multi-way set similarities is a fundamental problem.

no code implementations • NeurIPS 2008 • Ping Li, Kenneth W. Church, Trevor J. Hastie

Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise ($l_2$, $l_1$) distances, in static, large-scale, and sparse data sets such as text and Web data.

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