1 code implementation • NeurIPS 2020 • Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

To this end, we develop a provable algorithm for online structured tensor factorization, wherein one of the factors obeys some incoherence conditions, and the others are sparse.

1 code implementation • 27 Jun 2020 • Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e. g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies.

no code implementations • 27 Jun 2020 • Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME).

no code implementations • NeurIPS 2020 • Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora

Our work proves convergence to low robust training loss for \emph{polynomial} width instead of exponential, under natural assumptions and with the ReLU activation.

no code implementations • ICLR 2020 • Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao

Generative Adversarial Imitation Learning (GAIL) is a powerful and practical approach for learning sequential decision-making policies.

no code implementations • 8 Jan 2020 • Shahana Ibrahim, Xiao Fu, Xingguo Li

Our interest lies in the recoverability properties of compressed tensors under the \textit{canonical polyadic decomposition} (CPD) model.

no code implementations • ICLR 2019 • Minshuo Chen, Xingguo Li, Tuo Zhao

We remark: (1) Our generalization bound for vanilla RNNs is significantly tighter than the best of existing results; (2) We are not aware of any other generalization bounds for MGU, LSTM, and Conv RNNs in the exiting literature; (3) We demonstrate the advantages of these variants in generalization.

1 code implementation • NeurIPS 2019 • Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David Cox

In this paper, we propose a zeroth-order AdaMM (ZO-AdaMM) algorithm, that generalizes AdaMM to the gradient-free regime.

no code implementations • ICLR 2019 • Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt, Tuo Zhao

We propose a generalization error bound for a general family of deep neural networks based on the depth and width of the networks, as well as the spectral norm of weight matrices.

no code implementations • ICLR 2019 • Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

To this end, we develop a simple online alternating optimization-based algorithm for dictionary learning, which recovers both the dictionary and coefficients exactly at a geometric rate.

no code implementations • 28 Feb 2019 • Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients.

no code implementations • 26 Feb 2019 • Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

In this work, we present a technique to localize targets of interest based on their spectral signatures.

no code implementations • 26 Feb 2019 • Sirisha Rambhatla, Xingguo Li, Jineng Ren, Jarvis Haupt

We consider the task of localizing targets of interest in a hyperspectral (HS) image based on their spectral signature(s), by posing the problem as two distinct convex demixing task(s).

no code implementations • 21 Feb 2019 • Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing method.

no code implementations • 21 Feb 2019 • Sirisha Rambhatla, Xingguo Li, Jineng Ren, Jarvis Haupt

We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing method.

no code implementations • 13 Jun 2018 • Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis Haupt, Tuo Zhao

We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the networks.

no code implementations • 13 Jun 2018 • Zhehui Chen, Xingguo Li, Lin F. Yang, Jarvis Haupt, Tuo Zhao

However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown.

no code implementations • NeurIPS 2017 • Xingguo Li, Lin Yang, Jason Ge, Jarvis Haupt, Tong Zhang, Tuo Zhao

We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions.

no code implementations • NeurIPS 2017 • Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song

In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.

no code implementations • ICML 2018 • Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song

We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.

no code implementations • NeurIPS 2017 • Jarvis Haupt, Xingguo Li, David P. Woodruff

We study the least squares regression problem \begin{align*} \min_{\Theta \in \mathcal{S}_{\odot D, R}} \|A\Theta-b\|_2, \end{align*} where $\mathcal{S}_{\odot D, R}$ is the set of $\Theta$ for which $\Theta = \sum_{r=1}^{R} \theta_1^{(r)} \circ \cdots \circ \theta_D^{(r)}$ for vectors $\theta_d^{(r)} \in \mathbb{R}^{p_d}$ for all $r \in [R]$ and $d \in [D]$, and $\circ$ denotes the outer product of vectors.

no code implementations • 19 Jun 2017 • Xingguo Li, Lin F. Yang, Jason Ge, Jarvis Haupt, Tong Zhang, Tuo Zhao

We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions.

no code implementations • 29 Dec 2016 • Xingguo Li, Junwei Lu, Raman Arora, Jarvis Haupt, Han Liu, Zhaoran Wang, Tuo Zhao

We propose a general theory for studying the \xl{landscape} of nonconvex \xl{optimization} with underlying symmetric structures \tz{for a class of machine learning problems (e. g., low-rank matrix factorization, phase retrieval, and deep linear neural networks)}.

no code implementations • 7 Dec 2016 • Xingguo Li, Jarvis Haupt

This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing elements.

no code implementations • 10 Jul 2016 • Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong

In particular, we first show that for a family of quadratic minimization problems, the iteration complexity $\mathcal{O}(\log^2(p)\cdot\log(1/\epsilon))$ of the CBCD-type methods matches that of the GD methods in term of dependency on $p$, up to a $\log^2 p$ factor.

no code implementations • 25 May 2016 • Xingguo Li, Haoming Jiang, Jarvis Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao

Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility.

no code implementations • 9 May 2016 • Xingguo Li, Raman Arora, Han Liu, Jarvis Haupt, Tuo Zhao

We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints.

no code implementations • 1 Jul 2014 • Xingguo Li, Jarvis Haupt

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix.

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