Ji Liu

End-to-end Robustness for Sensing-Reasoning Machine Learning Pipelines

As machine learning (ML) being applied to many mission-critical scenario...
02/28/2020 ∙ by Zhuolin Yang, et al. ∙ 25 ∙ share

read it
Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis

For recovering 3D object poses from 2D images, a prevalent method is to ...
12/29/2018 ∙ by Jianqiao Wangni, et al. ∙ 24 ∙ share

read it
Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend

Decentralized Online Learning (online learning in decentralized networks...
01/29/2019 ∙ by Yawei Zhao, et al. ∙ 24 ∙ share

read it
Adversarially Trained Model Compression: When Robustness Meets Efficiency

The robustness of deep models to adversarial attacks has gained signific...
02/10/2019 ∙ by Shupeng Gui, et al. ∙ 15 ∙ share

read it
Depth Edge Guided CNNs for Sparse Depth Upsampling

Guided sparse depth upsampling aims to upsample an irregularly sampled s...
03/23/2020 ∙ by Yi Guo, et al. ∙ 12 ∙ share

read it
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

Starcraft II (SCII) is widely considered as the most challenging Real Ti...
09/19/2018 ∙ by Peng Sun, et al. ∙ 10 ∙ share

read it
An Interactive Control Approach to 3D Shape Reconstruction

The ability to accurately reconstruct the 3D facets of a scene is one of...
10/07/2019 ∙ by Bipul Islam, et al. ∙ 9 ∙ share

read it
Fully Implicit Online Learning

Regularized online learning is widely used in machine learning. In this ...
09/25/2018 ∙ by Chaobing Song, et al. ∙ 8 ∙ share

read it
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval

Enabling bi-directional retrieval of images and texts is important for u...
03/08/2020 ∙ by Hui Chen, et al. ∙ 8 ∙ share

read it
Variance Reduced methods for Non-convex Composition Optimization

This paper explores the non-convex composition optimization in the form ...
11/13/2017 ∙ by Liu Liu, et al. ∙ 0 ∙ share

read it
Duality-free Methods for Stochastic Composition Optimization

We consider the composition optimization with two expected-value functio...
10/26/2017 ∙ by Liu Liu, et al. ∙ 0 ∙ share

read it
Asynchronous Decentralized Parallel Stochastic Gradient Descent

Recent work shows that decentralized parallel stochastic gradient decent...
10/18/2017 ∙ by Xiangru Lian, et al. ∙ 0 ∙ share

read it
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application

Automatic event detection from time series signals has wide applications...
09/12/2017 ∙ by Yue Wu, et al. ∙ 0 ∙ share

read it
Lifelong Metric Learning

The state-of-the-art online learning approaches are only capable of lear...
05/03/2017 ∙ by Gan Sun, et al. ∙ 0 ∙ share

read it
An Interactive Greedy Approach to Group Sparsity in High Dimension

Sparsity learning with known grouping structures has received considerab...
07/10/2017 ∙ by Wei Qian, et al. ∙ 0 ∙ share

read it
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent

Most distributed machine learning systems nowadays, including TensorFlow...
05/25/2017 ∙ by Xiangru Lian, et al. ∙ 0 ∙ share

read it
Asynchronous Parallel Empirical Variance Guided Algorithms for the Thresholding Bandit Problem

This paper considers the multi-armed thresholding bandit problem -- iden...
04/15/2017 ∙ by Jie Zhong, et al. ∙ 0 ∙ share

read it
On The Projection Operator to A Three-view Cardinality Constrained Set

The cardinality constraint is an intrinsic way to restrict the solution ...
03/21/2017 ∙ by Haichuan Yang, et al. ∙ 0 ∙ share

read it
GaDei: On Scale-up Training As A Service For Deep Learning

Deep learning (DL) training-as-a-service (TaaS) is an important emerging...
11/18/2016 ∙ by Wei Zhang, et al. ∙ 0 ∙ share

read it
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning

Recently there has been significant interest in training machine-learnin...
11/16/2016 ∙ by Hantian Zhang, et al. ∙ 0 ∙ share

read it
Accelerating Stochastic Composition Optimization

Consider the stochastic composition optimization problem where the objec...
07/25/2016 ∙ by Mengdi Wang, et al. ∙ 0 ∙ share

read it
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution

In this paper, we consider linear state-space models with compressible i...
10/20/2016 ∙ by Abbas Kazemipour, et al. ∙ 0 ∙ share

read it
Exclusive Sparsity Norm Minimization with Random Groups via Cone Projection

Many practical applications such as gene expression analysis, multi-task...
10/27/2015 ∙ by Yijun Huang, et al. ∙ 0 ∙ share

read it
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

Asynchronous parallel implementations of stochastic gradient (SG) have b...
06/27/2015 ∙ by Xiangru Lian, et al. ∙ 0 ∙ share

read it
Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint

We consider forward-backward greedy algorithms for solving sparse featur...
12/31/2013 ∙ by Ji Liu, et al. ∙ 0 ∙ share

read it
Dictionary LASSO: Guaranteed Sparse Recovery under Linear Transformation

We consider the following signal recovery problem: given a measurement m...
04/30/2013 ∙ by Ji Liu, et al. ∙ 0 ∙ share

read it
Robust Dequantized Compressive Sensing

We consider the reconstruction problem in compressed sensing in which th...
07/03/2012 ∙ by Ji Liu, et al. ∙ 0 ∙ share

read it
A Game-Theoretic Method for Multi-Period Demand Response: Revenue Maximization, Power Allocation, and Asymptotic Behavior

We study a multi-period demand response management problem in the smart ...
09/30/2017 ∙ by Khaled Alshehri, et al. ∙ 0 ∙ share

read it
On the Analysis of the DeGroot-Friedkin Model with Dynamic Relative Interaction Matrices

This paper analyses the DeGroot-Friedkin model for evolution of the indi...
03/15/2017 ∙ by Mengbin Ye, et al. ∙ 0 ∙ share

read it
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads

We present ease.ml, a declarative machine learning service platform we b...
08/24/2017 ∙ by Tian Li, et al. ∙ 0 ∙ share

read it
Countries' Survival in Networked International Environments

This paper applies a recently developed power allocation game in Li and ...
10/09/2017 ∙ by Yuke Li, et al. ∙ 0 ∙ share

read it
Evolution of Social Power in Social Networks with Dynamic Topology

The recently proposed DeGroot-Friedkin model describes the dynamical evo...
05/27/2017 ∙ by Mengbin Ye, et al. ∙ 0 ∙ share

read it
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification

The positive-unlabeled (PU) classification is a common scenario in real-...
03/18/2018 ∙ by Ke Ren, et al. ∙ 0 ∙ share

read it
D^2: Decentralized Training over Decentralized Data

While training a machine learning model using multiple workers, each of ...
03/19/2018 ∙ by Hanlin Tang, et al. ∙ 0 ∙ share

read it
Decentralization Meets Quantization

Optimizing distributed learning systems is an art of balancing between c...
03/17/2018 ∙ by Hanlin Tang, et al. ∙ 0 ∙ share

read it
Multi-device, Multi-tenant Model Selection with GP-EI

Bayesian optimization is the core technique behind the emergence of Auto...
03/17/2018 ∙ by Chen Yu, et al. ∙ 0 ∙ share

read it
Learning Simple Thresholded Features with Sparse Support Recovery

The thresholded feature has recently emerged as an extremely efficient, ...
04/16/2018 ∙ by Zhangyang Wang, et al. ∙ 0 ∙ share

read it
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning

Graph embedding is a central problem in social network analysis and many...
05/28/2018 ∙ by Shupeng Gui, et al. ∙ 0 ∙ share

read it
Discrete-Time Polar Opinion Dynamics with Susceptibility

This paper considers a discrete-time opinion dynamics model in which eac...
09/21/2017 ∙ by Ji Liu, et al. ∙ 0 ∙ share

read it
End-to-End Learning of Energy-Constrained Deep Neural Networks

Deep Neural Networks (DNN) are increasingly deployed in highly energy-co...
06/12/2018 ∙ by Haichuan Yang, et al. ∙ 0 ∙ share

read it
Marginal Policy Gradients for Complex Control

Many complex domains, such as robotics control and real-time strategy (R...
06/13/2018 ∙ by Carson Eisenach, et al. ∙ 0 ∙ share

read it
Gradient Sparsification for Communication-Efficient Distributed Optimization

Modern large scale machine learning applications require stochastic opti...
10/26/2017 ∙ by Jianqiao Wangni, et al. ∙ 0 ∙ share

read it
Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem

In this paper, we consider the convex and non-convex composition problem...
09/06/2018 ∙ by Liu Liu, et al. ∙ 0 ∙ share

read it
Proximal Online Gradient is Optimum for Dynamic Regret

In online learning, the dynamic regret metric chooses the reference (opt...
10/08/2018 ∙ by Yawei Zhao, et al. ∙ 0 ∙ share

read it
Revisit Batch Normalization: New Understanding from an Optimization View and a Refinement via Composition Optimization

Batch Normalization (BN) has been used extensively in deep learning to a...
10/15/2018 ∙ by Xiangru Lian, et al. ∙ 0 ∙ share

read it
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

Most existing deep reinforcement learning (DRL) frameworks consider eith...
10/10/2018 ∙ by Jiechao Xiong, et al. ∙ 0 ∙ share

read it
P-MCGS: Parallel Monte Carlo Acyclic Graph Search

Recently, there have been great interests in Monte Carlo Tree Search (MC...
10/28/2018 ∙ by Chen Yu, et al. ∙ 0 ∙ share

read it
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity

Regularized empirical risk minimization problem with linear predictor ap...
11/03/2018 ∙ by Conghui Tan, et al. ∙ 0 ∙ share

read it
Distributed Learning over Unreliable Networks

Most of today's distributed machine learning systems assume reliable ne...
10/17/2018 ∙ by Hanlin Tang, et al. ∙ 0 ∙ share

read it
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning

Dantzig Selector (DS) is widely used in compressed sensing and sparse le...
11/02/2018 ∙ by Bo Liu, et al. ∙ 0 ∙ share

read it

Ji Liu

Featured Co-authors