Search Results for author: Jia Liu

Found 52 papers, 12 papers with code

Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning

no code implementations NeurIPS 2021 Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu

To our knowledge, this paper is the first work that achieves both $\mathcal{O}(\epsilon^{-2})$ sample complexity and $\mathcal{O}(\epsilon^{-2})$ communication complexity in decentralized policy evaluation for cooperative MARL.

Multi-agent Reinforcement Learning Stochastic Optimization

Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons

1 code implementation NeurIPS 2021 Wenbo Ren, Jia Liu, Ness Shroff

Here, a multi-wise comparison takes $m$ items as input and returns a (noisy) result about the best item (the winner feedback) or the order of these items (the full-ranking feedback).

CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow Estimation

no code implementations20 Nov 2021 Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, Lijun Chen

In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data.

Optical Flow Estimation Scene Flow Estimation

A global convergence theory for deep ReLU implicit networks via over-parameterization

no code implementations11 Oct 2021 Tianxiang Gao, Hailiang Liu, Jia Liu, Hridesh Rajan, Hongyang Gao

Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectures.

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

1 code implementation17 Sep 2021 Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.

OpenAI Gym

Anarchic Federated Learning

no code implementations23 Aug 2021 Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu

This diverse set of workers necessitates the development of FL algorithms that allow: (1) flexible worker participation that grants the workers' capability to engage in training at will, (2) varying number of local updates (based on computational resources) at each worker along with asynchronous communication with the server, and (3) heterogeneous data across workers.

Federated Learning

Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach

no code implementations25 Jul 2021 Fengjiao Li, Jia Liu, Bo Ji

Considering the achieved training accuracy of the global model as the utility of the selected workers, which is typically a monotone submodular function, we formulate the worker selection problem as a new multi-round monotone submodular maximization problem with cardinality and fairness constraints.

Fairness Federated Learning

The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest

no code implementations4 Jul 2021 Ziwei Cong, Jia Liu, Puneet Manchanda

Specifically, demand gradually becomes less price sensitive over time to the livestreaming day and is inelastic on the livestreaming day.

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning

no code implementations NeurIPS 2021 Prashant Khanduri, Pranay Sharma, Haibo Yang, Mingyi Hong, Jia Liu, Ketan Rajawat, Pramod K. Varshney

Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution.

Federated Learning

CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning

no code implementations14 Jun 2021 Haibo Yang, Jia Liu, Elizabeth S. Bentley

This matches the convergence rate of distributed/federated learning without compression, thus achieving high communication efficiency while not sacrificing learning accuracy in FL.

Federated Learning

Incentivized Bandit Learning with Self-Reinforcing User Preferences

no code implementations19 May 2021 Tianchen Zhou, Jia Liu, Chaosheng Dong, Jingyuan Deng

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences.

Recommendation Systems

GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning

no code implementations4 May 2021 Xin Zhang, Jia Liu, Zhengyuan Zhu, Elizabeth S. Bentley

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity.

Data Poisoning Attacks and Defenses to Crowdsourcing Systems

no code implementations18 Feb 2021 Minghong Fang, Minghao Sun, Qi Li, Neil Zhenqiang Gong, Jin Tian, Jia Liu

Our empirical results show that the proposed defenses can substantially reduce the estimation errors of the data poisoning attacks.

Data Poisoning

Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning

no code implementations ICLR 2021 Haibo Yang, Minghong Fang, Jia Liu

Our results also reveal that the local steps in FL could help the convergence and show that the maximum number of local steps can be improved to $T/m$ in full worker participation.

Federated Learning

Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

no code implementations16 Jan 2021 Jia Liu, Yaochu Jin

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans.

Distributionally robust second-order stochastic dominance constrained optimization with Wasserstein ball

no code implementations4 Jan 2021 Yu Mei, Jia Liu, Zhiping Chen

We consider a distributionally robust second-order stochastic dominance constrained optimization problem.

Optimization and Control 90C15, 91B70, 90C31, 90-08

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

no code implementations27 Dec 2020 Xiaoyu Cao, Minghong Fang, Jia Liu, Neil Zhenqiang Gong

Finally, the service provider computes the average of the normalized local model updates weighted by their trust scores as a global model update, which is used to update the global model.

Federated Learning

$κ$TNG: Effect of Baryonic Processes on Weak Lensing with IllustrisTNG Simulations

1 code implementation19 Oct 2020 Ken Osato, Jia Liu, Zoltán Haiman

The $\kappa$TNG suite includes 10, 000 realisations of $5 \times 5 \, \mathrm{deg}^2$ maps for 40 source redshifts up to $z_s = 2. 6$, well covering the range of interest for existing and upcoming weak lensing surveys.

Cosmology and Nongalactic Astrophysics

The Scale of Superpartner Masses and Electroweakino Searches at the High-Luminosity LHC

no code implementations26 Aug 2020 Jia Liu, Navin McGinnis, Carlos E. M. Wagner, Xiao-Ping Wang

Searches for weakly interacting particles is one of the main goals of the high luminosity LHC run.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Multi-Armed Bandits with Local Differential Privacy

no code implementations6 Jul 2020 Wenbo Ren, Xingyu Zhou, Jia Liu, Ness B. Shroff

To handle this dilemma, we adopt differential privacy and study the regret upper and lower bounds for MAB algorithms with a given LDP guarantee.

Multi-Armed Bandits

The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

1 code implementation ICML 2020 Wenbo Ren, Jia Liu, Ness B. Shroff

From a given set of items, the learner can make pairwise comparisons on every pair of items, and each comparison returns an independent noisy result about the preferred item.

Active Learning

Re-examining the Solar Axion Explanation for the XENON1T Excess

no code implementations25 Jun 2020 Christina Gao, Jia Liu, Lian-Tao Wang, Xiao-Ping Wang, Wei Xue, Yi-Ming Zhong

Meanwhile, they can also scatter with the atoms through the inverse Primakoff process via the axion-photon coupling, which emits a photon and mimics the electronic recoil signals.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Transformation Importance with Applications to Cosmology

2 code implementations4 Mar 2020 Chandan Singh, Wooseok Ha, Francois Lanusse, Vanessa Boehm, Jia Liu, Bin Yu

Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence.

Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

no code implementations19 Feb 2020 Minghong Fang, Neil Zhenqiang Gong, Jia Liu

Given the number of fake users the attacker can inject, we formulate the crafting of rating scores for the fake users as an optimization problem.

Data Poisoning Recommendation Systems

Toward Low-Cost and Stable Blockchain Networks

no code implementations19 Feb 2020 Minghong Fang, Jia Liu

To address the high mining cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks.

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization

no code implementations15 Jan 2020 Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu

Further, we show that the iteration complexity of the proposed method is $O(n\varepsilon^{-2})$ to achieve $\epsilon$-stationary point, where $n$ is the number of blocks of coordinates.


Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach

no code implementations12 Jan 2020 Xin Zhang, Minghong Fang, Jia Liu, Zhengyuan Zhu

In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing.

Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems

no code implementations16 Dec 2019 Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu

In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continuous gradient assumption (e. g., the nonnegative matrix factorization (NMF) problem).

Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms

no code implementations8 Dec 2019 Guangxia Lia, Yulong Shena, Peilin Zhaob, Xiao Lu, Jia Liu, Yangyang Liu, Steven C. H. Hoi

Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information.

Continuous Control Intrusion Detection

FDDWNet: A Lightweight Convolutional Neural Network for Real-time Sementic Segmentation

no code implementations2 Nov 2019 Jia Liu, Quan Zhou, Yong Qiang, Bin Kang, Xiaofu Wu, Baoyu Zheng

The comprehensive experiments demonstrate that our model achieves state-of-the-art results in terms of available speed and accuracy trade-off on CityScapes and CamVid datasets.

Semantic Segmentation

Hierarchical Feature-Aware Tracking

no code implementations13 Oct 2019 Wenhua Zhang, Licheng Jiao, Jia Liu

Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated.

Visual Tracking

Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach

no code implementations10 Sep 2019 Haibo Yang, Xin Zhang, Minghong Fang, Jia Liu

In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process.

Urban flows prediction from spatial-temporal data using machine learning: A survey

no code implementations26 Aug 2019 Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang

Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc.

Transfer Learning

Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach

no code implementations28 May 2019 Xin Zhang, Jia Liu, Zhengyuan Zhu

In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network.

Nucleus Neural Network: A Data-driven Self-organized Architecture

no code implementations8 Apr 2019 Jia Liu, Maoguo Gong, Haibo He

In this paper, we propose a nucleus neural network (NNN) and corresponding connecting architecture learning method.

Combinatorial Sleeping Bandits with Fairness Constraints

no code implementations15 Jan 2019 Fengjiao Li, Jia Liu, Bo Ji

To tackle this new problem, we extend an online learning algorithm, UCB, to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints.


Exploring $k$ out of Top $ρ$ Fraction of Arms in Stochastic Bandits

no code implementations28 Oct 2018 Wenbo Ren, Jia Liu, Ness Shroff

Results in this paper provide up to $\rho n/k$ reductions compared with the "$k$-exploration" algorithms that focus on finding the (PAC) best $k$ arms out of $n$ arms.

Poisoning Attacks to Graph-Based Recommender Systems

no code implementations11 Sep 2018 Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, Jia Liu

To address the challenge, we formulate the poisoning attacks as an optimization problem, solving which determines the rating scores for the fake users.

Recommendation Systems

Multiobjective Optimization Training of PLDA for Speaker Verification

2 code implementations25 Aug 2018 Liang He, Xianhong Chen, Can Xu, Jia Liu

Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.

Multiobjective Optimization Text-Independent Speaker Verification

PAC Ranking from Pairwise and Listwise Queries: Lower Bounds and Upper Bounds

no code implementations8 Jun 2018 Wenbo Ren, Jia Liu, Ness B. Shroff

For the PAC top-$k$ ranking problem, we derive a lower bound on the sample complexity (aka number of queries), and propose an algorithm that is sample-complexity-optimal up to an $O(\log(k+l)/\log{k})$ factor.

Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning

no code implementations24 May 2018 Xin Zhang, Jia Liu, Zhengyuan Zhu

Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning.

Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time

no code implementations23 May 2018 Hejian Sang, Jia Liu

In this paper, we propose a new adaptive stochastic gradient Langevin dynamics (ASGLD) algorithmic framework and its two specialized versions, namely adaptive stochastic gradient (ASG) and adaptive gradient Langevin dynamics(AGLD), for non-convex optimization problems.

Generative Steganography by Sampling

no code implementations26 Apr 2018 Jia Liu, Yu Lei, Yan Ke, Jun Li, Min-qing Zhang, Xiaoyuan Yan

In this paper, a new data-driven information hiding scheme called generative steganography by sampling (GSS) is proposed.

Image Inpainting

Coverless Information Hiding Based on Generative adversarial networks

no code implementations18 Dec 2017 Ming-ming Liu, Min-qing Zhang, Jia Liu, Ying-nan Zhang, Yan Ke

The main idea of the method is that the class label of generative adversarial networks is replaced with the secret information as a driver to generate hidden image directly, and then extract the secret information from the hidden image through the discriminator.

Cryptography and Security Multimedia

Generative Steganography with Kerckhoffs' Principle

no code implementations14 Nov 2017 Yan Ke, Min-qing Zhang, Jia Liu, Tingting Su, Xiaoyuan Yang

The secret messages can be outputted by the generator if and only if the extraction key and the cover image are both inputted.


Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification

no code implementations14 Jul 2017 Yi Liu, Liang He, Yao Tian, Zhuzi Chen, Jia Liu, Michael T. Johnson

Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.

Speaker Identification Speaker Recognition +2

Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

1 code implementation8 May 2017 Juntao Gao, Yulong Shen, Jia Liu, Minoru Ito, Norio Shiratori

Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion.

Networking and Internet Architecture

Lepton Jets from Radiating Dark Matter

1 code implementation27 May 2015 Malte Buschmann, Joachim Kopp, Jia Liu, Pedro A. N. Machado

In this paper, we discuss lepton jets as a promising signature of an extended dark sector.

High Energy Physics - Phenomenology

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