Search Results for author: Qiang Liu

Found 172 papers, 49 papers with code

Network Pruning by Greedy Subnetwork Selection

no code implementations ICML 2020 Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

Theoretically, we show that the small networks pruned using our method achieve provably lower loss than small networks trained from scratch with the same size.

Network Pruning

Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics

no code implementations ICML 2020 Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu

In this work, we investigate the statistical properties of the kernel loss, which allows us to find a feasible set that contains the true value function with high probability.

reinforcement-learning

A Chance-Constrained Generative Framework for Sequence Optimization

no code implementations ICML 2020 Xianggen Liu, Jian Peng, Qiang Liu, Sen Song

Deep generative modeling has achieved many successes for continuous data generation, such as producing realistic images and controlling their properties (e. g., styles).

Continual Learning and Private Unlearning

no code implementations24 Mar 2022 Bo Liu, Qiang Liu, Peter Stone

As intelligent agents become autonomous over longer periods of time, they may eventually become lifelong counterparts to specific people.

Continual Learning

WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition

no code implementations14 Mar 2022 Renjie Zhou, Qiang Hu, Jian Wan, Jilin Zhang, Qiang Liu, Tianxiang Hu, Jianjun Li

The model first trains the sentence pairs in the text, calculate similarity between words in sentence pairs by cosine similarity, and fine-tunes the BERT model used for the named entity recognition task through the similarity, so as to alleviate word ambiguity.

Contrastive Learning Knowledge Graphs +1

A Survey on Deep Graph Generation: Methods and Applications

no code implementations13 Mar 2022 Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu

In this paper, we conduct a comprehensive review on the existing literature of graph generation from a variety of emerging methods to its wide application areas.

Graph Generation Graph Learning

A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images

no code implementations27 Feb 2022 Junzheng Wu, Ruigang Fu, Qiang Liu, Weiping Ni, Kenan Cheng, Biao Li, Yuli Sun

To address this limitation, a dual neighborhood hypergraph neural network is proposed in this article, which combines the multiscale superpixel segmentation and hypergraph convolution to model and exploit the complex relationships.

Change Detection

How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity

no code implementations16 Feb 2022 Chengyue Gong, Lemeng Wu, Qiang Liu

Although traditional optimization methods focus on finding a single optimal solution, most objective functions in modern machine learning problems, especially those in deep learning, often have multiple or infinite numbers of optima.

Frame Text to image generation +1

EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing

no code implementations20 Jan 2022 Qiang Liu, Yuru Zhang, Haoxin Wang

High definition (HD) map needs to be updated frequently to capture road changes, which is constrained by limited specialized collection vehicles.

Edge-computing

Evidence-aware Fake News Detection with Graph Neural Networks

1 code implementation18 Jan 2022 Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, Liang Wang

In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i. e., a claim).

Fake News Detection Graph structure learning

Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning

no code implementations1 Jan 2022 Ziyang Tang, Yihao Feng, Qiang Liu

The benefit of learning the operator is that we can incorporate any new reward function as input and attain its corresponding value function in a zero-shot manner.

Q-Learning reinforcement-learning

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

1 code implementation30 Dec 2021 Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.

Network Compression via Central Filter

1 code implementation10 Dec 2021 Yuanzhi Duan, Xiaofang Hu, Yue Zhou, Qiang Liu, Shukai Duan

In this paper, by exploring the similarities between feature maps, we propose a novel filter pruning method, Central Filter (CF), which suggests that a filter is approximately equal to a set of other filters after appropriate adjustments.

Network Pruning

FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization

1 code implementation2 Dec 2021 Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu

We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf image generator (GANs), optimizing in the latent space of GAN to find images that achieve maximum CLIP score with the given input text.

Text to image generation Zero-Shot Text-to-Image Generation

Sampling with Trusthworthy Constraints: A Variational Gradient Framework

1 code implementation NeurIPS 2021 Xingchao Liu, Xin Tong, Qiang Liu

In this work, we propose a family of constrained sampling algorithms which generalize Langevin Dynamics (LD) and Stein Variational Gradient Descent (SVGD) to incorporate a moment constraint specified by a general nonlinear function.

Bayesian Inference Fairness

argmax centroid

no code implementations NeurIPS 2021 Chengyue Gong, Mao Ye, Qiang Liu

We propose a general method to construct centroid approximation for the distribution of maximum points of a random function (a. k. a.

Domain Adaptation Few-Shot Image Classification +2

Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent

1 code implementation NeurIPS 2021 Xingchao Liu, Xin Tong, Qiang Liu

Finding diverse and representative Pareto solutions from the Pareto front is a key challenge in multi-objective optimization (MOO).

Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach

no code implementations NeurIPS 2021 Chengyue Gong, Xingchao Liu, Qiang Liu

In this work, we consider constrained optimization as a more principled approach for trading off two losses, with a special emphasis on lexicographic optimization, a degenerated limit of constrained optimization which optimizes a secondary loss inside the optimal set of the main loss.

OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning

no code implementations2 Nov 2021 Qiang Liu, Nakjung Choi, Tao Han

As online learning is converged, OnSlicing reduces 12. 5% usage without any violations as compared to the state-of-the-art online DRL solution.

online learning reinforcement-learning

Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation

1 code implementation1 Nov 2021 Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang

Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships. To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity).

Collaborative Filtering

Conflict-Averse Gradient Descent for Multi-task Learning

2 code implementations NeurIPS 2021 Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu

The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks.

Multi-Task Learning

Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set

no code implementations17 Oct 2021 Mao Ye, Qiang Liu

The notion of the Pareto set allows us to focus on the set of (often infinite number of) models that cannot be strictly improved.

Multi-Task Learning

Centroid Approximation for Bootstrap

no code implementations17 Oct 2021 Mao Ye, Qiang Liu

In this work, we propose an efficient method to explicitly \emph{optimize} a small set of high quality "centroid" points to better approximate the ideal bootstrap distribution.

Exciting-Inhibition Network for Person Reidentification in Internet of Things

no code implementations IEEE Internet of Things Journal 2021 Meixia Fu, Songlin Sun, Qilian Liang, Xiaoyun Tong, Qiang Liu

Index Terms—Channel-spatial attention block (CSAB), exciting-inhibition network (EINet), Internet of Things (IoT), person reidentification (re-ID), soft batch dropblock.

Person Re-Identification

Relation-aware Heterogeneous Graph for User Profiling

1 code implementation14 Oct 2021 Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu, Liang Wang

User profiling has long been an important problem that investigates user interests in many real applications.

Node Classification

Speeding up Deep Model Training by Sharing Weights and Then Unsharing

no code implementations8 Oct 2021 Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou

Our approach exploits the special structure of BERT that contains a stack of repeated modules (i. e., transformer encoders).

Pareto Navigation Gradient Descent: a First Order Algorithm for Optimization in Pareto Set

no code implementations29 Sep 2021 Mao Ye, Qiang Liu

The notion of the Pareto set allows us to focus on the set of (often infinite number of) models that cannot be strictly improved.

Multi-Task Learning

NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training

no code implementations ICLR 2022 Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra

In this work, we observe that the poor performance is due to a gradient conflict issue: the gradients of different sub-networks conflict with that of the supernet more severely in ViTs than CNNs, which leads to early saturation in training and inferior convergence.

Data Augmentation Neural Architecture Search +1

Neural Energy Minimization for Molecular Conformation Optimization

no code implementations ICLR 2022 Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng

Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner.

An Empirical Study of Graph Contrastive Learning

1 code implementation2 Sep 2021 Yanqiao Zhu, Yichen Xu, Qiang Liu, Shu Wu

We envision this work to provide useful empirical evidence of effective GCL algorithms and offer several insights for future research.

Graph Classification Node Classification

Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning

no code implementations31 Aug 2021 Yanqiao Zhu, Yichen Xu, Hejie Cui, Carl Yang, Qiang Liu, Shu Wu

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data.

Contrastive Learning

Deep Active Learning for Text Classification with Diverse Interpretations

no code implementations15 Aug 2021 Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang, Shu Wu

To train high-performing models with the minimal annotation cost, active learning is proposed to select and label the most informative samples, yet it is still challenging to measure informativeness of samples used in DNNs.

Active Learning Classification +2

Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness

no code implementations29 Jul 2021 Runzhou Ge, Zhuangzhuang Ding, Yihan Hu, Wenxin Shao, Li Huang, Kun Li, Qiang Liu

Extended from our last year's award-winning model AFDet, we have made a handful of modifications to the base model, to improve the accuracy and at the same time to greatly reduce the latency.

Data Augmentation

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

no code implementations28 Jun 2021 Peng Jia, Yongyang Sun, Zhimin Yang, Rui Sun, Qiang Liu

Wide field small aperture telescopes (WFSATs) are preferable observation instruments for time domain astronomy, because they could obtain images of celestial objects with high cadence in a cost-effective way.

Transfer Learning

MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training

no code implementations CVPR 2021 Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu

The idea is to generate a set of augmented data with some random perturbations or transforms, and minimize the maximum, or worst case loss over the augmented data.

Data Augmentation Image Classification +1

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

no code implementations9 Jun 2021 Yuntian Chen, Yingtao Luo, Qiang Liu, Hao Xu, Dongxiao Zhang

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses.

KO-PDE: Kernel Optimized Discovery of Partial Differential Equations with Varying Coefficients

no code implementations2 Jun 2021 Yingtao Luo, Qiang Liu, Yuntian Chen, WenBo Hu, Jun Zhu

Previous algorithms can discover some simple instances of PDEs-VC but fail in the discovery of PDEs with coefficients of higher complexity, as a result of coefficient estimation inaccuracy.

Density Estimation

Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach

no code implementations NeurIPS 2021 Chengyue Gong, Xingchao Liu, Qiang Liu

In this work, we consider constrained optimization as a more principled approach for trading off two losses, with a special emphasis on lexicographic optimization, a degenerated limit of constrained optimization which optimizes a secondary loss inside the optimal set of the main loss.

Sampling with Trusthworthy Constraints: A Variational Gradient Framework

1 code implementation NeurIPS 2021 Xingchao Liu, Xin Tong, Qiang Liu

In this work, we propose a family of constrained sampling algorithms which generalize Langevin Dynamics (LD) and Stein Variational Gradient Descent (SVGD) to incorporate a moment constraint specified by a general nonlinear function.

Bayesian Inference Fairness

Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent

1 code implementation NeurIPS 2021 Xingchao Liu, Xin Tong, Qiang Liu

Finding diverse and representative Pareto solutions from the Pareto front is a key challenge in multi-objective optimization (MOO).

Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition

1 code implementation18 May 2021 Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar

Specifically, we 1) adopt the attention mechanism for both the coach and the players; 2) propose a variational objective to regularize learning; and 3) design an adaptive communication method to let the coach decide when to communicate with the players.

Multi-agent Reinforcement Learning reinforcement-learning +1

Vision Transformers with Patch Diversification

1 code implementation26 Apr 2021 Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu

To alleviate this problem, in this work, we introduce novel loss functions in vision transformer training to explicitly encourage diversity across patch representations for more discriminative feature extraction.

Image Classification Semantic Segmentation

Mining Latent Structures for Multimedia Recommendation

1 code implementation19 Apr 2021 Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, Liang Wang

To be specific, in the proposed LATTICE model, we devise a novel modality-aware structure learning layer, which learns item-item structures for each modality and aggregates multiple modalities to obtain latent item graphs.

Collaborative Filtering Recommendation Systems

Dynamic Graph Neural Networks for Sequential Recommendation

no code implementations15 Apr 2021 Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang

We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.

Graph Attention Link Prediction +1

DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

no code implementations7 Apr 2021 Zeyu Cui, Zekun Li, Shu Wu, XiaoYu Zhang, Qiang Liu, Liang Wang, Mengmeng Ai

We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings.

Dynamic graph embedding

VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

1 code implementation14 Mar 2021 Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae

Motivated by the rising abundance of observational data with continuous treatments, we investigate the problem of estimating the average dose-response curve (ADRF).

Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds

no code implementations ICLR 2021 Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu

Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies.

A Survey on Graph Structure Learning: Progress and Opportunities

no code implementations4 Mar 2021 Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu

Specifically, we first formulate a general pipeline of GSL and review state-of-the-art methods classified by the way of modeling graph structures, followed by applications of GSL across domains.

Graph structure learning

DNN2LR: Automatic Feature Crossing for Credit Scoring

no code implementations24 Feb 2021 Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen, Jun Zhu

Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR.

Feature Engineering

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

1 code implementation NeurIPS 2020 Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu

We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and architectures.

Continual Learning Image Classification +1

Centroid Transformers: Learning to Abstract with Attention

no code implementations17 Feb 2021 Lemeng Wu, Xingchao Liu, Qiang Liu

Self-attention, as the key block of transformers, is a powerful mechanism for extracting features from the inputs.

Abstractive Text Summarization Image Classification

AlphaNet: Improved Training of Supernets with Alpha-Divergence

2 code implementations16 Feb 2021 Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra

Weight-sharing NAS builds a supernet that assembles all the architectures as its sub-networks and jointly trains the supernet with the sub-networks.

Neural Architecture Search

STAN: Spatio-Temporal Attention Network for Next Location Recommendation

2 code implementations8 Feb 2021 Yingtao Luo, Qiang Liu, Zhaocheng Liu

The next location recommendation is at the core of various location-based applications.

Coherent ray-wave structured light based on (helical) Ince-Gaussian modes

no code implementations4 Feb 2021 Zhaoyang Wang, Yijie Shen, Qiang Liu, Xing Fu

The topological evolution of classic eigenmodes including Hermite-Laguerre-Gaussian and (helical) InceGaussian modes is exploited to construct coherent state modes, which unifies the representations of travelingwave (TW) and standing-wave (SW) ray-wave structured light for the first time and realizes the TW-SW unified ray-wave geometric beam with topology of raytrajectories splitting effect, breaking the boundary of TW and SW structured light.

Optics

Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction

2 code implementations11 Jan 2021 Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu, Shu Wu

To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.

Click-Through Rate Prediction Feature Importance

Varying Coefficient Neural Network with Functional Targeted Regularization for Estimating Continuous Treatment Effects

no code implementations ICLR 2021 Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae

With the rising abundance of observational data with continuous treatments, we investigate the problem of estimating average dose-response curve (ADRF).

Fast Training of Contrastive Learning with Intermediate Contrastive Loss

no code implementations1 Jan 2021 Chengyue Gong, Xingchao Liu, Qiang Liu

We apply our method to recently-proposed MOCO, SimCLR, SwAV and notice that we can reduce the computational cost with little loss on the performance of ImageNet linear classification and other downstream tasks.

Contrastive Learning

Speeding up Deep Learning Training by Sharing Weights and Then Unsharing

no code implementations1 Jan 2021 Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou

It has been widely observed that increasing deep learning model sizes often leads to significant performance improvements on a variety of natural language processing and computer vision tasks.

A Coach-Player Framework for Dynamic Team Composition

no code implementations1 Jan 2021 Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar

The performance of our method is comparable or even better than the setting where all players have a full view of the environment, but no coach.

LiveMap: Real-Time Dynamic Map in Automotive Edge Computing

no code implementations16 Dec 2020 Qiang Liu, Tao Han, Jiang, Xie, BaekGyu Kim

In this paper, we propose LiveMap, a real-time dynamic map, that detects, matches, and tracks objects on the road with crowdsourcing data from connected vehicles in sub-second.

Autonomous Driving Edge-computing +1

Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines

no code implementations2 Dec 2020 Yiming Gan, Yu Bo, Boyuan Tian, Leimeng Xu, Wei Hu, Shaoshan Liu, Qiang Liu, Yanjun Zhang, Jie Tang, Yuhao Zhu

We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe.

Self-Driving Cars Hardware Architecture

AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence

no code implementations CVPR 2021 Chengyue Gong, Dilin Wang, Qiang Liu

Semi-supervised learning (SSL) is a key approach toward more data-efficient machine learning by jointly leverage both labeled and unlabeled data.

Certified Monotonic Neural Networks

1 code implementation NeurIPS 2020 Xingchao Liu, Xing Han, Na Zhang, Qiang Liu

In this work, we propose to certify the monotonicity of the general piece-wise linear neural networks by solving a mixed integer linear programming problem. This provides a new general approach for learning monotonic neural networks with arbitrary model structures.

Fairness

Smart obervation method with wide field small aperture telescopes for real time transient detection

no code implementations20 Nov 2020 Peng Jia, Qiang Liu, Yongyang Sun, Yitian Zheng, Wenbo Liu, Yifei Zhao

The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets.

Ensemble Learning

Debiasing Convolutional Neural Networks via Meta Orthogonalization

1 code implementation15 Nov 2020 Kurtis Evan David, Qiang Liu, Ruth Fong

While deep learning models often achieve strong task performance, their successes are hampered by their inability to disentangle spurious correlations from causative factors, such as when they use protected attributes (e. g., race, gender, etc.)

Word Embeddings

When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision

no code implementations30 Oct 2020 Yanqiao Zhu, Weizhi Xu, Qiang Liu, Shu Wu

To this end, we present a minimax selection scheme that explicitly harnesses neighborhood information and discover homophilous subgraphs to facilitate active selection.

Active Learning Contrastive Learning +2

Off-Policy Interval Estimation with Lipschitz Value Iteration

no code implementations NeurIPS 2020 Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu

Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data.

Decision Making Medical Diagnosis

Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough

1 code implementation NeurIPS 2020 Mao Ye, Lemeng Wu, Qiang Liu

Despite the great success of deep learning, recent works show that large deep neural networks are often highly redundant and can be significantly reduced in size.

Graph Contrastive Learning with Adaptive Augmentation

1 code implementation27 Oct 2020 Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang

On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information.

Contrastive Learning Data Augmentation +2

Population Gradients improve performance across data-sets and architectures in object classification

no code implementations23 Oct 2020 Yurika Sakai, Andrey Kormilitzin, Qiang Liu, Alejo Nevado-Holgado

The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations.

General Classification

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data

no code implementations16 Oct 2020 Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal

To the best of our knowledge, this is the first work to provide in-depth analysis and discussion of applying pruning to online recommendation systems with non-stationary data distribution.

Recommendation Systems

An efficient representation of chronological events in medical texts

no code implementations EMNLP (Louhi) 2020 Andrey Kormilitzin, Nemanja Vaci, Qiang Liu, Hao Ni, Goran Nenadic, Alejo Nevado-Holgado

In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).

Modeling Dyadic Conversations for Personality Inference

no code implementations26 Sep 2020 Qiang Liu

We adjust the formulation of each layer of a conventional GRU with sequence to sequence learning and personal information of both sides of the conversation.

DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data

no code implementations22 Aug 2020 Zhaocheng Liu, Qiang Liu, Haoli Zhang, Yuntian Chen

Simple classifiers, e. g., Logistic Regression (LR), are globally interpretable, but not powerful enough to model complex nonlinear interactions among features in tabular data.

Disentangled Item Representation for Recommender Systems

no code implementations17 Aug 2020 Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu, Liang Wang

In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation.

Recommendation Systems

DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing

no code implementations17 Aug 2020 Qiang Liu, Tao Han, Ning Zhang, Ye Wang

Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond.

reinforcement-learning

Accountable Off-Policy Evaluation With Kernel Bellman Statistics

no code implementations15 Aug 2020 Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu

We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments, without requiring the execution of the new policy.

Medical Diagnosis Recommendation Systems

Deep Active Learning by Model Interpretability

no code implementations23 Jul 2020 Qiang Liu, Zhaocheng Liu, Xiaofang Zhu, Yeliang Xiu

In this paper, inspired by piece-wise linear interpretability in DNN, we introduce the linearly separable regions of samples to the problem of active learning, and propose a novel Deep Active learning approach by Model Interpretability (DAMI).

Active Learning Informativeness

Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective

no code implementations17 Jul 2020 Qiang Liu, Haoli Zhang, Zhaocheng Liu

Moreover, we have also conducted experiments on a typical task of graph embedding, i. e., community detection, and the proposed UCMF model outperforms several representative graph embedding models.

Community Detection Distributed Computing +2

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks

no code implementations ICML 2020 Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans

This is achieved by layerwise imitation, that is, forcing the thin network to mimic the intermediate outputs of the wide network from layer to layer.

Model Compression

TFNet: Multi-Semantic Feature Interaction for CTR Prediction

no code implementations29 Jun 2020 Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao, Fan Huang

The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.

Click-Through Rate Prediction Recommendation Systems

Deep Graph Contrastive Representation Learning

2 code implementations7 Jun 2020 Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang

Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.

Contrastive Learning Graph Representation Learning +1

SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions

1 code implementation ACL 2020 Mao Ye, Chengyue Gong, Qiang Liu

For security reasons, it is of critical importance to develop models with certified robustness that can provably guarantee that the prediction is can not be altered by any possible synonymous word substitution.

Text Classification

TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation

1 code implementation6 May 2020 Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.

Session-Based Recommendations

An Empirical Study on Feature Discretization

no code implementations27 Apr 2020 Qiang Liu, Zhaocheng Liu, Haoli Zhang

When dealing with continuous numeric features, we usually adopt feature discretization.

Dimension Independent Generalization Error by Stochastic Gradient Descent

no code implementations25 Mar 2020 Xi Chen, Qiang Liu, Xin T. Tong

One classical canon of statistics is that large models are prone to overfitting, and model selection procedures are necessary for high dimensional data.

Model Selection

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning

no code implementations ICLR 2020 Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou

Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and on-policy evaluation is expensive or impossible.

reinforcement-learning

Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting

no code implementations23 Mar 2020 Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu

Recently, Liu et al.[19] proposed a splitting steepest descent (S2D) method that jointly optimizes the neural parameters and architectures based on progressively growing network structures by splitting neurons into multiple copies in a steepest descent fashion.

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

1 code implementation3 Mar 2020 Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

This differs from the existing methods based on backward elimination, which remove redundant neurons from the large network.

Network Pruning

Stein Variational Inference for Discrete Distributions

no code implementations1 Mar 2020 Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu

In addition, such transform can be straightforwardly employed in gradient-free kernelized Stein discrepancy to perform goodness-of-fit (GOF) test on discrete distributions.

Variational Inference

Statistical Adaptive Stochastic Gradient Methods

1 code implementation25 Feb 2020 Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao

We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods.

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework

no code implementations NeurIPS 2020 Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning.

Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes

no code implementations21 Feb 2020 Peng Jia, Qiang Liu, Yongyang Sun

To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network.

General Classification Transfer Learning

Stein Self-Repulsive Dynamics: Benefits From Past Samples

1 code implementation NeurIPS 2020 Mao Ye, Tongzheng Ren, Qiang Liu

Our idea is to introduce Stein variational gradient as a repulsive force to push the samples of Langevin dynamics away from the past trajectories.

MaxUp: A Simple Way to Improve Generalization of Neural Network Training

1 code implementation20 Feb 2020 Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu

The idea is to generate a set of augmented data with some random perturbations or transforms and minimize the maximum, or worst case loss over the augmented data.

Few-Shot Image Classification General Classification +1

Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision

no code implementations20 Feb 2020 Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu

We propose multipoint quantization, a quantization method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers; this is in contrast to typical quantization methods that approximate each weight using a single low precision number.

Object Detection Quantization

Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation

1 code implementation7 Jan 2020 Di You, Nguyen Vo, Kyumin Lee, Qiang Liu

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e. g., Snopes. com and Politifact. com).

Fact Checking Graph Attention +1

Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions

1 code implementation1 Jan 2020 Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang

IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.

Click-Through Rate Prediction Feature Engineering

Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions

no code implementations CIKM 2020 Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu, Liang Wang

IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.

Click-Through Rate Prediction Feature Engineering

The Black-Scholes-Merton dual equation

no code implementations22 Dec 2019 Shuxin Guo, Qiang Liu

We derive the Black-Scholes-Merton dual equation, which has exactly the same form as the Black-Scholes-Merton equation.

Learning Preferences and Demands in Visual Recommendation

no code implementations11 Nov 2019 Qiang Liu, Shu Wu, Liang Wang

For modeling users' demands on different categories of items, the problem can be formulated as recommendation with contextual and sequential information.

Recommendation Systems

Stein Variational Gradient Descent With Matrix-Valued Kernels

1 code implementation NeurIPS 2019 Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu

Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference.

Bayesian Inference

Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

no code implementations ICLR 2020 Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu

Our method is doubly robust in that the bias vanishes when either the density ratio or the value function estimation is perfect.

Density Ratio Estimation

Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent

1 code implementation ICLR 2020 Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu

Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are highly limited.

Splitting Steepest Descent for Growing Neural Architectures

1 code implementation NeurIPS 2019 Qiang Liu, Lemeng Wu, Dilin Wang

We develop a progressive training approach for neural networks which adaptively grows the network structure by splitting existing neurons to multiple off-springs.

Statistical Adaptive Stochastic Optimization

no code implementations25 Sep 2019 Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao

We investigate statistical methods for automatically scheduling the learning rate (step size) in stochastic optimization.

Stochastic Optimization

Automatically Learning Feature Crossing from Model Interpretation for Tabular Data

no code implementations25 Sep 2019 Zhaocheng Liu, Qiang Liu, Haoli Zhang

Automatically feature generation is a major topic of automated machine learning.

Filling the Soap Bubbles: Efficient Black-Box Adversarial Certification with Non-Gaussian Smoothing

no code implementations25 Sep 2019 Dinghuai Zhang*, Mao Ye*, Chengyue Gong*, Zhanxing Zhu, Qiang Liu

Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning.

Training Robust Deep Neural Networks via Adversarial Noise Propagation

no code implementations19 Sep 2019 Aishan Liu, Xianglong Liu, Chongzhi Zhang, Hang Yu, Qiang Liu, DaCheng Tao

Various adversarial defense methods have accordingly been developed to improve adversarial robustness for deep models.

Adversarial Defense Adversarial Robustness

Learning Belief Representations for Imitation Learning in POMDPs

1 code implementation22 Jun 2019 Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng

We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs).

Continuous Control Imitation Learning +1

Exploration via Hindsight Goal Generation

1 code implementation NeurIPS 2019 Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng

Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space.

reinforcement-learning

Improving Neural Language Modeling via Adversarial Training

1 code implementation10 Jun 2019 Dilin Wang, Chengyue Gong, Qiang Liu

Theoretically, we show that our adversarial mechanism effectively encourages the diversity of the embedding vectors, helping to increase the robustness of models.

Language Modelling Machine Translation +1

Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning

1 code implementation31 May 2019 Yang Liu, Yunan Luo, Yuanyi Zhong, Xi Chen, Qiang Liu, Jian Peng

Recent advances in deep reinforcement learning algorithms have shown great potential and success for solving many challenging real-world problems, including Go game and robotic applications.

reinforcement-learning

A Kernel Loss for Solving the Bellman Equation

1 code implementation NeurIPS 2019 Yihao Feng, Lihong Li, Qiang Liu

Value function learning plays a central role in many state-of-the-art reinforcement-learning algorithms.

Q-Learning reinforcement-learning

On the Margin Theory of Feedforward Neural Networks

no code implementations ICLR 2019 Colin Wei, Jason Lee, Qiang Liu, Tengyu Ma

We establish: 1) for multi-layer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for deep networks.

Variational Inference with Tail-adaptive f-Divergence

1 code implementation NeurIPS 2018 Dilin Wang, Hao liu, Qiang Liu

Variational inference with {\alpha}-divergences has been widely used in modern probabilistic machine learning.

Variational Inference

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation

2 code implementations NeurIPS 2018 Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy.

Stein Variational Gradient Descent as Moment Matching

no code implementations NeurIPS 2018 Qiang Liu, Dilin Wang

Stein variational gradient descent (SVGD) is a non-parametric inference algorithm that evolves a set of particles to fit a given distribution of interest.

Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel

no code implementations NeurIPS 2019 Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma

We prove that for infinite-width two-layer nets, noisy gradient descent optimizes the regularized neural net loss to a global minimum in polynomial iterations.

Network localization is unalterable by infections in bursts

1 code implementation11 Oct 2018 Qiang Liu, Piet Van Mieghem

To shed light on the disease localization phenomenon, we study a bursty susceptible-infected-susceptible (SIS) model and analyze the model under the mean-field approximation.

Physics and Society Social and Information Networks

Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning

no code implementations27 Sep 2018 Yihao Feng, Hao liu, Jian Peng, Qiang Liu

Deep reinforcement learning has achieved remarkable successes in solving various challenging artificial intelligence tasks.

Continuous Control reinforcement-learning

Learning to Explore via Meta-Policy Gradient

no code implementations ICML 2018 Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy.

Continuous Control Q-Learning +1

Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy

no code implementations ICML 2018 Jiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville

Recent work has combined Stein’s method with reproducing kernel Hilbert space theory to develop nonparametric goodness-of-fit tests for un-normalized probability distributions.

Stein Variational Gradient Descent Without Gradient

no code implementations ICML 2018 Jun Han, Qiang Liu

Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions.

Learning Self-Imitating Diverse Policies

no code implementations ICLR 2019 Tanmay Gangwani, Qiang Liu, Jian Peng

Improving the efficiency of RL algorithms in real-world problems with sparse or episodic rewards is therefore a pressing need.

Continuous Control Decision Making +3

Learning to Explore with Meta-Policy Gradient

no code implementations13 Mar 2018 Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy.

Q-Learning reinforcement-learning

BEBP: An Poisoning Method Against Machine Learning Based IDSs

no code implementations11 Mar 2018 Pan Li, Qiang Liu, Wentao Zhao, Dongxu Wang, Siqi Wang

In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs.

Intrusion Detection

Adaptive Scan Gibbs Sampler for Large Scale Inference Problems

no code implementations27 Jan 2018 Vadim Smolyakov, Qiang Liu, John W. Fisher III

For large scale on-line inference problems the update strategy is critical for performance.

Stein Variational Message Passing for Continuous Graphical Models

no code implementations ICML 2018 Dilin Wang, Zhe Zeng, Qiang Liu

We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structure of the distribution of interest.

On the Discrimination-Generalization Tradeoff in GANs

no code implementations ICLR 2018 Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

When evaluated with neural distance, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set.

Generalization Bounds

Action-depedent Control Variates for Policy Optimization via Stein's Identity

2 code implementations30 Oct 2017 Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems.

Policy Gradient Methods reinforcement-learning

Efficient Localized Inference for Large Graphical Models

no code implementations28 Oct 2017 Jinglin Chen, Jian Peng, Qiang Liu

We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property.

Stochastic Variance Reduction for Policy Gradient Estimation

no code implementations17 Oct 2017 Tianbing Xu, Qiang Liu, Jian Peng

Recent advances in policy gradient methods and deep learning have demonstrated their applicability for complex reinforcement learning problems.

Continuous Control Policy Gradient Methods +1

Learning Infinite RBMs with Frank-Wolfe

no code implementations NeurIPS 2016 Wei Ping, Qiang Liu, Alexander Ihler

In this work, we propose an infinite restricted Boltzmann machine~(RBM), whose maximum likelihood estimation~(MLE) corresponds to a constrained convex optimization.

Energy-efficient Amortized Inference with Cascaded Deep Classifiers

no code implementations10 Oct 2017 Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng

Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing.

Image Classification

Learning to Draw Samples with Amortized Stein Variational Gradient Descent

no code implementations20 Jul 2017 Yihao Feng, Dilin Wang, Qiang Liu

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference.

Bayesian Inference

Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE

no code implementations4 Jul 2017 Qiang Liu, Dilin Wang

We propose a number of new algorithms for learning deep energy models and demonstrate their properties.

Stein Variational Gradient Descent as Gradient Flow

no code implementations NeurIPS 2017 Qiang Liu

Stein variational gradient descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate given distributions, based on an efficient gradient-based update that guarantees to optimally decrease the KL divergence within a function space.

Stein Variational Adaptive Importance Sampling

no code implementations18 Apr 2017 Jun Han, Qiang Liu

We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS).

Stein Variational Policy Gradient

no code implementations7 Apr 2017 Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng

Policy gradient methods have been successfully applied to many complex reinforcement learning problems.

Bayesian Inference Continuous Control +2

Approximate Inference with Amortised MCMC

no code implementations27 Feb 2017 Yingzhen Li, Richard E. Turner, Qiang Liu

We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler.

Two Methods For Wild Variational Inference

no code implementations30 Nov 2016 Qiang Liu, Yihao Feng

Variational inference provides a powerful tool for approximate probabilistic in- ference on complex, structured models.

Variational Inference

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

1 code implementation6 Nov 2016 Dilin Wang, Qiang Liu

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference.

Conditional Image Generation

Black-box Importance Sampling

no code implementations17 Oct 2016 Qiang Liu, Jason D. Lee

Importance sampling is widely used in machine learning and statistics, but its power is limited by the restriction of using simple proposals for which the importance weights can be tractably calculated.

ICE: Information Credibility Evaluation on Social Media via Representation Learning

no code implementations29 Sep 2016 Qiang Liu, Shu Wu, Feng Yu, Liang Wang, Tieniu Tan

In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media.

Feature Engineering Representation Learning

Context-aware Sequential Recommendation

no code implementations19 Sep 2016 Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li, Liang Wang

Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks.

Sequential Recommendation

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

9 code implementations NeurIPS 2016 Qiang Liu, Dilin Wang

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization.

Bayesian Inference Variational Inference

Bootstrap Model Aggregation for Distributed Statistical Learning

no code implementations NeurIPS 2016 Jun Han, Qiang Liu

In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation.

Local Perturb-and-MAP for Structured Prediction

no code implementations24 May 2016 Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi

In this work, we present a new Local Perturb-and-MAP (locPMAP) framework that replaces the global optimization with a local optimization by exploiting our observed connection between locPMAP and the pseudolikelihood of the original CRF model.

Combinatorial Optimization Structured Prediction

A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation

no code implementations10 Feb 2016 Qiang Liu, Jason D. Lee, Michael. I. Jordan

We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory.

Probabilistic Variational Bounds for Graphical Models

no code implementations NeurIPS 2015 Qiang Liu, John W. Fisher III, Alexander T. Ihler

We propose a simple Monte Carlo based inference method that augments convex variational bounds by adding importance sampling (IS).

Decomposition Bounds for Marginal MAP

no code implementations NeurIPS 2015 Wei Ping, Qiang Liu, Alexander Ihler

Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information.

Communication-efficient sparse regression: a one-shot approach

no code implementations14 Mar 2015 Jason D. Lee, Yuekai Sun, Qiang Liu, Jonathan E. Taylor

We devise a one-shot approach to distributed sparse regression in the high-dimensional setting.

A Convolutional Click Prediction Model

no code implementations CIKM 2015 Qiang Liu, Feng Yu, Shu Wu, Liang Wang

The explosion in online advertisement urges to better estimate the click prediction of ads.

A Convolutional Click Prediction Model

1 code implementation1 Jan 2015 Qiang Liu, Feng Yu, Shu Wu, Liang Wang

The explosion in online advertisement urges to better estimate the click prediction of ads.

Distributed Estimation, Information Loss and Exponential Families

no code implementations NeurIPS 2014 Qiang Liu, Alexander Ihler

Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important.

Marginal Structured SVM with Hidden Variables

no code implementations4 Sep 2014 Wei Ping, Qiang Liu, Alexander Ihler

In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables.

Structured Prediction

Variational Planning for Graph-based MDPs

no code implementations NeurIPS 2013 Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler

The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph.

Decision Making

Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?

no code implementations NeurIPS 2013 Qiang Liu, Alexander T. Ihler, Mark Steyvers

We study the problem of estimating continuous quantities, such as prices, probabilities, and point spreads, using a crowdsourcing approach.

Variational Algorithms for Marginal MAP

no code implementations26 Feb 2013 Qiang Liu, Alexander Ihler

The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining variables marginalized, is an important inference problem in many models, such as those with hidden variables or uncertain parameters.

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