
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Graph embedding, aiming to learn lowdimensional representations (aka. e...
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VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments
Motivated by the rising abundance of observational data with continuous ...
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Nonasymptotic Confidence Intervals of Offpolicy Evaluation: Primal and Dual Bounds
Offpolicy evaluation (OPE) is the task of estimating the expected rewar...
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Deep Graph Structure Learning for Robust Representations: A Survey
Graph Neural Networks (GNNs) are widely used for analyzing graphstructu...
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DNN2LR: Automatic Feature Crossing for Credit Scoring
Credit scoring is a major application of machine learning for financial ...
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Centroid Transformers: Learning to Abstract with Attention
Selfattention, as the key block of transformers, is a powerful mechanis...
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Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
We propose firefly neural architecture descent, a general framework for ...
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AlphaNet: Improved Training of Supernet with AlphaDivergence
Weightsharing neural architecture search (NAS) is an effective techniqu...
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STAN: SpatioTemporal Attention Network for Next Location Recommendation
The next location recommendation is at the core of various locationbase...
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Disentangled SelfAttentive Neural Networks for ClickThrough Rate Prediction
Clickthrough rate (CTR) prediction, which aims to predict the probabili...
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LiveMap: RealTime Dynamic Map in Automotive Edge Computing
Autonomous driving needs various lineofsight sensors to perceive surro...
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AlphaMatch: Improving Consistency for Semisupervised Learning with Alphadivergence
Semisupervised learning (SSL) is a key approach toward more dataeffici...
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KeepAugment: A Simple InformationPreserving Data Augmentation Approach
Data augmentation (DA) is an essential technique for training stateoft...
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Smart obervation method with wide field small aperture telescopes for real time transient detection
Wide field small aperture telescopes (WFSATs) are commonly used for fast...
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Modelling the Point Spread Function of Wide Field Small Aperture Telescopes With Deep Neural Networks – Applications in Point Spread Function Estimation
The point spread function (PSF) reflects states of a telescope and plays...
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Certified Monotonic Neural Networks
Learning monotonic models with respect to a subset of the inputs is a de...
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Debiasing Convolutional Neural Networks via Meta Orthogonalization
While deep learning models often achieve strong task performance, their ...
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Deep Active Graph Representation Learning
Graph neural networks (GNNs) aim to learn graph representations that pre...
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Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough
Despite the great success of deep learning, recent works show that large...
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OffPolicy Interval Estimation with Lipschitz Value Iteration
Offpolicy evaluation provides an essential tool for evaluating the effe...
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Graph Contrastive Learning with Adaptive Augmentation
Recently, contrastive learning (CL) has emerged as a successful method f...
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Population Gradients improve performance across datasets and architectures in object classification
The most successful methods such as ReLU transfer functions, batch norma...
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Adaptive DensetoSparse Paradigm for Pruning Online Recommendation System with NonStationary Data
Large scale deep learning provides a tremendous opportunity to improve t...
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An efficient representation of chronological events in medical texts
In this work we addressed the problem of capturing sequential informatio...
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Modeling Dyadic Conversations for Personality Inference
Nowadays, automatical personality inference is drawing extensive attenti...
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DNN2LR: Interpretationinspired Feature Crossing for Realworld Tabular Data
For sake of reliability, it is necessary for models in realworld applic...
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DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
Network slicing enables multiple virtual networks run on the same physic...
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Disentangled Item Representation for Recommender Systems
Item representations in recommendation systems are expected to reveal th...
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Accountable OffPolicy Evaluation With Kernel Bellman Statistics
We consider offpolicy evaluation (OPE), which evaluates the performance...
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Deep Active Learning by Model Interpretability
Recent successes of Deep Neural Networks (DNNs) in a variety of research...
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Edgeworth corrections for spot volatility estimator
We develop Edgeworth expansion theory for spot volatility estimator unde...
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Simplification of Graph Convolutional Networks: A Matrix Factorizationbased Perspective
In recent years, substantial progress has been made on Graph Convolution...
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TFNet: MultiSemantic Feature Interaction for CTR Prediction
The CTR (ClickThrough Rate) prediction plays a central role in the doma...
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Deep Graph Contrastive Representation Learning
Graph representation learning nowadays becomes fundamental in analyzing ...
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SAFER: A Structurefree Approach for Certified Robustness to Adversarial Word Substitutions
Stateoftheart NLP models can often be fooled by humanunaware transfo...
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TAGNN: Target Attentive Graph Neural Networks for Sessionbased Recommendation
Sessionbased recommendation nowadays plays a vital role in many website...
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An Empirical Study on Feature Discretization
When dealing with continuous numeric features, we usually adopt feature ...
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EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
5G and edge computing will serve various emerging use cases that have di...
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Dimension Independent Generalization Error with Regularized Online Optimization
One classical canon of statistics is that large models are prone to over...
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Blackbox Offpolicy Estimation for InfiniteHorizon Reinforcement Learning
Offpolicy estimation for longhorizon problems is important in many rea...
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Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting
We propose signed splitting steepest descent (S3D), which progressively ...
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Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection
Recent empirical works show that large deep neural networks are often hi...
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Med7: a transferable clinical natural language processing model for electronic health records
The field of clinical natural language processing has been advanced sign...
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Stein Variational Inference for Discrete Distributions
Gradientbased approximate inference methods, such as Stein variational ...
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Statistical Adaptive Stochastic Gradient Methods
We propose a statistical adaptive procedure called SALSA for automatical...
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Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
Wide field small aperture telescopes are widely used in optical transien...
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BlackBox Certification with Randomized Smoothing: A Functional Optimization Based Framework
Randomized classifiers have been shown to provide a promising approach f...
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Stein SelfRepulsive Dynamics: Benefits From Past Samples
We propose a new Stein selfrepulsive dynamics for obtaining diversified...
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Posttraining Quantization with Multiple Points: Mixed Precision without Mixed Precision
We consider the posttraining quantization problem, which discretizes th...
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MaxUp: A Simple Way to Improve Generalization of Neural Network Training
We propose MaxUp, an embarrassingly simple, highly effective technique f...
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Qiang Liu
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Assistant Professor at Department of Computer Science, The University of Texas at Austin