
Learning SampleSpecific Models with LowRank Personalized Regression
Modern applications of machine learning (ML) deal with increasingly hete...
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Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
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Learning Data Manipulation for Augmentation and Weighting
Manipulating data, such as weighting data examples or augmenting with ne...
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Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
Despite success on a wide range of problems related to vision, generativ...
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Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO), refers to a suite of techniques for global o...
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Stackelberg GAN: Towards Provable Minimax Equilibrium via MultiGenerator Architectures
We study the problem of alleviating the instability issue in the GAN tra...
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Knowledgedriven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Generating long and semanticcoherent reports to describe medical images...
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Theoretically Principled Tradeoff between Robustness and Accuracy
We identify a tradeoff between robustness and accuracy that serves as a...
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Learning Robust Global Representations by Penalizing Local Predictive Power
Despite their renowned predictive power on i.i.d. data, convolutional ne...
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Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest...
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Toward Understanding the Impact of Staleness in Distributed Machine Learning
Many distributed machine learning (ML) systems adopt the nonsynchronous...
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Adversarial Domain Adaptation Being Aware of Class Relationships
Adversarial training is a useful approach to promote the learning of tra...
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Learning LessOverlapping Representations
In representation learning (RL), how to make the learned representations...
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DiversityPromoting Bayesian Learning of Latent Variable Models
To address three important issues involved in latent variable models (LV...
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Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning
A primary goal of computational phenotype research is to conduct medical...
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A Sparse GraphStructured Lasso Mixed Model for Genetic Association with Confounding Correction
While linear mixed model (LMM) has shown a competitive performance in co...
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Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designa...
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Towards Visual Explanations for Convolutional Neural Networks via Input Resampling
The predictive power of neural networks often costs model interpretabili...
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On Unifying Deep Generative Models
Deep generative models have achieved impressive success in recent years....
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Generative Semantic Manipulation with Contrasting GAN
Generative Adversarial Networks (GANs) have recently achieved significan...
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Dual Motion GAN for FutureFlow Embedded Video Prediction
Future frame prediction in videos is a promising avenue for unsupervised...
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Contextual Explanation Networks
We introduce contextual explanation networks (CENs)a class of models ...
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SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data
Understanding how brain functions has been an intriguing topic for years...
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Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
Deep learning models can take weeks to train on a single GPUequipped ma...
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Stochastic Variational Deep Kernel Learning
Deep kernel learning combines the nonparametric flexibility of kernel m...
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Learning Scalable Deep Kernels with Recurrent Structure
Many applications in speech, robotics, finance, and biology deal with se...
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SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest Xrays
Chest Xray (CXR) is one of the most commonly prescribed medical imaging...
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Recurrent TopicTransition GAN for Visual Paragraph Generation
A natural image usually conveys rich semantic content and can be viewed ...
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Interpretable StructureEvolving LSTM
This paper develops a general framework for learning interpretable data ...
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Deep Variationstructured Reinforcement Learning for Visual Relationship and Attribute Detection
Despite progress in visual perception tasks such as image classification...
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Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Recently, there has been a surge of interest in using spectral methods f...
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State Space LSTM Models with Particle MCMC Inference
Long ShortTerm Memory (LSTM) is one of the most powerful sequence model...
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Strategies and Principles of Distributed Machine Learning on Big Data
The rise of Big Data has led to new demands for Machine Learning (ML) sy...
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Scalable Modeling of Conversationalrole based Selfpresentation Characteristics in Large Online Forums
Online discussion forums are complex webs of overlapping subcommunities ...
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Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting
We propose a distributed approach to train deep neural networks (DNNs), ...
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Deep Kernel Learning
We introduce scalable deep kernels, which combine the structural propert...
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The Human Kernel
Bayesian nonparametric models, such as Gaussian processes, provide a com...
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Bayesian Nonparametric KernelLearning
Kernel methods are ubiquitous tools in machine learning. They have prove...
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Alternating Directions Dual Decomposition
We propose AD3, a new algorithm for approximate maximum a posteriori (MA...
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LightLDA: Big Topic Models on Modest Compute Clusters
When building largescale machine learning (ML) programs, such as big to...
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ModelParallel Inference for Big Topic Models
In real world industrial applications of topic modeling, the ability to ...
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HighPerformance Distributed ML at Scale through Parameter Server Consistency Models
As Machine Learning (ML) applications increase in data size and model co...
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Screening Rules for Overlapping Group Lasso
Recently, to solve largescale lasso and group lasso problems, screening...
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Parallel and Distributed BlockCoordinate FrankWolfe Algorithms
We develop parallel and distributed FrankWolfe algorithms; the former o...
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TopicViz: Semantic Navigation of Document Collections
When people explore and manage information, they think in terms of topic...
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Consistent BoundedAsynchronous Parameter Servers for Distributed ML
In distributed ML applications, shared parameters are usually replicated...
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Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advance...
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Language Modeling with Power Low Rank Ensembles
We present power low rank ensembles (PLRE), a flexible framework for ng...
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StructureAware Dynamic Scheduler for Parallel Machine Learning
Training large machine learning (ML) models with many variables or param...
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Eric P Xing
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Professor of Machine Learning, Language Technology, Computer Science, Cargenie Mellon University