Eric P Xing

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Petuum, Inc. ∙ Carnegie Mellon University

Professor of Machine Learning, Language Technology, Computer Science, Cargenie Mellon University

Learning Sample-Specific Models with Low-Rank Personalized Regression

Modern applications of machine learning (ML) deal with increasingly hete...
10/15/2019 ∙ by Benjamin Lengerich, et al. ∙ 37 ∙ share

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Distributed, partially collapsed MCMC for Bayesian Nonparametrics

Bayesian nonparametric (BNP) models provide elegant methods for discover...
01/15/2020 ∙ by Avinava Dubey, et al. ∙ 33 ∙ share

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Learning Data Manipulation for Augmentation and Weighting

Manipulating data, such as weighting data examples or augmenting with ne...
10/28/2019 ∙ by Zhiting Hu, et al. ∙ 30 ∙ share

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Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...
09/29/2019 ∙ by Xun Zheng, et al. ∙ 18 ∙ share

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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...
03/15/2019 ∙ by Kirthevasan Kandasamy, et al. ∙ 14 ∙ share

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Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures

We study the problem of alleviating the instability issue in the GAN tra...
11/19/2018 ∙ by Hongyang Zhang, et al. ∙ 8 ∙ share

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Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

Generating long and semantic-coherent reports to describe medical images...
03/25/2019 ∙ by Christy Y. Li, et al. ∙ 8 ∙ share

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Theoretically Principled Trade-off between Robustness and Accuracy

We identify a trade-off between robustness and accuracy that serves as a...
01/24/2019 ∙ by Hongyang Zhang, et al. ∙ 5 ∙ share

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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...
05/29/2019 ∙ by Haohan Wang, et al. ∙ 4 ∙ share

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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...
07/10/2018 ∙ by Nanqing Dong, et al. ∙ 2 ∙ share

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Toward Understanding the Impact of Staleness in Distributed Machine Learning

Many distributed machine learning (ML) systems adopt the non-synchronous...
10/08/2018 ∙ by Wei Dai, et al. ∙ 2 ∙ share

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Adversarial Domain Adaptation Being Aware of Class Relationships

Adversarial training is a useful approach to promote the learning of tra...
05/28/2019 ∙ by Zeya Wang, et al. ∙ 1 ∙ share

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Learning Less-Overlapping Representations

In representation learning (RL), how to make the learned representations...
11/25/2017 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

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Diversity-Promoting Bayesian Learning of Latent Variable Models

To address three important issues involved in latent variable models (LV...
11/23/2017 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

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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...
11/12/2017 ∙ by Shiyue Zhang, et al. ∙ 0 ∙ share

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A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction

While linear mixed model (LMM) has shown a competitive performance in co...
11/11/2017 ∙ by Wenting Ye, et al. ∙ 0 ∙ share

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Structured Generative Adversarial Networks

We study the problem of conditional generative modeling based on designa...
11/02/2017 ∙ by Zhijie Deng, et al. ∙ 0 ∙ share

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Towards Visual Explanations for Convolutional Neural Networks via Input Resampling

The predictive power of neural networks often costs model interpretabili...
07/30/2017 ∙ by Benjamin J. Lengerich, et al. ∙ 0 ∙ share

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On Unifying Deep Generative Models

Deep generative models have achieved impressive success in recent years....
06/02/2017 ∙ by Zhiting Hu, et al. ∙ 0 ∙ share

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Generative Semantic Manipulation with Contrasting GAN

Generative Adversarial Networks (GANs) have recently achieved significan...
08/01/2017 ∙ by Xiaodan Liang, et al. ∙ 0 ∙ share

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Dual Motion GAN for Future-Flow Embedded Video Prediction

Future frame prediction in videos is a promising avenue for unsupervised...
08/01/2017 ∙ by Xiaodan Liang, et al. ∙ 0 ∙ share

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Contextual Explanation Networks

We introduce contextual explanation networks (CENs)---a class of models ...
05/29/2017 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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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...
11/29/2016 ∙ by Jingkang Yang, et al. ∙ 0 ∙ share

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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 GPU-equipped ma...
06/11/2017 ∙ by Hao Zhang, et al. ∙ 0 ∙ share

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Stochastic Variational Deep Kernel Learning

Deep kernel learning combines the non-parametric flexibility of kernel m...
11/01/2016 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ share

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Learning Scalable Deep Kernels with Recurrent Structure

Many applications in speech, robotics, finance, and biology deal with se...
10/27/2016 ∙ by Maruan Al-Shedivat, et al. ∙ 0 ∙ share

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SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

Chest X-ray (CXR) is one of the most commonly prescribed medical imaging...
03/26/2017 ∙ by Wei Dai, et al. ∙ 0 ∙ share

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Recurrent Topic-Transition GAN for Visual Paragraph Generation

A natural image usually conveys rich semantic content and can be viewed ...
03/21/2017 ∙ by Xiaodan Liang, et al. ∙ 0 ∙ share

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Interpretable Structure-Evolving LSTM

This paper develops a general framework for learning interpretable data ...
03/08/2017 ∙ by Xiaodan Liang, et al. ∙ 0 ∙ share

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Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection

Despite progress in visual perception tasks such as image classification...
03/08/2017 ∙ by Xiaodan Liang, et al. ∙ 0 ∙ share

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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...
09/21/2016 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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State Space LSTM Models with Particle MCMC Inference

Long Short-Term Memory (LSTM) is one of the most powerful sequence model...
11/30/2017 ∙ by Xun Zheng, et al. ∙ 0 ∙ share

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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...
12/31/2015 ∙ by Eric P Xing, et al. ∙ 0 ∙ share

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Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums

Online discussion forums are complex webs of overlapping subcommunities ...
12/10/2015 ∙ by Abhimanu Kumar, et al. ∙ 0 ∙ share

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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), ...
12/09/2015 ∙ by Abhimanu Kumar, et al. ∙ 0 ∙ share

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Deep Kernel Learning

We introduce scalable deep kernels, which combine the structural propert...
11/06/2015 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ share

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The Human Kernel

Bayesian nonparametric models, such as Gaussian processes, provide a com...
10/26/2015 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ share

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Bayesian Nonparametric Kernel-Learning

Kernel methods are ubiquitous tools in machine learning. They have prove...
06/29/2015 ∙ by Junier Oliva, et al. ∙ 0 ∙ share

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Alternating Directions Dual Decomposition

We propose AD3, a new algorithm for approximate maximum a posteriori (MA...
12/28/2012 ∙ by André F. T. Martins, et al. ∙ 0 ∙ share

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LightLDA: Big Topic Models on Modest Compute Clusters

When building large-scale machine learning (ML) programs, such as big to...
12/04/2014 ∙ by Jinhui Yuan, et al. ∙ 0 ∙ share

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Model-Parallel Inference for Big Topic Models

In real world industrial applications of topic modeling, the ability to ...
11/10/2014 ∙ by Xun Zheng, et al. ∙ 0 ∙ share

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High-Performance Distributed ML at Scale through Parameter Server Consistency Models

As Machine Learning (ML) applications increase in data size and model co...
10/29/2014 ∙ by Wei Dai, et al. ∙ 0 ∙ share

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Screening Rules for Overlapping Group Lasso

Recently, to solve large-scale lasso and group lasso problems, screening...
10/25/2014 ∙ by Seunghak Lee, et al. ∙ 0 ∙ share

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Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

We develop parallel and distributed Frank-Wolfe algorithms; the former o...
09/22/2014 ∙ by Yu-Xiang Wang, et al. ∙ 0 ∙ share

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TopicViz: Semantic Navigation of Document Collections

When people explore and manage information, they think in terms of topic...
10/27/2011 ∙ by Jacob Eisenstein, et al. ∙ 0 ∙ share

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Consistent Bounded-Asynchronous Parameter Servers for Distributed ML

In distributed ML applications, shared parameters are usually replicated...
12/30/2013 ∙ by Jinliang Wei, et al. ∙ 0 ∙ share

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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...
12/30/2013 ∙ by Eric P Xing, et al. ∙ 0 ∙ share

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Language Modeling with Power Low Rank Ensembles

We present power low rank ensembles (PLRE), a flexible framework for n-g...
12/26/2013 ∙ by Ankur P. Parikh, et al. ∙ 0 ∙ share

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Structure-Aware Dynamic Scheduler for Parallel Machine Learning

Training large machine learning (ML) models with many variables or param...
12/19/2013 ∙ by Seunghak Lee, et al. ∙ 0 ∙ share

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Integrating Document Clustering and Topic Modeling

Document clustering and topic modeling are two closely related tasks whi...
09/26/2013 ∙ by Pengtao Xie, et al. ∙ 0 ∙ share

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Eric P Xing

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