Tan Nguyen

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qut ∙ Berkeley Lab

Sample Efficient Graph-Based Optimization with Noisy Observations

We study sample complexity of optimizing "hill-climbing friendly" functi...
06/04/2020 ∙ by Tan Nguyen, et al. ∙ 10 ∙ share

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Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees

The problem of learning an optimal convex combination of basis models ha...
10/09/2019 ∙ by Tan Nguyen, et al. ∙ 0 ∙ share

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Out-of-Distribution Detection Using Neural Rendering Generative Models

Out-of-distribution (OoD) detection is a natural downstream task for dee...
07/10/2019 ∙ by Yujia Huang, et al. ∙ 35 ∙ share

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Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

State-of-the-art convolutional neural networks (CNNs) yield record-break...
07/10/2019 ∙ by Yue Wang, et al. ∙ 0 ∙ share

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Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning

Unsupervised and semi-supervised learning are important problems that ar...
11/01/2018 ∙ by Nhat Ho, et al. ∙ 0 ∙ share

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Semi-Supervised Learning with the Deep Rendering Mixture Model

Semi-supervised learning algorithms reduce the high cost of acquiring la...
12/06/2016 ∙ by Tan Nguyen, et al. ∙ 0 ∙ share

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A Probabilistic Framework for Deep Learning

We develop a probabilistic framework for deep learning based on the Deep...
12/06/2016 ∙ by Ankit B. Patel, et al. ∙ 0 ∙ share

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BoxLib with Tiling: An AMR Software Framework

In this paper we introduce a block-structured adaptive mesh refinement (...
04/12/2016 ∙ by Weiqun Zhang, et al. ∙ 0 ∙ share

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A Probabilistic Theory of Deep Learning

A grand challenge in machine learning is the development of computationa...
04/02/2015 ∙ by Ankit B. Patel, et al. ∙ 0 ∙ share

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Tan Nguyen

Featured Co-authors

Michael I. Jordan

Yue Wang

Anima Anandkumar

Zhangyang Wang

Richard G. Baraniuk

Peter L. Bartlett

Branislav Kveton

Nhat Ho

Yasin Abbasi-Yadkori

Pengfei Xu

Richard Baraniuk

Sample Efficient Graph-Based Optimization with Noisy Observations

Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees

Out-of-Distribution Detection Using Neural Rendering Generative Models

Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning

Semi-Supervised Learning with the Deep Rendering Mixture Model

A Probabilistic Framework for Deep Learning

BoxLib with Tiling: An AMR Software Framework

A Probabilistic Theory of Deep Learning