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Rong Ge

Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo

Nov 06, 2017
Rong Ge, Holden Lee, Andrej Risteski

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* 53 pages

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Learning One-hidden-layer Neural Networks with Landscape Design

Nov 03, 2017
Rong Ge, Jason D. Lee, Tengyu Ma

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Generalization and Equilibrium in Generative Adversarial Nets (GANs)

Aug 01, 2017
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang

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* This is an updated version of an ICML'17 paper with the same title. The main difference is that in the ICML'17 version the pure equilibrium result was only proved for Wasserstein GAN. In the current version the result applies to most reasonable training objectives. In particular, Theorem 4.3 now applies to both original GAN and Wasserstein GAN

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On the Optimization Landscape of Tensor Decompositions

Jun 18, 2017
Rong Ge, Tengyu Ma

* Best paper in the NIPS 2016 Workshop on Nonconvex Optimization for Machine Learning: Theory and Practice. In submission

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Homotopy Analysis for Tensor PCA

Jun 14, 2017
Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi

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* Accepted to COLT 2017

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On the ability of neural nets to express distributions

Jun 02, 2017
Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora

* Accepted to COLT 2017

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No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis

Apr 03, 2017
Rong Ge, Chi Jin, Yi Zheng

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Provable learning of Noisy-or Networks

Dec 28, 2016
Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski

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Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

May 27, 2016
Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford

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* International Conference on Machine Learning (ICML) 2016

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