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ORCIDRong Ge 0001
Person information
- affiliation: Duke University, Durham, NC, USA
- affiliation (former): Princeton University, NJ, USA
Other persons with the same name
- Rong Ge — disambiguation page
- Rong Ge 0002
![[0000-0002-2218-3675]](/help/writing-https-dblp.org/img/orcid-mark.12x12.png "0000-0002-2218-3675")
— Clemson University, School of Computing, SC, USA - Rong Ge 0003 — Marquette University, Milwaukee, WI, USA
- Rong Ge 0005 — Purdue University, West Lafayette, IN, USA
- Rong Ge 0006 — Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Ecosystem Network Observation and Modeling, Beijing, China
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2020 – today
- 2026
[i87]Anish Hebbar, Rong Ge, Amit Kumar, Debmalya Panigrahi:
Learning-Augmented Algorithms for k-median via Online Learning. CoRR abs/2603.18157 (2026)- [i86]Ruomin Huang, Eshaan Nichani, Jason D. Lee, Rong Ge:
Fine-Tuning Dynamics of In-Context Factual Recall in Transformers. CoRR abs/2605.27774 (2026) - 2025
- [c80]Muthu Chidambaram, Rong Ge:
Reassessing How to Compare and Improve the Calibration of Machine Learning Models. ICLR 2025 - [c79]Muthu Chidambaram, Rong Ge:
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. ICLR 2025 - [c78]Anish Hebbar, Rong Ge, Amit Kumar, Debmalya Panigrahi:
Learning-Augmented Algorithms for $k$-median via Online Learning. NeurIPS 2025 - 2024
- [c77]Muthu Chidambaram, Rong Ge:
On the Limitations of Temperature Scaling for Distributions with Overlaps. ICLR 2024 - [c76]Mo Zhou, Rong Ge:
How does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. NeurIPS 2024 - [i85]Muthu Chidambaram, Rong Ge:
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. CoRR abs/2402.06855 (2024) - [i84]Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright:
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. CoRR abs/2403.10547 (2024) - [i83]Mo Zhou, Rong Ge:
How Does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. CoRR abs/2406.01766 (2024) - [i82]Muthu Chidambaram, Rong Ge:
Reassessing How to Compare and Improve the Calibration of Machine Learning Models. CoRR abs/2406.04068 (2024) - 2023
- [c75]Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora:
Do Transformers Parse while Predicting the Masked Word? EMNLP 2023: 16513-16542 - [c74]Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang:
The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. ICBINB 2023: 118-126 - [c73]Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge:
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. ICLR 2023 - [c72]Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge:
Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. ICLR 2023 - [c71]Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge:
Understanding The Robustness of Self-supervised Learning Through Topic Modeling. ICLR 2023 - [c70]Yunwei Ren, Mo Zhou, Rong Ge:
Depth Separation with Multilayer Mean-Field Networks. ICLR 2023 - [c69]Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge:
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. ICML 2023: 5563-5599 - [c68]Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge:
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. ICML 2023: 5600-5615 - [c67]Mo Zhou, Rong Ge:
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. ICML 2023: 42543-42573 - [c66]Chenwei Wu, Holden Lee, Rong Ge:
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation. NeurIPS 2023 - [c65]Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee:
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. NeurIPS 2023 - [c64]Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright:
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. NeurIPS 2023 - [i81]Mo Zhou, Rong Ge:
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. CoRR abs/2302.00257 (2023) - [i80]Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge:
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. CoRR abs/2302.12715 (2023) - [i79]Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora:
Do Transformers Parse while Predicting the Masked Word? CoRR abs/2303.08117 (2023) - [i78]Yunwei Ren, Mo Zhou, Rong Ge:
Depth Separation with Multilayer Mean-Field Networks. CoRR abs/2304.01063 (2023) - [i77]Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee:
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. CoRR abs/2305.10633 (2023) - [i76]Muthu Chidambaram, Rong Ge:
A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration. CoRR abs/2306.00740 (2023) - [i75]Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang:
The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. CoRR abs/2310.02777 (2023) - 2022
- [j14]Abraham Frandsen, Rong Ge:
Optimization landscape of Tucker decomposition. Math. Program. 193(2): 687-712 (2022) - [j13]Rong Ge, Tengyu Ma:
On the optimization landscape of tensor decompositions. Math. Program. 193(2): 713-759 (2022) - [c63]Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge:
Towards Understanding the Data Dependency of Mixup-style Training. ICLR 2022 - [c62]Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi:
Online Algorithms with Multiple Predictions. ICML 2022: 582-598 - [c61]Abraham Frandsen, Rong Ge, Holden Lee:
Extracting Latent State Representations with Linear Dynamics from Rich Observations. ICML 2022: 6705-6725 - [c60]Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. NeurIPS 2022 - [i74]Zeping Luo, Cindy Weng, Shiyou Wu, Mo Zhou, Rong Ge:
One Objective for All Models - Self-supervised Learning for Topic Models. CoRR abs/2203.03539 (2022) - [i73]Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi:
Online Algorithms with Multiple Predictions. CoRR abs/2205.03921 (2022) - [i72]Keerti Anand, Rong Ge, Debmalya Panigrahi:
Customizing ML Predictions for Online Algorithms. CoRR abs/2205.08715 (2022) - [i71]Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi:
A Regression Approach to Learning-Augmented Online Algorithms. CoRR abs/2205.08717 (2022) - [i70]Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge:
Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. CoRR abs/2210.01019 (2022) - [i69]Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge:
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. CoRR abs/2210.03294 (2022) - [i68]Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge:
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. CoRR abs/2210.13512 (2022) - 2021
- [j12]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68(2): 11:1-11:29 (2021) - [j11]Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi:
Online Service with Delay. ACM Trans. Algorithms 17(3): 23:1-23:31 (2021) - [c59]Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski:
Efficient sampling from the Bingham distribution. ALT 2021: 673-685 - [c58]Mo Zhou, Rong Ge, Chi Jin:
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. COLT 2021: 4577-4632 - [c57]Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge:
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. ICML 2021: 10981-10990 - [c56]Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou:
Understanding Deflation Process in Over-parametrized Tensor Decomposition. NeurIPS 2021: 1299-1311 - [c55]Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi:
A Regression Approach to Learning-Augmented Online Algorithms. NeurIPS 2021: 30504-30517 - [i67]Mo Zhou, Rong Ge, Chi Jin:
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. CoRR abs/2102.02410 (2021) - [i66]Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou:
Understanding Deflation Process in Over-parametrized Tensor Decomposition. CoRR abs/2106.06573 (2021) - [i65]Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi:
Outlier-Robust Sparse Estimation via Non-Convex Optimization. CoRR abs/2109.11515 (2021) - [i64]Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge:
Towards Understanding the Data Dependency of Mixup-style Training. CoRR abs/2110.07647 (2021) - 2020
- [c54]Keerti Anand, Rong Ge, Debmalya Panigrahi:
Customizing ML Predictions for Online Algorithms. ICML 2020: 303-313 - [c53]Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi:
High-dimensional Robust Mean Estimation via Gradient Descent. ICML 2020: 1768-1778 - [c52]Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge:
Beyond Lazy Training for Over-parameterized Tensor Decomposition. NeurIPS 2020 - [c51]Rong Ge, Holden Lee, Jianfeng Lu:
Estimating normalizing constants for log-concave distributions: algorithms and lower bounds. STOC 2020: 579-586 - [p1]Rong Ge, Ankur Moitra:
Topic Models and Nonnegative Matrix Factorization. Beyond the Worst-Case Analysis of Algorithms 2020: 445-464 - [i63]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. CoRR abs/2004.07984 (2020) - [i62]Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi:
High-Dimensional Robust Mean Estimation via Gradient Descent. CoRR abs/2005.01378 (2020) - [i61]Abraham Frandsen, Rong Ge:
Extracting Latent State Representations with Linear Dynamics from Rich Observations. CoRR abs/2006.16128 (2020) - [i60]Abraham Frandsen, Rong Ge:
Optimization Landscape of Tucker Decomposition. CoRR abs/2006.16297 (2020) - [i59]Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge:
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. CoRR abs/2006.16495 (2020) - [i58]Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski:
Efficient sampling from the Bingham distribution. CoRR abs/2010.00137 (2020) - [i57]Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie N. Wang, Rong Ge:
Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks. CoRR abs/2010.04261 (2020) - [i56]Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge:
Beyond Lazy Training for Over-parameterized Tensor Decomposition. CoRR abs/2010.11356 (2020)
2010 – 2019
- 2019
- [j10]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. 12(5-6): 393-536 (2019) - [c50]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. COLT 2019: 727-757 - [c49]Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang:
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. COLT 2019: 1394-1448 - [c48]Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019: 3190-3193 - [c47]Abraham Frandsen, Rong Ge:
Understanding Composition of Word Embeddings via Tensor Decomposition. ICLR (Poster) 2019 - [c46]Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang:
Learning Two-layer Neural Networks with Symmetric Inputs. ICLR (Poster) 2019 - [c45]Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora:
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. NeurIPS 2019: 14574-14583 - [c44]Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli:
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. NeurIPS 2019: 14951-14962 - [c43]Yu Cheng, Ilias Diakonikolas, Rong Ge:
High-Dimensional Robust Mean Estimation in Nearly-Linear Time. SODA 2019: 2755-2771 - [i55]Abraham Frandsen, Rong Ge:
Understanding Composition of Word Embeddings via Tensor Decomposition. CoRR abs/1902.00613 (2019) - [i54]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR abs/1902.03736 (2019) - [i53]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR abs/1902.04811 (2019) - [i52]Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli:
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR abs/1904.12838 (2019) - [i51]Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang:
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. CoRR abs/1905.00529 (2019) - [i50]Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff:
Faster Algorithms for High-Dimensional Robust Covariance Estimation. CoRR abs/1906.04661 (2019) - [i49]Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge:
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. CoRR abs/1906.06247 (2019) - [i48]Rong Ge, Runzhe Wang, Haoyu Zhao:
Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently. CoRR abs/1909.11837 (2019) - [i47]Rong Ge, Holden Lee, Jianfeng Lu:
Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds. CoRR abs/1911.03043 (2019) - 2018
- [j9]Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
Learning topic models - provably and efficiently. Commun. ACM 61(4): 85-93 (2018) - [c42]Yu Cheng, Rong Ge:
Non-Convex Matrix Completion Against a Semi-Random Adversary. COLT 2018: 1362-1394 - [c41]Rong Ge, Jason D. Lee, Tengyu Ma:
Learning One-hidden-layer Neural Networks with Landscape Design. ICLR (Poster) 2018 - [c40]Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML 2018: 254-263 - [c39]Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi:
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. ICML 2018: 1466-1475 - [c38]Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
On the Local Minima of the Empirical Risk. NeurIPS 2018: 4901-4910 - [c37]Holden Lee, Andrej Risteski, Rong Ge:
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. NeurIPS 2018: 7858-7867 - [i46]Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi:
Global Convergence of Policy Gradient Methods for Linearized Control Problems. CoRR abs/1801.05039 (2018) - [i45]Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger generalization bounds for deep nets via a compression approach. CoRR abs/1802.05296 (2018) - [i44]Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan:
Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR abs/1803.09357 (2018) - [i43]Yu Cheng, Rong Ge:
Non-Convex Matrix Completion Against a Semi-Random Adversary. CoRR abs/1803.10846 (2018) - [i42]Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang:
Learning Two-layer Neural Networks with Symmetric Inputs. CoRR abs/1810.06793 (2018) - [i41]Yu Cheng, Ilias Diakonikolas, Rong Ge:
High-Dimensional Robust Mean Estimation in Nearly-Linear Time. CoRR abs/1811.09380 (2018) - [i40]Rong Ge, Holden Lee, Andrej Risteski:
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition. CoRR abs/1812.00793 (2018) - 2017
- [j8]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics in Overcomplete Regime. J. Mach. Learn. Res. 18: 22:1-22:40 (2017) - [c36]Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi:
Homotopy Analysis for Tensor PCA. COLT 2017: 79-104 - [c35]Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora:
On the Ability of Neural Nets to Express Distributions. COLT 2017: 1271-1296 - [c34]Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang:
Generalization and Equilibrium in Generative Adversarial Nets (GANs). ICML 2017: 224-232 - [c33]Rong Ge, Chi Jin, Yi Zheng:
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. ICML 2017: 1233-1242 - [c32]Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. ICML 2017: 1724-1732 - [c31]Rong Ge, Tengyu Ma:
On the Optimization Landscape of Tensor Decompositions. NIPS 2017: 3653-3663 - [c30]Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi:
Online service with delay. STOC 2017: 551-563 - [c29]Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski:
Provable learning of noisy-OR networks. STOC 2017: 1057-1066 - [i39]Holden Lee, Rong Ge, Andrej Risteski, Tengyu Ma, Sanjeev Arora:
On the ability of neural nets to express distributions. CoRR abs/1702.07028 (2017) - [i38]Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang:
Generalization and Equilibrium in Generative Adversarial Nets (GANs). CoRR abs/1703.00573 (2017) - [i37]Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. CoRR abs/1703.00887 (2017) - [i36]Rong Ge, Chi Jin, Yi Zheng:
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. CoRR abs/1704.00708 (2017) - [i35]Rong Ge, Tengyu Ma:
On the Optimization Landscape of Tensor Decompositions. CoRR abs/1706.05598 (2017) - [i34]Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi:
Online Service with Delay. CoRR abs/1708.05611 (2017) - [i33]Rong Ge, Holden Lee, Andrej Risteski:
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. CoRR abs/1710.02736 (2017) - [i32]Rong Ge, Jason D. Lee, Tengyu Ma:
Learning One-hidden-layer Neural Networks with Landscape Design. CoRR abs/1711.00501 (2017) - 2016
- [j7]Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra:
Computing a Nonnegative Matrix Factorization - Provably. SIAM J. Comput. 45(4): 1582-1611 (2016) - [j6]Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh:
Minimal Realization Problems for Hidden Markov Models. IEEE Trans. Signal Process. 64(7): 1896-1904 (2016) - [c28]Animashree Anandkumar, Rong Ge:
Efficient approaches for escaping higher order saddle points in non-convex optimization. COLT 2016: 81-102 - [c27]Rong Ge, James Zou:
Rich Component Analysis. ICML 2016: 1502-1510 - [c26]Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. ICML 2016: 2741-2750 - [c25]Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. ICML 2016: 2859-2867 - [c24]Rong Ge, Jason D. Lee, Tengyu Ma:
Matrix Completion has No Spurious Local Minimum. NIPS 2016: 2973-2981 - [i31]Anima Anandkumar, Rong Ge:
Efficient approaches for escaping higher order saddle points in non-convex optimization. CoRR abs/1602.05908 (2016) - [i30]Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. CoRR abs/1604.03930 (2016) - [i29]Rong Ge, Jason D. Lee, Tengyu Ma:
Matrix Completion has No Spurious Local Minimum. CoRR abs/1605.07272 (2016) - [i28]Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. CoRR abs/1605.08491 (2016) - [i27]Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi:
Homotopy Method for Tensor Principal Component Analysis. CoRR abs/1610.09322 (2016) - [i26]Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski:
Provable learning of Noisy-or Networks. CoRR abs/1612.08795 (2016) - 2015
- [j5]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. Algorithmica 72(1): 215-236 (2015) - [c23]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). ALT 2015: 19-38 - [c22]Rong Ge, Tengyu Ma:
Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms. APPROX-RANDOM 2015: 829-849 - [c21]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Learning Overcomplete Latent Variable Models through Tensor Methods. COLT 2015: 36-112 - [c20]Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra:
Simple, Efficient, and Neural Algorithms for Sparse Coding. COLT 2015: 113-149 - [c19]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. COLT 2015: 728-763 - [c18]Rong Ge, Furong Huang, Chi Jin, Yang Yuan:
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. COLT 2015: 797-842 - [c17]Rong Ge, James Zou:
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees. ICML 2015: 2295-2303 - [c16]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. ICML 2015: 2540-2548 - [c15]Rong Ge, Qingqing Huang, Sham M. Kakade:
Learning Mixtures of Gaussians in High Dimensions. STOC 2015: 761-770 - [i25]Rong Ge, Qingqing Huang, Sham M. Kakade:
Learning Mixtures of Gaussians in High Dimensions. CoRR abs/1503.00424 (2015) - [i24]Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra:
Simple, Efficient, and Neural Algorithms for Sparse Coding. CoRR abs/1503.00778 (2015) - [i23]Rong Ge, Furong Huang, Chi Jin, Yang Yuan:
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. CoRR abs/1503.02101 (2015) - [i22]Rong Ge, Tengyu Ma:
Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms. CoRR abs/1504.05287 (2015) - [i21]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. CoRR abs/1506.07512 (2015) - [i20]Rong Ge, James Zou:
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees. CoRR abs/1507.02189 (2015) - [i19]Rong Ge, James Zou:
Rich Component Analysis. CoRR abs/1507.03867 (2015) - 2014
- [j4]Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A tensor approach to learning mixed membership community models. J. Mach. Learn. Res. 15(1): 2239-2312 (2014) - [j3]Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15(1): 2773-2832 (2014) - [c14]Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh:
Minimal realization problem for Hidden Markov Models. Allerton 2014: 4-11 - [c13]Sanjeev Arora, Rong Ge, Ankur Moitra:
New Algorithms for Learning Incoherent and Overcomplete Dictionaries. COLT 2014: 779-806 - [c12]Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
Provable Bounds for Learning Some Deep Representations. ICML 2014: 584-592 - [i18]Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
More Algorithms for Provable Dictionary Learning. CoRR abs/1401.0579 (2014) - [i17]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates. CoRR abs/1402.5180 (2014) - [i16]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings. CoRR abs/1408.0553 (2014) - [i15]Anima Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models. CoRR abs/1411.1488 (2014) - [i14]Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh:
Minimal Realization Problems for Hidden Markov Models. CoRR abs/1411.3698 (2014) - [i13]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. CoRR abs/1412.6606 (2014) - 2013
- [b1]Rong Ge:
Provable Algorithms for Machine Learning Problems. Princeton University, USA, 2013 - [c11]Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A Tensor Spectral Approach to Learning Mixed Membership Community Models. COLT 2013: 867-881 - [c10]Sanjeev Arora, Rong Ge, Ali Kemal Sinop:
Towards a Better Approximation for Sparsest Cut? FOCS 2013: 270-279 - [c9]Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. ICML (2) 2013: 280-288 - [i12]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade:
A Tensor Spectral Approach to Learning Mixed Membership Community Models. CoRR abs/1302.2684 (2013) - [i11]Sanjeev Arora, Rong Ge, Ali Kemal Sinop:
Towards a better approximation for sparsest cut? CoRR abs/1304.3365 (2013) - [i10]Sanjeev Arora, Rong Ge, Ankur Moitra:
New Algorithms for Learning Incoherent and Overcomplete Dictionaries. CoRR abs/1308.6273 (2013) - [i9]Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma:
Provable Bounds for Learning Some Deep Representations. CoRR abs/1310.6343 (2013) - 2012
- [c8]Sanjeev Arora, Rong Ge, Ankur Moitra:
Learning Topic Models - Going beyond SVD. FOCS 2012: 1-10 - [c7]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". NIPS 2012: 2384-2392 - [c6]Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck:
Finding overlapping communities in social networks: toward a rigorous approach. EC 2012: 37-54 - [c5]Sanjeev Arora, Rong Ge, Ravindran Kannan, Ankur Moitra:
Computing a nonnegative matrix factorization - provably. STOC 2012: 145-162 - [i8]Sanjeev Arora, Rong Ge, Ankur Moitra:
Learning Topic Models - Going beyond SVD. CoRR abs/1204.1956 (2012) - [i7]Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva:
Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. CoRR abs/1206.5349 (2012) - [i6]Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor decompositions for learning latent variable models. CoRR abs/1210.7559 (2012) - [i5]Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu:
A Practical Algorithm for Topic Modeling with Provable Guarantees. CoRR abs/1212.4777 (2012) - 2011
- [j2]Decheng Dai, Rong Ge:
Another Sub-exponential Algorithm for the Simple Stochastic Game. Algorithmica 61(4): 1092-1104 (2011) - [j1]Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge:
Computational complexity and information asymmetry in financial products. Commun. ACM 54(5): 101-107 (2011) - [c4]Sanjeev Arora, Rong Ge:
New Tools for Graph Coloring. APPROX-RANDOM 2011: 1-12 - [c3]Sanjeev Arora, Rong Ge:
New Algorithms for Learning in Presence of Errors. ICALP (1) 2011: 403-415 - [i4]Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra:
Computing a Nonnegative Matrix Factorization -- Provably. CoRR abs/1111.0952 (2011) - [i3]Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck:
Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach. CoRR abs/1112.1831 (2011) - 2010
- [c2]Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge:
Computational Complexity and Information Asymmetry in Financial Products (Extended Abstract). ICS 2010: 49-65 - [i2]Sanjeev Arora, Rong Ge:
Learning Parities with Structured Noise. Electron. Colloquium Comput. Complex. TR10: Article TR10-066 (2010)
2000 – 2009
- 2009
- [c1]Decheng Dai, Rong Ge:
New Results on Simple Stochastic Games. ISAAC 2009: 1014-1023 - 2008
- [i1]Fangtao Li, Zhicheng Zheng, Yang Tang, Fan Bu, Rong Ge, Xiaoyan Zhu, Xian Zhang, Minlie Huang:
THU QUANTA at TAC 2008 QA and RTE Track. TAC 2008
Coauthor Index
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