Rong Ge - RankMe

181 papers A* 63A 1B 3Journal 99Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning-Augmented Algorithms for k-median via Online Learning. CoRR Anish Hebbar, Rong Ge, Amit Kumar, Debmalya Panigrahi
2025	A*	`conf`	For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. ICLR Muthu Chidambaram, Rong Ge
2025	A*	`conf`	Reassessing How to Compare and Improve the Calibration of Machine Learning Models. ICLR Muthu Chidambaram, Rong Ge
2024	J	`jnl`	For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. CoRR Muthu Chidambaram, Rong Ge
2024	J	`jnl`	How Does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. CoRR Mo Zhou, Rong Ge
2024	A*	`conf`	How does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. NeurIPS Mo Zhou, Rong Ge
2024	A*	`conf`	On the Limitations of Temperature Scaling for Distributions with Overlaps. ICLR Muthu Chidambaram, Rong Ge
2024	J	`jnl`	Reassessing How to Compare and Improve the Calibration of Machine Learning Models. CoRR Muthu Chidambaram, Rong Ge
2024	J	`jnl`	Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. CoRR Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright
2023	J	`jnl`	A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration. CoRR Muthu Chidambaram, Rong Ge
2023	A*	`conf`	Connecting Pre-trained Language Model and Downstream Task via Properties of Representation. NeurIPS Chenwei Wu, Holden Lee, Rong Ge
2023	A*	`conf`	Depth Separation with Multilayer Mean-Field Networks. ICLR Yunwei Ren, Mo Zhou, Rong Ge
2023	J	`jnl`	Depth Separation with Multilayer Mean-Field Networks. CoRR Yunwei Ren, Mo Zhou, Rong Ge
2023	A*	`conf`	Do Transformers Parse while Predicting the Masked Word? EMNLP Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
2023	J	`jnl`	Do Transformers Parse while Predicting the Masked Word? CoRR Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
2023	A*	`conf`	Hiding Data Helps: On the Benefits of Masking for Sparse Coding. ICML Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge
2023	J	`jnl`	Hiding Data Helps: On the Benefits of Masking for Sparse Coding. CoRR Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge
2023	A*	`conf`	Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. ICML Mo Zhou, Rong Ge
2023	J	`jnl`	Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. CoRR Mo Zhou, Rong Ge
2023	A*	`conf`	Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. ICLR Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge
2023	A*	`conf`	Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. ICML Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge
2023	A*	`conf`	Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. NeurIPS Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright
2023	A*	`conf`	Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. NeurIPS Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee
2023	J	`jnl`	Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. CoRR Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee
2023	—	`conf`	The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. ICBINB Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang
2023	J	`jnl`	The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. CoRR Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang
2023	A*	`conf`	Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. ICLR Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge
2023	A*	`conf`	Understanding The Robustness of Self-supervised Learning Through Topic Modeling. ICLR Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge
2022	J	`jnl`	A Regression Approach to Learning-Augmented Online Algorithms. CoRR Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
2022	J	`jnl`	Customizing ML Predictions for Online Algorithms. CoRR Keerti Anand, Rong Ge, Debmalya Panigrahi
2022	A*	`conf`	Extracting Latent State Representations with Linear Dynamics from Rich Observations. ICML Abraham Frandsen, Rong Ge, Holden Lee
2022	J	`jnl`	On the optimization landscape of tensor decompositions. Math. Program. Rong Ge, Tengyu Ma
2022	J	`jnl`	One Objective for All Models - Self-supervised Learning for Topic Models. CoRR Zeping Luo, Cindy Weng, Shiyou Wu, Mo Zhou, Rong Ge
2022	A*	`conf`	Online Algorithms with Multiple Predictions. ICML Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
2022	J	`jnl`	Online Algorithms with Multiple Predictions. CoRR Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
2022	J	`jnl`	Optimization landscape of Tucker decomposition. Math. Program. Abraham Frandsen, Rong Ge
2022	A*	`conf`	Outlier-Robust Sparse Estimation via Non-Convex Optimization. NeurIPS Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi
2022	J	`jnl`	Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. CoRR Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge
2022	J	`jnl`	Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. CoRR Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge
2022	A*	`conf`	Towards Understanding the Data Dependency of Mixup-style Training. ICLR Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge
2022	J	`jnl`	Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. CoRR Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge
2021	A*	`conf`	A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. COLT Mo Zhou, Rong Ge, Chi Jin
2021	J	`jnl`	A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. CoRR Mo Zhou, Rong Ge, Chi Jin
2021	A*	`conf`	A Regression Approach to Learning-Augmented Online Algorithms. NeurIPS Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
2021	B	`conf`	Efficient sampling from the Bingham distribution. ALT Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski
2021	A*	`conf`	Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. ICML Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
2021	J	`jnl`	On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2021	J	`jnl`	Online Service with Delay. ACM Trans. Algorithms Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
2021	J	`jnl`	Outlier-Robust Sparse Estimation via Non-Convex Optimization. CoRR Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi
2021	J	`jnl`	Towards Understanding the Data Dependency of Mixup-style Training. CoRR Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge
2021	A*	`conf`	Understanding Deflation Process in Over-parametrized Tensor Decomposition. NeurIPS Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou
2021	J	`jnl`	Understanding Deflation Process in Over-parametrized Tensor Decomposition. CoRR Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou
2020	A*	`conf`	Beyond Lazy Training for Over-parameterized Tensor Decomposition. NeurIPS Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge
2020	J	`jnl`	Beyond Lazy Training for Over-parameterized Tensor Decomposition. CoRR Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge
2020	A*	`conf`	Customizing ML Predictions for Online Algorithms. ICML Keerti Anand, Rong Ge, Debmalya Panigrahi
2020	J	`jnl`	Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks. CoRR Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie N. Wang, Rong Ge
2020	J	`jnl`	Efficient sampling from the Bingham distribution. CoRR Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski
2020	A*	`conf`	Estimating normalizing constants for log-concave distributions: algorithms and lower bounds. STOC Rong Ge, Holden Lee, Jianfeng Lu
2020	J	`jnl`	Extracting Latent State Representations with Linear Dynamics from Rich Observations. CoRR Abraham Frandsen, Rong Ge
2020	J	`jnl`	Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. CoRR Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
2020	J	`jnl`	High-Dimensional Robust Mean Estimation via Gradient Descent. CoRR Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi
2020	A*	`conf`	High-dimensional Robust Mean Estimation via Gradient Descent. ICML Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi
2020	J	`jnl`	Optimization Landscape of Tucker Decomposition. CoRR Abraham Frandsen, Rong Ge
2020	J	`jnl`	Spectral Learning on Matrices and Tensors. CoRR Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
2020	—	`ch.`	Topic Models and Nonnegative Matrix Factorization. Beyond the Worst-Case Analysis of Algorithms Rong Ge, Ankur Moitra
2019	J	`jnl`	A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	J	`jnl`	Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds. CoRR Rong Ge, Holden Lee, Jianfeng Lu
2019	A*	`conf`	Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. NeurIPS Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora
2019	J	`jnl`	Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. CoRR Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge
2019	A*	`conf`	Faster Algorithms for High-Dimensional Robust Covariance Estimation. COLT Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff
2019	J	`jnl`	Faster Algorithms for High-Dimensional Robust Covariance Estimation. CoRR Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff
2019	A*	`conf`	High-Dimensional Robust Mean Estimation in Nearly-Linear Time. SODA Yu Cheng, Ilias Diakonikolas, Rong Ge
2019	—	`conf`	Learning Two-layer Neural Networks with Symmetric Inputs. ICLR (Poster) Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
2019	J	`jnl`	Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently. CoRR Rong Ge, Runzhe Wang, Haoyu Zhao
2019	A*	`conf`	Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli
2019	J	`jnl`	Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
2019	A*	`conf`	Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. COLT Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang
2019	J	`jnl`	Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. CoRR Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang
2019	J	`jnl`	Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	A*	`conf`	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. NeurIPS Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
2019	J	`jnl`	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
2019	—	`conf`	Understanding Composition of Word Embeddings via Tensor Decomposition. ICLR (Poster) Abraham Frandsen, Rong Ge
2019	J	`jnl`	Understanding Composition of Word Embeddings via Tensor Decomposition. CoRR Abraham Frandsen, Rong Ge
2018	A*	`conf`	Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. NeurIPS Holden Lee, Andrej Risteski, Rong Ge
2018	J	`jnl`	Global Convergence of Policy Gradient Methods for Linearized Control Problems. CoRR Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi
2018	A*	`conf`	Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. ICML Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi
2018	J	`jnl`	High-Dimensional Robust Mean Estimation in Nearly-Linear Time. CoRR Yu Cheng, Ilias Diakonikolas, Rong Ge
2018	—	`conf`	Learning One-hidden-layer Neural Networks with Landscape Design. ICLR (Poster) Rong Ge, Jason D. Lee, Tengyu Ma
2018	J	`jnl`	Learning Two-layer Neural Networks with Symmetric Inputs. CoRR Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
2018	J	`jnl`	Learning topic models - provably and efficiently. Commun. ACM Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2018	J	`jnl`	Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
2018	A*	`conf`	Non-Convex Matrix Completion Against a Semi-Random Adversary. COLT Yu Cheng, Rong Ge
2018	J	`jnl`	Non-Convex Matrix Completion Against a Semi-Random Adversary. CoRR Yu Cheng, Rong Ge
2018	A*	`conf`	On the Local Minima of the Empirical Risk. NeurIPS Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
2018	J	`jnl`	Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition. CoRR Rong Ge, Holden Lee, Andrej Risteski
2018	A*	`conf`	Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
2018	J	`jnl`	Stronger generalization bounds for deep nets via a compression approach. CoRR Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
2017	J	`jnl`	Analyzing Tensor Power Method Dynamics in Overcomplete Regime. J. Mach. Learn. Res. Animashree Anandkumar, Rong Ge, Majid Janzamin
2017	J	`jnl`	Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. CoRR Rong Ge, Holden Lee, Andrej Risteski
2017	A*	`conf`	Generalization and Equilibrium in Generative Adversarial Nets (GANs). ICML Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
2017	J	`jnl`	Generalization and Equilibrium in Generative Adversarial Nets (GANs). CoRR Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
2017	A*	`conf`	Homotopy Analysis for Tensor PCA. COLT Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
2017	A*	`conf`	How to Escape Saddle Points Efficiently. ICML Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	J	`jnl`	How to Escape Saddle Points Efficiently. CoRR Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	J	`jnl`	Learning One-hidden-layer Neural Networks with Landscape Design. CoRR Rong Ge, Jason D. Lee, Tengyu Ma
2017	A*	`conf`	No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. ICML Rong Ge, Chi Jin, Yi Zheng
2017	J	`jnl`	No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. CoRR Rong Ge, Chi Jin, Yi Zheng
2017	A*	`conf`	On the Ability of Neural Nets to Express Distributions. COLT Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora
2017	—	`conf`	On the Optimization Landscape of Tensor Decompositions. NIPS Rong Ge, Tengyu Ma
2017	J	`jnl`	On the Optimization Landscape of Tensor Decompositions. CoRR Rong Ge, Tengyu Ma
2017	J	`jnl`	On the ability of neural nets to express distributions. CoRR Holden Lee, Rong Ge, Andrej Risteski, Tengyu Ma, Sanjeev Arora
2017	J	`jnl`	Online Service with Delay. CoRR Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
2017	A*	`conf`	Online service with delay. STOC Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
2017	A*	`conf`	Provable learning of noisy-OR networks. STOC Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
2016	J	`jnl`	Computing a Nonnegative Matrix Factorization - Provably. SIAM J. Comput. Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra
2016	A*	`conf`	Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. ICML Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. CoRR Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	A*	`conf`	Efficient approaches for escaping higher order saddle points in non-convex optimization. COLT Animashree Anandkumar, Rong Ge
2016	J	`jnl`	Efficient approaches for escaping higher order saddle points in non-convex optimization. CoRR Anima Anandkumar, Rong Ge
2016	J	`jnl`	Homotopy Method for Tensor Principal Component Analysis. CoRR Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
2016	—	`conf`	Matrix Completion has No Spurious Local Minimum. NIPS Rong Ge, Jason D. Lee, Tengyu Ma
2016	J	`jnl`	Matrix Completion has No Spurious Local Minimum. CoRR Rong Ge, Jason D. Lee, Tengyu Ma
2016	J	`jnl`	Minimal Realization Problems for Hidden Markov Models. IEEE Trans. Signal Process. Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh
2016	A*	`conf`	Provable Algorithms for Inference in Topic Models. ICML Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
2016	J	`jnl`	Provable Algorithms for Inference in Topic Models. CoRR Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
2016	J	`jnl`	Provable learning of Noisy-or Networks. CoRR Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski
2016	A*	`conf`	Rich Component Analysis. ICML Rong Ge, James Zou
2015	A*	`conf`	Competing with the Empirical Risk Minimizer in a Single Pass. COLT Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2015	—	`conf`	Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms. APPROX-RANDOM Rong Ge, Tengyu Ma
2015	J	`jnl`	Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms. CoRR Rong Ge, Tengyu Ma
2015	A*	`conf`	Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. COLT Rong Ge, Furong Huang, Chi Jin, Yang Yuan
2015	J	`jnl`	Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. CoRR Rong Ge, Furong Huang, Chi Jin, Yang Yuan
2015	A*	`conf`	Intersecting Faces: Non-negative Matrix Factorization With New Guarantees. ICML Rong Ge, James Zou
2015	J	`jnl`	Intersecting Faces: Non-negative Matrix Factorization With New Guarantees. CoRR Rong Ge, James Zou
2015	A*	`conf`	Learning Mixtures of Gaussians in High Dimensions. STOC Rong Ge, Qingqing Huang, Sham M. Kakade
2015	J	`jnl`	Learning Mixtures of Gaussians in High Dimensions. CoRR Rong Ge, Qingqing Huang, Sham M. Kakade
2015	A*	`conf`	Learning Overcomplete Latent Variable Models through Tensor Methods. COLT Animashree Anandkumar, Rong Ge, Majid Janzamin
2015	J	`jnl`	Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. Algorithmica Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
2015	J	`jnl`	Rich Component Analysis. CoRR Rong Ge, James Zou
2015	A*	`conf`	Simple, Efficient, and Neural Algorithms for Sparse Coding. COLT Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra
2015	J	`jnl`	Simple, Efficient, and Neural Algorithms for Sparse Coding. CoRR Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra
2015	B	`conf`	Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). ALT Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
2015	A*	`conf`	Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. ICML Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2015	J	`jnl`	Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. CoRR Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2014	J	`jnl`	A tensor approach to learning mixed membership community models. J. Mach. Learn. Res. Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
2014	J	`jnl`	Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models. CoRR Anima Anandkumar, Rong Ge, Majid Janzamin
2014	J	`jnl`	Competing with the Empirical Risk Minimizer in a Single Pass. CoRR Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford
2014	J	`jnl`	Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates. CoRR Animashree Anandkumar, Rong Ge, Majid Janzamin
2014	J	`jnl`	Minimal Realization Problems for Hidden Markov Models. CoRR Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh
2014	—	`conf`	Minimal realization problem for Hidden Markov Models. Allerton Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh
2014	J	`jnl`	More Algorithms for Provable Dictionary Learning. CoRR Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma
2014	A*	`conf`	New Algorithms for Learning Incoherent and Overcomplete Dictionaries. COLT Sanjeev Arora, Rong Ge, Ankur Moitra
2014	A*	`conf`	Provable Bounds for Learning Some Deep Representations. ICML Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma
2014	J	`jnl`	Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings. CoRR Animashree Anandkumar, Rong Ge, Majid Janzamin
2014	J	`jnl`	Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
2013	—	`conf`	A Practical Algorithm for Topic Modeling with Provable Guarantees. ICML (2) Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2013	J	`jnl`	A Tensor Spectral Approach to Learning Mixed Membership Community Models CoRR Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
2013	A*	`conf`	A Tensor Spectral Approach to Learning Mixed Membership Community Models. COLT Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade
2013	J	`jnl`	New Algorithms for Learning Incoherent and Overcomplete Dictionaries. CoRR Sanjeev Arora, Rong Ge, Ankur Moitra
2013	—	`—`	Provable Algorithms for Machine Learning Problems Rong Ge
2013	J	`jnl`	Provable Bounds for Learning Some Deep Representations. CoRR Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma
2013	A*	`conf`	Towards a Better Approximation for Sparsest Cut? FOCS Sanjeev Arora, Rong Ge, Ali Kemal Sinop
2013	J	`jnl`	Towards a better approximation for sparsest cut? CoRR Sanjeev Arora, Rong Ge, Ali Kemal Sinop
2012	—	`conf`	"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". NIPS Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
2012	J	`jnl`	A Practical Algorithm for Topic Modeling with Provable Guarantees CoRR Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu
2012	A*	`conf`	Computing a nonnegative matrix factorization - provably. STOC Sanjeev Arora, Rong Ge, Ravindran Kannan, Ankur Moitra
2012	A*	`conf`	Finding overlapping communities in social networks: toward a rigorous approach. EC Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck
2012	J	`jnl`	Learning Topic Models - Going beyond SVD CoRR Sanjeev Arora, Rong Ge, Ankur Moitra
2012	A*	`conf`	Learning Topic Models - Going beyond SVD. FOCS Sanjeev Arora, Rong Ge, Ankur Moitra
2012	J	`jnl`	Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders CoRR Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
2012	J	`jnl`	Tensor decompositions for learning latent variable models CoRR Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky
2011	J	`jnl`	Another Sub-exponential Algorithm for the Simple Stochastic Game. Algorithmica Decheng Dai, Rong Ge
2011	J	`jnl`	Computational complexity and information asymmetry in financial products. Commun. ACM Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge
2011	J	`jnl`	Computing a Nonnegative Matrix Factorization -- Provably CoRR Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra
2011	J	`jnl`	Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach CoRR Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck
2011	—	`conf`	New Algorithms for Learning in Presence of Errors. ICALP (1) Sanjeev Arora, Rong Ge
2011	—	`conf`	New Tools for Graph Coloring. APPROX-RANDOM Sanjeev Arora, Rong Ge
2010	A	`conf`	Computational Complexity and Information Asymmetry in Financial Products (Extended Abstract). ICS Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge
2010	J	`jnl`	Learning Parities with Structured Noise. Electron. Colloquium Comput. Complex. Sanjeev Arora, Rong Ge
2009	B	`conf`	New Results on Simple Stochastic Games. ISAAC Decheng Dai, Rong Ge
2008	—	`conf`	THU QUANTA at TAC 2008 QA and RTE Track. TAC Fangtao Li, Zhicheng Zheng, Yang Tang, Fan Bu, Rong Ge, Xiaoyan Zhu, Xian Zhang, Minlie Huang