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Ge, Rong

  1. Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
    2015/03/06 by Rong Ge, Ge, Rong, Furong Huang +5 · 96 citations
    Mathematics · Engineering · Computer Science · #Tensor decomposition and applications #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  2. Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
    2018/01/15 by Maryam Fazel, Rong Ge, Fazel, Maryam +5 · 41 citations
    Computer Science · Engineering · Physics and Astronomy · #Adaptive Dynamic Programming Control #Advanced Control Systems Optimization #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
  3. How to Escape Saddle Points Efficiently
    2017/03/02 by Jin, Chi, Ge, Rong, Netrapalli, Praneeth +2 · 33 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  4. Stronger generalization bounds for deep nets via a compression approach
    2018/02/14 by Sanjeev Arora, Rong Ge, Arora, Sanjeev +5 · 31 citations
    Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Stochastic Gradient Optimization Techniques
  5. No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified\n Geometric Analysis
    2017/04/03 by Rong Ge, Chi Jin, Ge, Rong +3 · 27 citations
    Engineering · Mathematics · Computer Science · #Sparse and Compressive Sensing Techniques #Advanced Optimization Algorithms Research #Stochastic Gradient Optimization Techniques
  6. Learning Topic Models - Going beyond SVD
    2012/04/09 by Sanjeev Arora, Rong Ge, Arora, Sanjeev +3 · 15 citations
    Computer Science · #Topic Modeling #Text and Document Classification Technologies #Advanced Text Analysis Techniques
  7. A Practical Algorithm for Topic Modeling with Provable Guarantees
    2012/12/19 by Sanjeev Arora, Arora, Sanjeev, Rong Ge +13 · 9 citations
    Computer Science · #Topic Modeling #Advanced Graph Neural Networks #Text and Document Classification Technologies
  8. Computing a Nonnegative Matrix Factorization -- Provably
    2011/11/03 by Sanjeev Arora, Rong Ge, Arora, Sanjeev +5 · 8 citations
    Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #DNA and Biological Computing #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #graph theory and CDMA systems
  9. Matrix Completion has No Spurious Local Minimum
    2016/05/24 by Ge, Rong, Lee, Jason D., Ma, Tengyu · 9 citations
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  10. Learning One-hidden-layer Neural Networks with Landscape Design
    2017/11/01 by Rong Ge, Jason D. Lee, Ge, Rong +3 · 20 citations
    Computer Science · #Stochastic Gradient Optimization Techniques #Machine Learning and Algorithms #Machine Learning and ELM
  11. A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
    2019/02/11 by Chi Jin, Praneeth Netrapalli, Jin, Chi +7 · 10 citations
    Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Approximation and Integration #Point processes and geometric inequalities #Probability (math.PR) #Random Matrices and Applications
  12. ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
    2024/06/23 by Roy Xie, Xie, Roy, Junlin Wang +13 · 17 citations
    Decision Sciences · #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG)
  13. Learning Mixtures of Gaussians in High Dimensions
    2015/03/02 by Ge, Rong, Huang, Qingqing, Kakade, Sham M. · 5 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  14. Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models
    2023/05/18 by Damian, Alex, Nichani, Eshaan, Ge, Rong +1 · 9 citations
    #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  15. Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates
    2014/02/21 by Anandkumar, Animashree, Ge, Rong, Janzamin, Majid · 4 citations
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA)
  16. The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning\n Rate Procedure For Least Squares
    2019/04/29 by Rong Ge, Sham M. Kakade, Ge, Rong +5 · 5 citations
    Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  17. Generalization and Equilibrium in Generative Adversarial Nets (GANs)
    2017/03/02 by Arora, Sanjeev, Ge, Rong, Liang, Yingyu +2 · 4 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
  18. Un-regularizing: approximate proximal point and faster stochastic\n algorithms for empirical risk minimization
    2015/06/24 by Roy Frostig, Rong Ge, Frostig, Roy +5 · 5 citations
    Computer Science · Decision Sciences · Engineering · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Risk and Portfolio Optimization #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  19. High-Dimensional Robust Mean Estimation in Nearly-Linear Time
    2018/11/23 by Yu Cheng, Ilias Diakonikolas, Cheng, Yu +3 · 4 citations
    Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Statistical Methods and Inference #Statistics Theory (math.ST)
  20. Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks
    2020/10/08 by Yikai Wu, Wu, Yikai, Xingyu Zhu +7 · 4 citations
    Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
  21. Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
    2017/10/07 by Ge, Rong, Lee, Holden, Risteski, Andrej · 3 citations
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR)
  22. A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
    2021/02/04 by Mo Zhou, Zhou, Mo, Rong Ge +3 · 4 citations
    Computer Science · #Neural Networks and Applications #Advanced Neural Network Applications #Stochastic Gradient Optimization Techniques
  23. Do Transformers Parse while Predicting the Masked Word?
    2023/03/14 by Zhao, Haoyu, Panigrahi, Abhishek, Ge, Rong +1 · 4 citations
    #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  24. Simple, Efficient, and Neural Algorithms for Sparse Coding
    2015/03/02 by Arora, Sanjeev, Ge, Rong, Ma, Tengyu +1 · 2 citations
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE)
  25. Linear Transformers are Versatile In-Context Learners
    2024/02/21 by Vladymyrov, Max, von Oswald, Johannes, Sandler, Mark +1 · 5 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG)
  26. Homotopy Analysis for Tensor PCA
    2016/10/28 by Anima Anandkumar, Yuan Deng, Anandkumar, Anima +5 · 2 citations
    Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications
  27. Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach
    2011/12/08 by Sanjeev Arora, Arora, Sanjeev, Rong Ge +5 · 2 citations
    Physics and Astronomy · Computer Science · #Complex Network Analysis Techniques #Opinion Dynamics and Social Influence #Peer-to-Peer Network Technologies
  28. Online Service with Delay
    2017/08/18 by Azar, Yossi, Ganesh, Arun, Ge, Rong +1 · 2 citations
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences
  29. On the Optimization Landscape of Tensor Decompositions
    2017/06/18 by Ge, Rong, Ma, Tengyu · 2 citations
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Probability (math.PR)
  30. Learning Two-layer Neural Networks with Symmetric Inputs
    2018/10/16 by Ge, Rong, Kuditipudi, Rohith, Li, Zhize +1 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  31. Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
    2019/06/14 by Kuditipudi, Rohith, Wang, Xiang, Lee, Holden +5 · 2 citations
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  32. Online Algorithms with Multiple Predictions
    2022/05/08 by Anand, Keerti, Ge, Rong, Kumar, Amit +1 · 2 citations
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  33. Customizing ML Predictions for Online Algorithms
    2022/05/18 by Keerti Anand, Rong Ge, Anand, Keerti +3 · 2 citations
    Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #Optimization and Search Problems
  34. A Regression Approach to Learning-Augmented Online Algorithms
    2022/05/18 by Keerti Anand, Rong Ge, Anand, Keerti +5 · 2 citations
    Computer Science · Decision Sciences · #Online Learning and Analytics #Advanced Bandit Algorithms Research #Optimization and Search Problems
  35. Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
    2022/10/07 by Xingyu Zhu, Zixuan Wang, Zhu, Xingyu +7 · 2 citations
    Computer Science · Engineering · Physics and Astronomy · #Advanced Memory and Neural Computing #FOS: Computer and information sciences #FOS: Mathematics #I.2.6 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Optimization and Control (math.OC)
  36. Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
    2015/07/08 by Ge, Rong, Zou, James · 1 citation
    #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
  37. On the Local Minima of the Empirical Risk
    2018/03/25 by Jin, Chi, Liu, Lydia T., Ge, Rong +1 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  38. Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization
    2019/05/01 by Ge, Rong, Li, Zhize, Wang, Weiyao +1 · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
  39. Faster Algorithms for High-Dimensional Robust Covariance Estimation
    2019/06/11 by Cheng, Yu, Diakonikolas, Ilias, Ge, Rong +1 · 1 citation
    #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
  40. For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
    2024/02/10 by Chidambaram, Muthu, Ge, Rong · 2 citations
    #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG)
  41. Understanding Deflation Process in Over-parametrized Tensor Decomposition
    2021/06/11 by Rong Ge, Ge, Rong, Yunwei Ren +5 · 1 citation
    Engineering · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications
  42. Depth Separation with Multilayer Mean-Field Networks
    2023/04/03 by Ren, Yunwei, Zhou, Mo, Ge, Rong · 1 citation
    #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC)
  43. Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis
    2016/04/13 by Rong Ge, Chi Jin, Ge, Rong +7 · 1 citation
    Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques #Topological and Geometric Data Analysis
  44. On the Limitations of Temperature Scaling for Distributions with Overlaps
    2023/06/01 by Muthu Chidambaram, Chidambaram, Muthu, Rong Ge +1 · 1 citation
    Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification
  45. How Does Gradient Descent Learn Features -- A Local Analysis for Regularized Two-Layer Neural Networks
    2024/06/03 by Mo Zhou, Rong Ge, Zhou, Mo +1 · 1 citation
    Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
  46. Reassessing How to Compare and Improve the Calibration of Machine Learning Models
    2024/06/06 by Muthu Chidambaram, Chidambaram, Muthu, Rong Ge +1 · 1 citation
    Computer Science · Engineering · #Advanced Data Processing Techniques #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Statistics Theory (math.ST)