Rong Ge

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 120

Top Venues

Publications

Muthu Chidambaram, Rong Ge
On the Limitations of Temperature Scaling for Distributions with Overlaps. ICLR (2024)
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 (2024)
Muthu Chidambaram, Rong Ge
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. CoRR (2024)
Mo Zhou, Rong Ge
How Does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. CoRR (2024)
Muthu Chidambaram, Rong Ge
Reassessing How to Compare and Improve the Calibration of Machine Learning Models. CoRR (2024)
Chenwei Wu, Holden Lee, Rong Ge
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation. NeurIPS (2023)
Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. CoRR (2023)
Mo Zhou, Rong Ge
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. ICML (2023)
Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. ICML (2023)
Yunwei Ren, Mo Zhou, Rong Ge
Depth Separation with Multilayer Mean-Field Networks. CoRR (2023)
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)
Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
Do Transformers Parse while Predicting the Masked Word? EMNLP (2023)
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)
Mo Zhou, Rong Ge
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. CoRR (2023)
Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge
Understanding The Robustness of Self-supervised Learning Through Topic Modeling. ICLR (2023)
Yunwei Ren, Mo Zhou, Rong Ge
Depth Separation with Multilayer Mean-Field Networks. ICLR (2023)
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)
Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. ICML (2023)
Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora
Do Transformers Parse while Predicting the Masked Word? CoRR (2023)
Muthu Chidambaram, Rong Ge
A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration. CoRR (2023)
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)
Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. ICLR (2023)
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 (2023)
Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi
Outlier-Robust Sparse Estimation via Non-Convex Optimization. NeurIPS (2022)
Rong Ge, Tengyu Ma
On the optimization landscape of tensor decompositions. Math. Program. 193 (2) (2022)
Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge
Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. CoRR (2022)
Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. CoRR (2022)
Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
Online Algorithms with Multiple Predictions. ICML (2022)
Keerti Anand, Rong Ge, Debmalya Panigrahi
Customizing ML Predictions for Online Algorithms. CoRR (2022)
Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. CoRR (2022)
Zeping Luo, Cindy Weng, Shiyou Wu, Mo Zhou, Rong Ge
One Objective for All Models - Self-supervised Learning for Topic Models. CoRR (2022)
Abraham Frandsen, Rong Ge, Holden Lee
Extracting Latent State Representations with Linear Dynamics from Rich Observations. ICML (2022)
Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge
Towards Understanding the Data Dependency of Mixup-style Training. ICLR (2022)
Abraham Frandsen, Rong Ge
Optimization landscape of Tucker decomposition. Math. Program. 193 (2) (2022)
Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
Online Algorithms with Multiple Predictions. CoRR (2022)
Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
A Regression Approach to Learning-Augmented Online Algorithms. CoRR (2022)
Yu Cheng, Ilias Diakonikolas, Daniel M. Kane, Rong Ge, Shivam Gupta, Mahdi Soltanolkotabi
Outlier-Robust Sparse Estimation via Non-Convex Optimization. CoRR (2021)
Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou
Understanding Deflation Process in Over-parametrized Tensor Decomposition. NeurIPS (2021)
Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski
Efficient sampling from the Bingham distribution. ALT (2021)
Mo Zhou, Rong Ge, Chi Jin
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. CoRR (2021)
Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi
A Regression Approach to Learning-Augmented Online Algorithms. NeurIPS (2021)
Mo Zhou, Rong Ge, Chi Jin
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. COLT (2021)
Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou
Understanding Deflation Process in Over-parametrized Tensor Decomposition. CoRR (2021)
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) (2021)
Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi
Online Service with Delay. ACM Trans. Algorithms 17 (3) (2021)
Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. ICML (2021)
Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge
Towards Understanding the Data Dependency of Mixup-style Training. CoRR (2021)
Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge
Beyond Lazy Training for Over-parameterized Tensor Decomposition. NeurIPS (2020)
Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. CoRR (2020)
Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge
Beyond Lazy Training for Over-parameterized Tensor Decomposition. CoRR (2020)
Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi
High-Dimensional Robust Mean Estimation via Gradient Descent. CoRR (2020)
Rong Ge, Holden Lee, Jianfeng Lu
Estimating normalizing constants for log-concave distributions: algorithms and lower bounds. STOC (2020)
Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie Wang, Rong Ge
Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks. CoRR (2020)
Abraham Frandsen, Rong Ge
Extracting Latent State Representations with Linear Dynamics from Rich Observations. CoRR (2020)
Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski
Efficient sampling from the Bingham distribution. CoRR (2020)
Abraham Frandsen, Rong Ge
Optimization Landscape of Tucker Decomposition. CoRR (2020)
Keerti Anand, Rong Ge, Debmalya Panigrahi
Customizing ML Predictions for Online Algorithms. ICML (2020)
Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi
High-dimensional Robust Mean Estimation via Gradient Descent. ICML (2020)
Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
Spectral Learning on Matrices and Tensors. CoRR (2020)
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)
Abraham Frandsen, Rong Ge
Understanding Composition of Word Embeddings via Tensor Decomposition. ICLR (Poster) (2019)
Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff
Faster Algorithms for High-Dimensional Robust Covariance Estimation. CoRR (2019)
Abraham Frandsen, Rong Ge
Understanding Composition of Word Embeddings via Tensor Decomposition. CoRR (2019)
Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. 12 (5-6) (2019)
Yu Cheng, Ilias Diakonikolas, Rong Ge
High-Dimensional Robust Mean Estimation in Nearly-Linear Time. SODA (2019)
Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff
Faster Algorithms for High-Dimensional Robust Covariance Estimation. COLT (2019)
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 (2019)
Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
Learning Two-layer Neural Networks with Symmetric Inputs. ICLR (Poster) (2019)
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 (2019)
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)
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)
Rong Ge, Holden Lee, Jianfeng Lu
Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds. CoRR (2019)
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR (2019)
Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. CoRR (2019)
Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR (2019)
Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. COLT (2019)
Yu Cheng, Ilias Diakonikolas, Rong Ge
High-Dimensional Robust Mean Estimation in Nearly-Linear Time. CoRR (2018)
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML (2018)
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
Minimizing Nonconvex Population Risk from Rough Empirical Risk. CoRR (2018)
Holden Lee, Andrej Risteski, Rong Ge
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. NeurIPS (2018)
Yu Cheng, Rong Ge
Non-Convex Matrix Completion Against a Semi-Random Adversary. CoRR (2018)
Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. ICML (2018)
Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang
Learning Two-layer Neural Networks with Symmetric Inputs. CoRR (2018)
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Stronger generalization bounds for deep nets via a compression approach. CoRR (2018)
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) (2018)
Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi
Global Convergence of Policy Gradient Methods for Linearized Control Problems. CoRR (2018)
Rong Ge, Jason D. Lee, Tengyu Ma
Learning One-hidden-layer Neural Networks with Landscape Design. ICLR (Poster) (2018)
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
On the Local Minima of the Empirical Risk. NeurIPS (2018)
Yu Cheng, Rong Ge
Non-Convex Matrix Completion Against a Semi-Random Adversary. COLT (2018)
Rong Ge, Holden Lee, Andrej Risteski
Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition. CoRR (2018)
Rong Ge, Holden Lee, Andrej Risteski
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. CoRR (2017)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
Generalization and Equilibrium in Generative Adversarial Nets (GANs). ICML (2017)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang
Generalization and Equilibrium in Generative Adversarial Nets (GANs). CoRR (2017)
Holden Lee, Rong Ge, Andrej Risteski, Tengyu Ma, Sanjeev Arora
On the ability of neural nets to express distributions. CoRR (2017)
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
How to Escape Saddle Points Efficiently. CoRR (2017)
Rong Ge, Jason D. Lee, Tengyu Ma
Learning One-hidden-layer Neural Networks with Landscape Design. CoRR (2017)
Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
Homotopy Analysis for Tensor PCA. COLT (2017)
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
How to Escape Saddle Points Efficiently. ICML (2017)
Rong Ge, Tengyu Ma
On the Optimization Landscape of Tensor Decompositions. NIPS (2017)
Rong Ge, Tengyu Ma
On the Optimization Landscape of Tensor Decompositions. CoRR (2017)

Rong Ge.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Rong Ge