Rong Ge

According to our database, Rong Ge authored at least 110 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Reassessing How to Compare and Improve the Calibration of Machine Learning Models.

Muthu Chidambaram, Rong Ge

CoRR, 2024

How Does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks.

Mo Zhou, Rong Ge

CoRR, 2024

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation.

Muthu Chidambaram, Rong Ge

CoRR, 2024

On the Limitations of Temperature Scaling for Distributions with Overlaps.

Muthu Chidambaram, Rong Ge

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration.

Muthu Chidambaram, Rong Ge

CoRR, 2023

Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing.

Shuyao Li, Yu Cheng, Ilias Diakonikolas, Jelena Diakonikolas, Rong Ge, Stephen J. Wright

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models.

Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Connecting Pre-trained Language Model and Downstream Task via Properties of Representation.

Chenwei Wu, Holden Lee, Rong Ge

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression.

Mo Zhou, Rong Ge

Proceedings of the International Conference on Machine Learning, 2023

Hiding Data Helps: On the Benefits of Masking for Sparse Coding.

Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge

Proceedings of the International Conference on Machine Learning, 2023

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup.

Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge

Proceedings of the International Conference on Machine Learning, 2023

Depth Separation with Multilayer Mean-Field Networks.

Yunwei Ren, Mo Zhou, Rong Ge

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Understanding The Robustness of Self-supervised Learning Through Topic Modeling.

Zeping Luo, Shiyou Wu, Cindy Weng, Mo Zhou, Rong Ge

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks.

Xiang Wang, Annie N. Wang, Mo Zhou, Rong Ge

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Understanding Edge-of-Stability Training Dynamics with a Minimalist Example.

Xingyu Zhu, Zixuan Wang, Xiang Wang, Mo Zhou, Rong Ge

Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models.

Chenwei Wu, Li Erran Li, Stefano Ermon, Patrick Haffner, Rong Ge, Zaiwei Zhang

Proceedings of the Proceedings on "I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models" at NeurIPS 2023 Workshops, 2023

Do Transformers Parse while Predicting the Masked Word?

Haoyu Zhao, Abhishek Panigrahi, Rong Ge, Sanjeev Arora

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

On the optimization landscape of tensor decompositions.

Rong Ge, Tengyu Ma

Math. Program., 2022

Optimization landscape of Tucker decomposition.

Abraham Frandsen, Rong Ge

Math. Program., 2022

One Objective for All Models - Self-supervised Learning for Topic Models.

Zeping Luo, Cindy Weng, Shiyou Wu, Mo Zhou, Rong Ge

CoRR, 2022

Outlier-Robust Sparse Estimation via Non-Convex Optimization.

Yu Cheng, Ilias Diakonikolas, Rong Ge, Shivam Gupta, Daniel Kane, Mahdi Soltanolkotabi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Extracting Latent State Representations with Linear Dynamics from Rich Observations.

Abraham Frandsen, Rong Ge, Holden Lee

Proceedings of the International Conference on Machine Learning, 2022

Online Algorithms with Multiple Predictions.

Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi

Proceedings of the International Conference on Machine Learning, 2022

Towards Understanding the Data Dependency of Mixup-style Training.

Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Online Service with Delay.

Yossi Azar, Arun Ganesh, Rong Ge, Debmalya Panigrahi

ACM Trans. Algorithms, 2021

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points.

Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan

J. ACM, 2021

Understanding Deflation Process in Over-parametrized Tensor Decomposition.

Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Regression Approach to Learning-Augmented Online Algorithms.

Keerti Anand, Rong Ge, Amit Kumar, Debmalya Panigrahi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Guarantees for Tuning the Step Size using a Learning-to-Learn Approach.

Xiang Wang, Shuai Yuan, Chenwei Wu, Rong Ge

Proceedings of the 38th International Conference on Machine Learning, 2021

A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network.

Mo Zhou, Rong Ge, Chi Jin

Proceedings of the Conference on Learning Theory, 2021

Efficient sampling from the Bingham distribution.

Rong Ge, Holden Lee, Jianfeng Lu, Andrej Risteski

Proceedings of the Algorithmic Learning Theory, 2021

2020

Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks.

Yikai Wu, Xingyu Zhu, Chenwei Wu, Annie N. Wang, Rong Ge

CoRR, 2020

Extracting Latent State Representations with Linear Dynamics from Rich Observations.

Abraham Frandsen, Rong Ge

CoRR, 2020

Estimating normalizing constants for log-concave distributions: algorithms and lower bounds.

Rong Ge, Holden Lee, Jianfeng Lu

Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Beyond Lazy Training for Over-parameterized Tensor Decomposition.

Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Customizing ML Predictions for Online Algorithms.

Keerti Anand, Rong Ge, Debmalya Panigrahi

Proceedings of the 37th International Conference on Machine Learning, 2020

High-dimensional Robust Mean Estimation via Gradient Descent.

Yu Cheng, Ilias Diakonikolas, Rong Ge, Mahdi Soltanolkotabi

Proceedings of the 37th International Conference on Machine Learning, 2020

Topic Models and Nonnegative Matrix Factorization.

Rong Ge, Ankur Moitra

Proceedings of the Beyond the Worst-Case Analysis of Algorithms, 2020

2019

Spectral Learning on Matrices and Tensors.

Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar

Found. Trends Mach. Learn., 2019

Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently.

Rong Ge, Runzhe Wang, Haoyu Zhao

CoRR, 2019

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure.

Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

CoRR, 2019

Stochastic Gradient Descent Escapes Saddle Points Efficiently.

Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan

CoRR, 2019

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm.

Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan

CoRR, 2019

High-Dimensional Robust Mean Estimation in Nearly-Linear Time.

Yu Cheng, Ilias Diakonikolas, Rong Ge

Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets.

Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Rong Ge, Sanjeev Arora

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares.

Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Two-layer Neural Networks with Symmetric Inputs.

Rong Ge, Rohith Kuditipudi, Zhize Li, Xiang Wang

Proceedings of the 7th International Conference on Learning Representations, 2019

Understanding Composition of Word Embeddings via Tensor Decomposition.

Abraham Frandsen, Rong Ge

Proceedings of the 7th International Conference on Learning Representations, 2019

Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization.

Rong Ge, Zhize Li, Weiyao Wang, Xiang Wang

Proceedings of the Conference on Learning Theory, 2019

Faster Algorithms for High-Dimensional Robust Covariance Estimation.

Yu Cheng, Ilias Diakonikolas, Rong Ge, David P. Woodruff

Proceedings of the Conference on Learning Theory, 2019

Open Problem: Do Good Algorithms Necessarily Query Bad Points?

Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli

Proceedings of the Conference on Learning Theory, 2019

2018

Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition.

Rong Ge, Holden Lee, Andrej Risteski

CoRR, 2018

Minimizing Nonconvex Population Risk from Rough Empirical Risk.

Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan

CoRR, 2018

Global Convergence of Policy Gradient Methods for Linearized Control Problems.

Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi

CoRR, 2018

Learning topic models - provably and efficiently.

Sanjeev Arora, Rong Ge, Yoni Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu

Commun. ACM, 2018

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

Holden Lee, Andrej Risteski, Rong Ge

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On the Local Minima of the Empirical Risk.

Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator.

Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi

Proceedings of the 35th International Conference on Machine Learning, 2018

Stronger Generalization Bounds for Deep Nets via a Compression Approach.

Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning One-hidden-layer Neural Networks with Landscape Design.

Rong Ge, Jason D. Lee, Tengyu Ma

Proceedings of the 6th International Conference on Learning Representations, 2018

Non-Convex Matrix Completion Against a Semi-Random Adversary.

Yu Cheng, Rong Ge

Proceedings of the Conference On Learning Theory, 2018

2017

Analyzing Tensor Power Method Dynamics in Overcomplete Regime.

Animashree Anandkumar, Rong Ge, Majid Janzamin

J. Mach. Learn. Res., 2017

Provable learning of noisy-OR networks.

Sanjeev Arora, Rong Ge, Tengyu Ma, Andrej Risteski

Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

How to Escape Saddle Points Efficiently.

Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan

Proceedings of the 34th International Conference on Machine Learning, 2017

Generalization and Equilibrium in Generative Adversarial Nets (GANs).

Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang

Proceedings of the 34th International Conference on Machine Learning, 2017

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

Rong Ge, Chi Jin, Yi Zheng

Proceedings of the 34th International Conference on Machine Learning, 2017

On the Ability of Neural Nets to Express Distributions.

Holden Lee, Rong Ge, Tengyu Ma, Andrej Risteski, Sanjeev Arora

Proceedings of the 30th Conference on Learning Theory, 2017

Homotopy Analysis for Tensor PCA.

Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Minimal Realization Problems for Hidden Markov Models.

Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh

IEEE Trans. Signal Process., 2016

Computing a Nonnegative Matrix Factorization - Provably.

Sanjeev Arora, Rong Ge, Ravi Kannan, Ankur Moitra

SIAM J. Comput., 2016

Homotopy Method for Tensor Principal Component Analysis.

Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi

CoRR, 2016

Matrix Completion has No Spurious Local Minimum.

Rong Ge, Jason D. Lee, Tengyu Ma

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Rich Component Analysis.

Rong Ge, James Zou

Proceedings of the 33nd International Conference on Machine Learning, 2016

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

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Provable Algorithms for Inference in Topic Models.

Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra

Proceedings of the 33nd International Conference on Machine Learning, 2016

Efficient approaches for escaping higher order saddle points in non-convex optimization.

Animashree Anandkumar, Rong Ge

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders.

Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva

Algorithmica, 2015

Learning Mixtures of Gaussians in High Dimensions.

Rong Ge, Qingqing Huang, Sham M. Kakade

Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, 2015

Intersecting Faces: Non-negative Matrix Factorization With New Guarantees.

Rong Ge, James Zou

Proceedings of the 32nd International Conference on Machine Learning, 2015

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization.

Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford

Proceedings of the 32nd International Conference on Machine Learning, 2015

Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition.

Rong Ge, Furong Huang, Chi Jin, Yang Yuan

Proceedings of The 28th Conference on Learning Theory, 2015

Competing with the Empirical Risk Minimizer in a Single Pass.

Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford

Proceedings of The 28th Conference on Learning Theory, 2015

Simple, Efficient, and Neural Algorithms for Sparse Coding.

Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra

Proceedings of The 28th Conference on Learning Theory, 2015

Learning Overcomplete Latent Variable Models through Tensor Methods.

Animashree Anandkumar, Rong Ge, Majid Janzamin

Proceedings of The 28th Conference on Learning Theory, 2015

Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms.

Rong Ge, Tengyu Ma

Proceedings of the Approximation, 2015

Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT).

Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky

Proceedings of the Algorithmic Learning Theory - 26th International Conference, 2015

2014

Tensor decompositions for learning latent variable models.

Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky

J. Mach. Learn. Res., 2014

A tensor approach to learning mixed membership community models.

Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade

J. Mach. Learn. Res., 2014

More Algorithms for Provable Dictionary Learning.

Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma

CoRR, 2014

Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models.

Anima Anandkumar, Rong Ge, Majid Janzamin

CoRR, 2014

Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings.

Animashree Anandkumar, Rong Ge, Majid Janzamin

CoRR, 2014

Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates.

Animashree Anandkumar, Rong Ge, Majid Janzamin

CoRR, 2014

Provable Bounds for Learning Some Deep Representations.

Sanjeev Arora, Aditya Bhaskara, Rong Ge, Tengyu Ma

Proceedings of the 31th International Conference on Machine Learning, 2014

New Algorithms for Learning Incoherent and Overcomplete Dictionaries.

Sanjeev Arora, Rong Ge, Ankur Moitra

Proceedings of The 27th Conference on Learning Theory, 2014

Minimal realization problem for Hidden Markov Models.

Qingqing Huang, Rong Ge, Sham M. Kakade, Munther A. Dahleh

Proceedings of the 52nd Annual Allerton Conference on Communication, 2014

2013

Provable Algorithms for Machine Learning Problems

Rong Ge

PhD thesis, 2013

A Practical Algorithm for Topic Modeling with Provable Guarantees.

Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu

Proceedings of the 30th International Conference on Machine Learning, 2013

Towards a Better Approximation for Sparsest Cut?

Sanjeev Arora, Rong Ge, Ali Kemal Sinop

Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

A Tensor Spectral Approach to Learning Mixed Membership Community Models.

Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade

Proceedings of the COLT 2013, 2013

2012

Finding overlapping communities in social networks: toward a rigorous approach.

Sanjeev Arora, Rong Ge, Sushant Sachdeva, Grant Schoenebeck

Proceedings of the 13th ACM Conference on Electronic Commerce, 2012

"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders".

Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning Topic Models - Going beyond SVD.

Sanjeev Arora, Rong Ge, Ankur Moitra

Proceedings of the 53rd Annual IEEE Symposium on Foundations of Computer Science, 2012

2011

Computational complexity and information asymmetry in financial products.

Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge

Commun. ACM, 2011

Another Sub-exponential Algorithm for the Simple Stochastic Game.

Decheng Dai, Rong Ge

Algorithmica, 2011

New Algorithms for Learning in Presence of Errors.

Sanjeev Arora, Rong Ge

Proceedings of the Automata, Languages and Programming - 38th International Colloquium, 2011

New Tools for Graph Coloring.

Sanjeev Arora, Rong Ge

Proceedings of the Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2011

2010

Learning Parities with Structured Noise.

Sanjeev Arora, Rong Ge

Electron. Colloquium Comput. Complex., 2010

Computational Complexity and Information Asymmetry in Financial Products (Extended Abstract).

Sanjeev Arora, Boaz Barak, Markus Brunnermeier, Rong Ge

Proceedings of the Innovations in Computer Science, 2010

2009

New Results on Simple Stochastic Games.

Decheng Dai, Rong Ge

Proceedings of the Algorithms and Computation, 20th International Symposium, 2009

2008

THU QUANTA at TAC 2008 QA and RTE Track.

Fangtao Li, Zhicheng Zheng, Yang Tang, Fan Bu, Rong Ge, Xiaoyan Zhu, Xian Zhang, Minlie Huang

Proceedings of the First Text Analysis Conference, 2008