THU QUANTA at TAC 2008 QA and RTE Track. - Fangtao Li; Zhicheng Zheng; Yang Tang; Fan Bu; Rong Ge 001; Xiaoyan Zhu 001; Xian Zhang 006; Minlie Huang (TAC, 2008)

New Results on Simple Stochastic Games. - Decheng Dai; Rong Ge 001 (ISAAC, 2009)

Computational Complexity and Information Asymmetry in Financial Products (Extended Abstract). - Sanjeev Arora; Boaz Barak; Markus Brunnermeier; Rong Ge 001 (ICS, 2010)

Learning Parities with Structured Noise. - Sanjeev Arora; Rong Ge 001 (Electron. Colloquium Comput. Complex., 2010)

New Tools for Graph Coloring. - Sanjeev Arora; Rong Ge 001 (APPROX-RANDOM, 2011)

New Algorithms for Learning in Presence of Errors. - Sanjeev Arora; Rong Ge 001 (ICALP (1), 2011)

Another Sub-exponential Algorithm for the Simple Stochastic Game. - Decheng Dai; Rong Ge 001 (Algorithmica, 2011)

Computational complexity and information asymmetry in financial products. - Sanjeev Arora; Boaz Barak; Markus Brunnermeier; Rong Ge 001 (Commun. ACM, 2011)

Learning Topic Models - Going beyond SVD. - Sanjeev Arora; Rong Ge 001; Ankur Moitra (FOCS, 2012)

"Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders". - Sanjeev Arora; Rong Ge 001; Ankur Moitra; Sushant Sachdeva (NIPS, 2012)

Finding overlapping communities in social networks: toward a rigorous approach. - Sanjeev Arora; Rong Ge 001; Sushant Sachdeva; Grant Schoenebeck (EC, 2012)

A Tensor Spectral Approach to Learning Mixed Membership Community Models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade (COLT, 2013)

Towards a Better Approximation for Sparsest Cut? - Sanjeev Arora; Rong Ge 001; Ali Kemal Sinop (FOCS, 2013)

A Practical Algorithm for Topic Modeling with Provable Guarantees. - Sanjeev Arora; Rong Ge 001; Yonatan Halpern; David M. Mimno; Ankur Moitra; David A. Sontag; Yichen Wu; Michael Zhu (ICML (2), 2013)

Provable Algorithms for Machine Learning Problems - Rong Ge 001 (2013)

Minimal realization problem for Hidden Markov Models. - Qingqing Huang 001; Rong Ge 001; Sham M. Kakade; Munther A. Dahleh (Allerton, 2014)

New Algorithms for Learning Incoherent and Overcomplete Dictionaries. - Sanjeev Arora; Rong Ge 001; Ankur Moitra (COLT, 2014)

Provable Bounds for Learning Some Deep Representations. - Sanjeev Arora; Aditya Bhaskara; Rong Ge 001; Tengyu Ma 001 (ICML, 2014)

Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates. - Animashree Anandkumar; Rong Ge 001; Majid Janzamin (CoRR, 2014)

Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings. - Animashree Anandkumar; Rong Ge 001; Majid Janzamin (CoRR, 2014)

Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models. - Anima Anandkumar; Rong Ge 001; Majid Janzamin (CoRR, 2014)

More Algorithms for Provable Dictionary Learning. - Sanjeev Arora; Aditya Bhaskara; Rong Ge 001; Tengyu Ma 001 (CoRR, 2014)

A tensor approach to learning mixed membership community models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade (J. Mach. Learn. Res., 2014)

Tensor decompositions for learning latent variable models. - Animashree Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (J. Mach. Learn. Res., 2014)

Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). - Anima Anandkumar; Rong Ge 001; Daniel J. Hsu; Sham M. Kakade; Matus Telgarsky (ALT, 2015)

Decomposing Overcomplete 3rd Order Tensors using Sum-of-Squares Algorithms. - Rong Ge 001; Tengyu Ma 001 (APPROX-RANDOM, 2015)

Learning Overcomplete Latent Variable Models through Tensor Methods. - Animashree Anandkumar; Rong Ge 001; Majid Janzamin (COLT, 2015)

Simple, Efficient, and Neural Algorithms for Sparse Coding. - Sanjeev Arora; Rong Ge 001; Tengyu Ma 001; Ankur Moitra (COLT, 2015)

Competing with the Empirical Risk Minimizer in a Single Pass. - Roy Frostig; Rong Ge 001; Sham M. Kakade; Aaron Sidford (COLT, 2015)

Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition. - Rong Ge 001; Furong Huang; Chi Jin 001; Yang Yuan (COLT, 2015)

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. - Roy Frostig; Rong Ge 001; Sham M. Kakade; Aaron Sidford (ICML, 2015)

Intersecting Faces: Non-negative Matrix Factorization With New Guarantees. - Rong Ge 001; James Zou 002 (ICML, 2015)

Learning Mixtures of Gaussians in High Dimensions. - Rong Ge 001; Qingqing Huang 001; Sham M. Kakade (STOC, 2015)

Provable ICA with Unknown Gaussian Noise, and Implications for Gaussian Mixtures and Autoencoders. - Sanjeev Arora; Rong Ge 001; Ankur Moitra; Sushant Sachdeva (Algorithmica, 2015)

Efficient approaches for escaping higher order saddle points in non-convex optimization. - Animashree Anandkumar; Rong Ge 001 (COLT, 2016)

Provable Algorithms for Inference in Topic Models. - Sanjeev Arora; Rong Ge 001; Frederic Koehler; Tengyu Ma 001; Ankur Moitra (ICML, 2016)

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. - Rong Ge 001; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Rich Component Analysis. - Rong Ge 001; James Zou 002 (ICML, 2016)

Matrix Completion has No Spurious Local Minimum. - Rong Ge 001; Jason D. Lee; Tengyu Ma 001 (NIPS, 2016)

Homotopy Method for Tensor Principal Component Analysis. - Anima Anandkumar; Yuan Deng; Rong Ge 001; Hossein Mobahi (CoRR, 2016)

Computing a Nonnegative Matrix Factorization - Provably. - Sanjeev Arora; Rong Ge 001; Ravi Kannan; Ankur Moitra (SIAM J. Comput., 2016)

Minimal Realization Problems for Hidden Markov Models. - Qingqing Huang 001; Rong Ge 001; Sham M. Kakade; Munther A. Dahleh (IEEE Trans. Signal Process., 2016)

Homotopy Analysis for Tensor PCA. - Anima Anandkumar; Yuan Deng; Rong Ge 001; Hossein Mobahi (COLT, 2017)

On the Ability of Neural Nets to Express Distributions. - Holden Lee; Rong Ge 001; Tengyu Ma 001; Andrej Risteski; Sanjeev Arora (COLT, 2017)

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis. - Rong Ge 001; Chi Jin 001; Yi Zheng (ICML, 2017)

Generalization and Equilibrium in Generative Adversarial Nets (GANs). - Sanjeev Arora; Rong Ge 001; Yingyu Liang; Tengyu Ma 001; Yi Zhang 074 (ICML, 2017)

How to Escape Saddle Points Efficiently. - Chi Jin 001; Rong Ge 001; Praneeth Netrapalli; Sham M. Kakade; Michael I. Jordan (ICML, 2017)

Provable learning of noisy-OR networks. - Sanjeev Arora; Rong Ge 001; Tengyu Ma 001; Andrej Risteski (STOC, 2017)

Analyzing Tensor Power Method Dynamics in Overcomplete Regime. - Animashree Anandkumar; Rong Ge 001; Majid Janzamin (J. Mach. Learn. Res., 2017)

Non-Convex Matrix Completion Against a Semi-Random Adversary. - Yu Cheng 002; Rong Ge 001 (COLT, 2018)

Learning One-hidden-layer Neural Networks with Landscape Design. - Rong Ge 001; Jason D. Lee; Tengyu Ma 001 (ICLR (Poster), 2018)

Stronger Generalization Bounds for Deep Nets via a Compression Approach. - Sanjeev Arora; Rong Ge 001; Behnam Neyshabur; Yi Zhang 074 (ICML, 2018)

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator. - Maryam Fazel; Rong Ge 001; Sham M. Kakade; Mehran Mesbahi (ICML, 2018)

On the Local Minima of the Empirical Risk. - Chi Jin 001; Lydia T. Liu; Rong Ge 001; Michael I. Jordan (NeurIPS, 2018)

Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. - Holden Lee; Andrej Risteski; Rong Ge 001 (NeurIPS, 2018)

Learning topic models - provably and efficiently. - Sanjeev Arora; Rong Ge 001; Yoni Halpern; David M. Mimno; Ankur Moitra; David A. Sontag; Yichen Wu; Michael Zhu (Commun. ACM, 2018)

Global Convergence of Policy Gradient Methods for Linearized Control Problems. - Maryam Fazel; Rong Ge 001; Sham M. Kakade; Mehran Mesbahi (CoRR, 2018)

Minimizing Nonconvex Population Risk from Rough Empirical Risk. - Chi Jin 001; Lydia T. Liu; Rong Ge 001; Michael I. Jordan (CoRR, 2018)

Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition. - Rong Ge 001; Holden Lee; Andrej Risteski (CoRR, 2018)

Open Problem: Do Good Algorithms Necessarily Query Bad Points? - Rong Ge 001; Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Dheeraj M. Nagaraj; Praneeth Netrapalli (COLT, 2019)

Faster Algorithms for High-Dimensional Robust Covariance Estimation. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; David P. Woodruff (COLT, 2019)

Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization. - Rong Ge 001; Zhize Li; Weiyao Wang 001; Xiang Wang 011 (COLT, 2019)

Understanding Composition of Word Embeddings via Tensor Decomposition. - Abraham Frandsen; Rong Ge 001 (ICLR (Poster), 2019)

Learning Two-layer Neural Networks with Symmetric Inputs. - Rong Ge 001; Rohith Kuditipudi; Zhize Li; Xiang Wang 011 (ICLR (Poster), 2019)

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. - Rong Ge 001; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli (NeurIPS, 2019)

Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. - Rohith Kuditipudi; Xiang Wang 011; Holden Lee; Yi Zhang 074; Zhiyuan Li 005; Wei Hu 014; Rong Ge 001; Sanjeev Arora (NeurIPS, 2019)

High-Dimensional Robust Mean Estimation in Nearly-Linear Time. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001 (SODA, 2019)

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

Stochastic Gradient Descent Escapes Saddle Points Efficiently. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (CoRR, 2019)

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. - Rong Ge 001; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli (CoRR, 2019)

Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently. - Rong Ge 001; Runzhe Wang; Haoyu Zhao (CoRR, 2019)

Spectral Learning on Matrices and Tensors. - Majid Janzamin; Rong Ge 001; Jean Kossaifi; Anima Anandkumar (Found. Trends Mach. Learn., 2019)

Topic Models and Nonnegative Matrix Factorization. - Rong Ge 001; Ankur Moitra (Beyond the Worst-Case Analysis of Algorithms, 2020)

High-dimensional Robust Mean Estimation via Gradient Descent. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Mahdi Soltanolkotabi (ICML, 2020)

Customizing ML Predictions for Online Algorithms. - Keerti Anand; Rong Ge 001; Debmalya Panigrahi (ICML, 2020)

Beyond Lazy Training for Over-parameterized Tensor Decomposition. - Xiang Wang 011; Chenwei Wu 002; Jason D. Lee; Tengyu Ma 001; Rong Ge 001 (NeurIPS, 2020)

Estimating normalizing constants for log-concave distributions: algorithms and lower bounds. - Rong Ge 001; Holden Lee; Jianfeng Lu 001 (STOC, 2020)

Extracting Latent State Representations with Linear Dynamics from Rich Observations. - Abraham Frandsen; Rong Ge 001 (CoRR, 2020)

Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks. - Yikai Wu 001; Xingyu Zhu 003; Chenwei Wu 002; Annie N. Wang; Rong Ge 001 (CoRR, 2020)

Efficient sampling from the Bingham distribution. - Rong Ge 001; Holden Lee; Jianfeng Lu 001; Andrej Risteski (ALT, 2021)

A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network. - Mo Zhou; Rong Ge 001; Chi Jin 001 (COLT, 2021)

Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. - Xiang Wang 011; Shuai Yuan; Chenwei Wu 002; Rong Ge 001 (ICML, 2021)

A Regression Approach to Learning-Augmented Online Algorithms. - Keerti Anand; Rong Ge 001; Amit Kumar 001; Debmalya Panigrahi (NeurIPS, 2021)

Understanding Deflation Process in Over-parametrized Tensor Decomposition. - Rong Ge 001; Yunwei Ren; Xiang Wang 011; Mo Zhou (NeurIPS, 2021)

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. - Chi Jin 001; Praneeth Netrapalli; Rong Ge 001; Sham M. Kakade; Michael I. Jordan (J. ACM, 2021)

Online Service with Delay. - Yossi Azar; Arun Ganesh; Rong Ge 001; Debmalya Panigrahi (ACM Trans. Algorithms, 2021)

Towards Understanding the Data Dependency of Mixup-style Training. - Muthu Chidambaram; Xiang Wang 011; Yuzheng Hu; Chenwei Wu 002; Rong Ge 001 (ICLR, 2022)

Online Algorithms with Multiple Predictions. - Keerti Anand; Rong Ge 001; Amit Kumar 001; Debmalya Panigrahi (ICML, 2022)

Extracting Latent State Representations with Linear Dynamics from Rich Observations. - Abraham Frandsen; Rong Ge 001; Holden Lee (ICML, 2022)

Outlier-Robust Sparse Estimation via Non-Convex Optimization. - Yu Cheng 002; Ilias Diakonikolas; Rong Ge 001; Shivam Gupta 002; Daniel Kane 001; Mahdi Soltanolkotabi (NeurIPS, 2022)

One Objective for All Models - Self-supervised Learning for Topic Models. - Zeping Luo; Cindy Weng; Shiyou Wu; Mo Zhou; Rong Ge 001 (CoRR, 2022)

Optimization landscape of Tucker decomposition. - Abraham Frandsen; Rong Ge 001 (Math. Program., 2022)

On the optimization landscape of tensor decompositions. - Rong Ge 001; Tengyu Ma 001 (Math. Program., 2022)

Do Transformers Parse while Predicting the Masked Word? - Haoyu Zhao; Abhishek Panigrahi; Rong Ge 001; Sanjeev Arora (EMNLP, 2023)

The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models. - Chenwei Wu 002; Li Erran Li; Stefano Ermon; Patrick Haffner; Rong Ge 001; Zaiwei Zhang (ICBINB, 2023)

Understanding Edge-of-Stability Training Dynamics with a Minimalist Example. - Xingyu Zhu 003; Zixuan Wang; Xiang Wang 011; Mo Zhou; Rong Ge 001 (ICLR, 2023)

Plateau in Monotonic Linear Interpolation - A "Biased" View of Loss Landscape for Deep Networks. - Xiang Wang 011; Annie N. Wang; Mo Zhou; Rong Ge 001 (ICLR, 2023)

Understanding The Robustness of Self-supervised Learning Through Topic Modeling. - Zeping Luo; Shiyou Wu; Cindy Weng; Mo Zhou; Rong Ge 001 (ICLR, 2023)

Depth Separation with Multilayer Mean-Field Networks. - Yunwei Ren; Mo Zhou; Rong Ge 001 (ICLR, 2023)

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. - Muthu Chidambaram; Xiang Wang 011; Chenwei Wu 002; Rong Ge 001 (ICML, 2023)

Hiding Data Helps: On the Benefits of Masking for Sparse Coding. - Muthu Chidambaram; Chenwei Wu 002; Yu Cheng 002; Rong Ge 001 (ICML, 2023)

Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression. - Mo Zhou; Rong Ge 001 (ICML, 2023)

Connecting Pre-trained Language Model and Downstream Task via Properties of Representation. - Chenwei Wu 002; Holden Lee; Rong Ge 001 (NeurIPS, 2023)

Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. - Alex Damian; Eshaan Nichani; Rong Ge 001; Jason D. Lee (NeurIPS, 2023)

Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing. - Shuyao Li 001; Yu Cheng 002; Ilias Diakonikolas; Jelena Diakonikolas; Rong Ge 001; Stephen J. Wright 001 (NeurIPS, 2023)

A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration. - Muthu Chidambaram; Rong Ge 001 (CoRR, 2023)

On the Limitations of Temperature Scaling for Distributions with Overlaps. - Muthu Chidambaram; Rong Ge 001 (ICLR, 2024)

How does Gradient Descent Learn Features - A Local Analysis for Regularized Two-Layer Neural Networks. - Mo Zhou; Rong Ge 001 (NeurIPS, 2024)

Reassessing How to Compare and Improve the Calibration of Machine Learning Models. - Muthu Chidambaram; Rong Ge 001 (ICLR, 2025)

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. - Muthu Chidambaram; Rong Ge 001 (ICLR, 2025)