← Authors

Rong Ge

69 papers · 2012–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (18) 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (13) 🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (4) 🏠 Conference Loyalist (20) 🀝 Dynamic Duo (11) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (21) πŸ† Keyword Champion πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (14) ❓ The Questioner (3) ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (57) πŸš€ Conference Pioneer πŸ’Ž Century Club (69)

Conferences

ICML (20) NIPS (17) COLT (14) ICLR (12) JMLR (3) EMNLP (2) ALT (1)

Top co-authors

Sanjeev Arora (11) Xiang Wang (10) Tengyu Ma (9) Mo Zhou (8) Sham M. Kakade (6) Muthu Chidambaram (6) Chenwei Wu (6) Chi Jin (6) Animashree Anandkumar (6) Yu Cheng (6)

Research topics

Privacy (1)

Keywords

non-convex optimization (9) tensor decomposition (8) gradient descent (7) stochastic gradient descent (7) latent variable model (7) representation learning (5) neural network (5) online algorithm (4) nonconvex optimization (4) sparse coding (4) local minima (3) empirical risk minimization (3) spectral method (3) high-dimensional statistics (3) second-order stationary point (3) learning theory (3) matrix completion (3) feature learning (3) matrix factorization (3) sample complexity (3)

Papers

Task Descriptors Help Transformers Learn Linear Models In-Context ICLR 2025
Reassessing How to Compare and Improve the Calibration of Machine Learning Models ICLR 2025
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation ICLR 2025
Linear Transformers are Versatile In-Context Learners NIPS 2024
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods EMNLP 2024
On the Limitations of Temperature Scaling for Distributions with Overlaps ICLR 2024
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input NIPS 2024
How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks NIPS 2024
Connecting Pre-trained Language Model and Downstream Task via Properties of Representation NIPS 2023
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing NIPS 2023
Plateau in Monotonic Linear Interpolation --- A "Biased" View of Loss Landscape for Deep Networks ICLR 2023
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup ICML 2023
Depth Separation with Multilayer Mean-Field Networks ICLR 2023
Hiding Data Helps: On the Benefits of Masking for Sparse Coding ICML 2023
Understanding The Robustness of Self-supervised Learning Through Topic Modeling ICLR 2023
Do Transformers Parse while Predicting the Masked Word? EMNLP 2023
Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression ICML 2023
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example ICLR 2023
Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models NIPS 2023
Towards Understanding the Data Dependency of Mixup-style Training ICLR 2022
Outlier-Robust Sparse Estimation via Non-Convex Optimization NIPS 2022
Extracting Latent State Representations with Linear Dynamics from Rich Observations ICML 2022
Online Algorithms with Multiple Predictions ICML 2022
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network COLT 2021
Understanding Deflation Process in Over-parametrized Tensor Decomposition NIPS 2021
A Regression Approach to Learning-Augmented Online Algorithms NIPS 2021
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach ICML 2021
Efficient sampling from the Bingham distribution ALT 2021
Beyond Lazy Training for Over-parameterized Tensor Decomposition NIPS 2020
Customizing ML Predictions for Online Algorithms ICML 2020
High-dimensional Robust Mean Estimation via Gradient Descent ICML 2020
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares NIPS 2019
Faster Algorithms for High-Dimensional Robust Covariance Estimation COLT 2019
Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization COLT 2019
Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets NIPS 2019
Learning Two-layer Neural Networks with Symmetric Inputs ICLR 2019
Understanding Composition of Word Embeddings via Tensor Decomposition ICLR 2019
Learning One-hidden-layer Neural Networks with Landscape Design ICLR 2018
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo NIPS 2018
On the Local Minima of the Empirical Risk NIPS 2018
Non-Convex Matrix Completion Against a Semi-Random Adversary COLT 2018
Stronger Generalization Bounds for Deep Nets via a Compression Approach ICML 2018
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator ICML 2018
Homotopy Analysis for Tensor PCA COLT 2017
Generalization and Equilibrium in Generative Adversarial Nets (GANs) ICML 2017
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis ICML 2017
How to Escape Saddle Points Efficiently ICML 2017
Analyzing Tensor Power Method Dynamics in Overcomplete Regime JMLR 2017
On the Optimization Landscape of Tensor Decompositions NIPS 2017
On the Ability of Neural Nets to Express Distributions COLT 2017
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis ICML 2016
Provable Algorithms for Inference in Topic Models ICML 2016
Matrix Completion has No Spurious Local Minimum NIPS 2016
Efficient approaches for escaping higher order saddle points in non-convex optimization COLT 2016
Rich Component Analysis ICML 2016
Escaping From Saddle Points β€” Online Stochastic Gradient for Tensor Decomposition COLT 2015
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization ICML 2015
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees ICML 2015
Competing with the Empirical Risk Minimizer in a Single Pass COLT 2015
Simple, Efficient, and Neural Algorithms for Sparse Coding COLT 2015
Learning Overcomplete Latent Variable Models through Tensor Methods COLT 2015
New Algorithms for Learning Incoherent and Overcomplete Dictionaries COLT 2014
A Tensor Approach to Learning Mixed Membership Community Models JMLR 2014
Tensor Decompositions for Learning Latent Variable Models JMLR 2014
Provable Bounds for Learning Some Deep Representations ICML 2014
A Tensor Spectral Approach to Learning Mixed Membership Community Models COLT 2013
A Practical Algorithm for Topic Modeling with Provable Guarantees ICML 2013
Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders NIPS 2012