Yifei Wang

According to our database, Yifei Wang authored at least 21 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Overparameterized ReLU Neural Networks Learn the Simplest Model: Neural Isometry and Phase Transitions.

Yifei Wang, Yixuan Hua, Emmanuel J. Candès, Mert Pilanci

IEEE Trans. Inf. Theory, March, 2025

2024

Correction to: Sketching the Krylov subspace: faster computation of the entire ridge regularization path.

Yifei Wang, Mert Pilanci

J. Supercomput., January, 2024

Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization.

Yifei Wang, Peng Chen, Mert Pilanci, Wuchen Li

SIAM J. Math. Data Sci., 2024

Randomized Geometric Algebra Methods for Convex Neural Networks.

Yifei Wang, Sungyoon Kim, Paul Chu, Indu Subramaniam, Mert Pilanci

CoRR, 2024

A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features.

Emi Zeger, Yifei Wang, Aaron Mishkin, Tolga Ergen, Emmanuel J. Candès, Mert Pilanci

CoRR, 2024

A Circuit Approach to Constructing Blockchains on Blockchains.

Ertem Nusret Tas, David Tse, Yifei Wang

Proceedings of the 6th Conference on Advances in Financial Technologies, 2024

2023

Sketching the Krylov subspace: faster computation of the entire ridge regularization path.

Yifei Wang, Mert Pilanci

J. Supercomput., November, 2023

A Decomposition Augmented Lagrangian Method for Low-Rank Semidefinite Programming.

Yifei Wang, Kangkang Deng, Haoyang Liu, Zaiwen Wen

SIAM J. Optim., September, 2023

Polynomial-Time Solutions for ReLU Network Training: A Complexity Classification via Max-Cut and Zonotopes.

Yifei Wang, Mert Pilanci

CoRR, 2023

Parallel Deep Neural Networks Have Zero Duality Gap.

Yifei Wang, Tolga Ergen, Mert Pilanci

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference.

Yifei Wang, Peng Chen, Wuchen Li

SIAM/ASA J. Uncertain. Quantification, 2022

Beyond the Best: Estimating Distribution Functionals in Infinite-Armed Bandits.

Yifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao, David Tse

CoRR, 2022

ReLU Neural Networks Learn the Simplest Models: Neural Isometry and Exact Recovery.

Yifei Wang, Yixuan Hua, Emmanuel J. Candès, Mert Pilanci

CoRR, 2022

A stochastic Stein Variational Newton method.

Alex Leviyev, Joshua Chen, Yifei Wang, Omar Ghattas, Aaron Zimmerman

CoRR, 2022

Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits.

Yifei Wang, Tavor Z. Baharav, Yanjun Han, Jiantao Jiao, David Tse

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

The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program.

Yifei Wang, Mert Pilanci

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions.

Yifei Wang, Jonathan Lacotte, Mert Pilanci

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Search Direction Correction with Normalized Gradient Makes First-Order Methods Faster.

Yifei Wang, Zeyu Jia, Zaiwen Wen

SIAM J. Sci. Comput., 2021

Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality.

Jonathan Lacotte, Yifei Wang, Mert Pilanci

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

2020

Information Newton's flow: second-order optimization method in probability space.

Yifei Wang, Wuchen Li

CoRR, 2020

2019

Accelerated Information Gradient flow.

Yifei Wang, Wuchen Li

CoRR, 2019