Yifei Wang
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Publications (sorted by dates)

Journal

  • Yifei Wang, Peng Chen, Mert Pilanci, Wuchen Li, Optimal Neural Network Approximation of Wasserstein Gradient Direction via Convex Optimization, to appear on SIAM Journal on Mathematics of Data Science 2024 (paper), (slide), (talk video at SIAM MDS22)

  • Yifei Wang, Mert Pilanci, Sketching the Krylov Subspace: Faster Computation of the Entire Ridge Regularization path, Journal of Supercomputing 2023. (paper), (code)

  • Yifei Wang, Kangkang Deng, Haoyang Li, Zaiwen Wen, A Decomposition Augmented Lagrangian Method for Low-rank Semidefinite Programming, SIAM on Optimization (2023)(paper)

  • Yifei Wang, Peng Chen, Wuchen Li, Projected Wasserstein gradient descent for high-dimensional Bayesian inference, SIAM on Uncertainty Quantification (2022) (paper), (code), (talk video at SIAM MDS22)

  • Yifei Wang, Zeyu Jia, Zaiwen Wen, Search Direction Correction with Normalized Gradient Makes First-Order Methods Faster, SIAM on Scientific Computing (2021), Vol. 43, No. 5, pp. A3184-A3211, (paper), (slide)

  • Yifei Wang, Wuchen Li, Accelerated Information Gradient flow, Journal of Scientific Computing (2022), https:doi.org10.1007s10915-021-01709-3 (paper), (website), (code), (talk video at SIAM MDS22)

Conference

  • Ertem Nusret Tas, David Tse, Yifei Wang, A Circuit Approach to Constructing Blockchains on Blockchains, (paper), Advances in Financial Technologies (AFT) 2024.

  • Yifei Wang, Tolga Ergen, Mert Pilanci, Parallel Deep Neural Networks Have Zero Duality Gap, International Conference on Learning Representations (ICLR) 2023 Poster (paper)

  • Yifei Wang, Tavor Baharav, Yanjun Han, Jiantao Jiao, David Tse, Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits, Conference on Neural Information Processing Systems (NeurIPS) 2022, (paper), (slide), (poster)

  • Yifei Wang, Jonathan Lacotte, Mert Pilanci, The Hidden Convex Optimization Landscape of Two-Layer ReLU Neural Networks: an Exact Characterization of the Optimal Solutions, International Conference on Learning Representations (ICLR) 2022 Oral, (paper), (poster), (slide)

  • Yifei Wang, Mert Pilanci, The Convex Geometry of Backpropagation: Neural Network Gradient Flows Converge to Extreme Points of the Dual Convex Program, International Conference on Learning Representations (ICLR) 2022 Poster, (paper), (poster), (slide)

  • Jonathan Lacotte, Yifei Wang, Mert Pilanci, Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality, International Conference on Machine Learning (ICML) 2021 Poster, (paper), (code), (poster), (slide)

Preprints

  • Yifei Wang, Sungyoon Kim, Paul Chu, Indu Subramaniam, Mert Pilanci, Randomized Geometric Algebra Methods for Convex Neural Networks, (paper)

  • Emi Zeger, Yifei Wang, Aaron Mishkin, Tolga Ergen, Emmanuel Candes, Mert Pilanci, A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features, (paper)

  • Yifei Wang, Mert Pilanci, Polynomial-Time Solutions for ReLU Network Training: A Complexity Classification via Max-Cut and Zonotopes, (paper)

  • Yifei Wang, Yixuan Hua, Emmanuel Candes, Mert Pilanci, Overparameterized ReLU Neural Networks Learn the Simplest Models: Neural Isometry and Exact Recovery, (paper), (code)

  • Alex Leviyev, Joshua Chen, Yifei Wang, Omar Ghattas, Aaron Zimmerman, A stochastic Stein variational Newton Method, (paper), (code)