Chi Jin - Optimization

Optimization

(α-β order) denotes alphabetical ordering, * denotes equal contribution.

Tuning-Free Stochastic Optimization [arXiv]

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method. [arXiv]

Efficient displacement convex optimization with particle gradient descent [arXiv]

Faster Federated Optimization under Second-order Similarity [arXiv]

Minimax Optimization with Smooth Algorithmic Adversaries [arXiv]

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points [arXiv]

Near-Optimal Algorithms for Minimax Optimization [arXiv]

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? [arXiv]

On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems [arXiv]

Sampling Can Be Faster Than Optimization [arXiv]

Stochastic Cubic Regularization for Fast Nonconvex Optimization [arXiv]

Nilesh Tripuraneni*, Mitchell Stern*, Chi Jin, Jeffrey Regier, Michael I. Jordan
(Oral) Neural Information Processing Systems (NIPS) 2018.

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent [arXiv]

Gradient Descent Can Take Exponential Time to Escape Saddle Points [arXiv]

Simon S. Du, Chi Jin, Jason D. Lee, Michael I. Jordan, Barnabas Poczos, Aarti Singh
Neural Information Processing Systems (NIPS) 2017.

How to Escape Saddle Points Efficiently [arXiv] [blog]

No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis [arXiv]

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot [arXiv]

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences [arXiv]

Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael I. Jordan
Neural Information Processing Systems (NIPS) 2016.

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent [arXiv]

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm [arXiv]

(α-β order) Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
Conference of Learning Theory (COLT) 2016.

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis [arXiv]

(α-β order) Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
International Conference on Machine Learning (ICML) 2016.

Faster Eigenvector Computation via Shift-and-Invert Preconditioning [arXiv]

(α-β order) Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
International Conference on Machine Learning (ICML) 2016.

Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition [arXiv]

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