Zeyuan Allen Zhu

According to our database1, Zeyuan Allen Zhu authored at least 50 papers between 2009 and 2019.

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Bibliography

2019
Nearly linear-time packing and covering LP solvers - Achieving width-independence and -convergence.
Math. Program., 2019

A Convergence Theory for Deep Learning via Over-Parameterization.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Operator scaling via geodesically convex optimization, invariant theory and polynomial identity testing.
Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

Is Q-Learning Provably Efficient?
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Lingering of Gradients: How to Reuse Gradients Over Time.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

NEON2: Finding Local Minima via First-Order Oracles.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Natasha 2: Faster Non-Convex Optimization Than SGD.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Byzantine Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits.
Proceedings of the 35th International Conference on Machine Learning, 2018

Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Katyusha: the first direct acceleration of stochastic gradient methods.
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

Finding approximate local minima faster than gradient descent.
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent.
Proceedings of the 8th Innovations in Theoretical Computer Science Conference, 2017

Near-Optimal Design of Experiments via Regret Minimization.
Proceedings of the 34th International Conference on Machine Learning, 2017

Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU.
Proceedings of the 34th International Conference on Machine Learning, 2017

Faster Principal Component Regression and Stable Matrix Chebyshev Approximation.
Proceedings of the 34th International Conference on Machine Learning, 2017

Doubly Accelerated Methods for Faster CCA and Generalized Eigendecomposition.
Proceedings of the 34th International Conference on Machine Learning, 2017

Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter.
Proceedings of the 34th International Conference on Machine Learning, 2017

Much Faster Algorithms for Matrix Scaling.
Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate.
Proceedings of the 58th IEEE Annual Symposium on Foundations of Computer Science, 2017

2016
Reconstructing Markov processes from independent and anonymous experiments.
Discrete Applied Mathematics, 2016

Expanders via Local Edge Flips.
Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, 2016

Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver.
Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, 2016

Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Even Faster SVD Decomposition Yet Without Agonizing Pain.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Optimal Black-Box Reductions Between Optimization Objectives.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Even Faster Accelerated Coordinate Descent Using Non-Uniform Sampling.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Variance Reduction for Faster Non-Convex Optimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Optimization Algorithms for Faster Computational Geometry.
Proceedings of the 43rd International Colloquium on Automata, Languages, and Programming, 2016

2015
Shorter arithmetization of nondeterministic computations.
Theor. Comput. Sci., 2015

Nearly-Linear Time Positive LP Solver with Faster Convergence Rate.
Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, 2015

Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates.
Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, 2015

Using Optimization to Break the Epsilon Barrier: A Faster and Simpler Width-Independent Algorithm for Solving Positive Linear Programs in Parallel.
Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

Restricted Isometry Property for General p-Norms.
Proceedings of the 31st International Symposium on Computational Geometry, 2015

2014
Flow-Based Algorithms for Local Graph Clustering.
Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 2014

Knightian self uncertainty in the vcg mechanism for unrestricted combinatorial auctions.
Proceedings of the ACM Conference on Economics and Computation, 2014

2013
A simple, combinatorial algorithm for solving SDD systems in nearly-linear time.
Proceedings of the Symposium on Theory of Computing Conference, 2013

A Local Algorithm for Finding Well-Connected Clusters.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Randomized accuracy-aware program transformations for efficient approximate computations.
Proceedings of the 39th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, 2012

Mechanism design with approximate valuations.
Proceedings of the Innovations in Theoretical Computer Science 2012, 2012

2011
Optimal Pricing in Social Networks with Incomplete Information.
Proceedings of the Internet and Network Economics - 7th International Workshop, 2011

2010
A novel click model and its applications to online advertising.
Proceedings of the Third International Conference on Web Search and Web Data Mining, 2010

Asymptotically optimal strategy-proof mechanisms for two-facility games.
Proceedings of the Proceedings 11th ACM Conference on Electronic Commerce (EC-2010), 2010

2009
Inverse Time Dependency in Convex Regularized Learning.
Proceedings of the ICDM 2009, 2009

P-packSVM: Parallel Primal grAdient desCent Kernel SVM.
Proceedings of the ICDM 2009, 2009

A general magnitude-preserving boosting algorithm for search ranking.
Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009

To divide and conquer search ranking by learning query difficulty.
Proceedings of the 18th ACM Conference on Information and Knowledge Management, 2009


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