
Boosting Method in Approximately Solving Linear Programming with Fast Online Algorithm
In this paper, we develop a new algorithm combining the idea of “boostin...
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Distributed stochastic optimization with large delays
One of the most widely used methods for solving largescale stochastic o...
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Fisher Markets with Linear Constraints: Equilibrium Properties and Efficient Distributed Algorithms
The Fisher market is one of the most fundamental models for resource all...
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Robustifying Conditional Portfolio Decisions via Optimal Transport
We propose a datadriven portfolio selection model that integrates side ...
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The Symmetry between Arms and Knapsacks: A PrimalDual Approach for Bandits with Knapsacks
In this paper, we study the bandits with knapsacks (BwK) problem and dev...
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Distributionally Robust Local Nonparametric Conditional Estimation
Conditional estimation given specific covariate values (i.e., local cond...
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A MeanField Theory for Learning the Schönberg Measure of Radial Basis Functions
We develop and analyze a projected particle Langevin optimization method...
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Markets for Efficient Public Good Allocation with Social Distancing
Public goods are often either overconsumed in the absence of regulatory...
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Markets for Efficient Public Good Allocation
Public goods are often either overconsumed in the absence of regulatory...
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Computations and Complexities of Tarski's Fixed Points and Supermodular Games
We consider two models of computation for Tarski's order preserving func...
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Sequential Batch Learning in FiniteAction Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual band...
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Diagonal Preconditioning: Theory and Algorithms
Diagonal preconditioning has been a staple technique in optimization and...
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Simple and Fast Algorithm for Binary Integer and Online Linear Programming
In this paper, we develop a simple and fast online algorithm for solving...
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Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds
We study an online linear programming (OLP) problem under a random input...
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Solving Discounted Stochastic TwoPlayer Games with NearOptimal Time and Sample Complexity
In this paper, we settle the sampling complexity of solving discounted t...
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On a Randomized MultiBlock ADMM for Solving Selected Machine Learning Problems
The Alternating Direction Method of Multipliers (ADMM) has now days gain...
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Toward Solving 2TBSG Efficiently
2TBSG is a twoplayer game model which aims to find Nash equilibriums a...
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Interiorpoint Methods Strike Back: Solving the Wasserstein Barycenter Problem
Computing the Wasserstein barycenter of a set of probability measures un...
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HighDimensional Learning under Approximate Sparsity: A Unifying Framework for Nonsmooth Learning and Regularized Neural Networks
Highdimensional statistical learning (HDSL) has been widely applied in ...
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A polynomial time log barrier method for problems with nonconvex constraints
Interior point methods (IPMs) that handle nonconvex constraints such as ...
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Statistical ranking and combinatorial Hodge theory
We propose a number of techniques for obtaining a global ranking from da...
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Yinyu Ye
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K. T. Li Chair Professor of Engineering Management Science & Engineering and, by courtesy, Electrical Engineering, Director, Industrial Affiliates Program, MS&E, Department of Management Science and Engineering at Stanford University