Enlu Zhou

Orcid: 0000-0001-5399-6508

Affiliations:
  • Georgia Institute of Technology, Atlanta, GA, USA


According to our database1, Enlu Zhou authored at least 78 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data.
SIAM J. Optim., March, 2024

Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate.
CoRR, 2024

2023
Bayesian Distributionally Robust Optimization.
SIAM J. Optim., June, 2023

Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes.
CoRR, 2023

Input Data Collection Versus Simulation: Simultaneous Resource Allocation.
Proceedings of the Winter Simulation Conference, 2023

Reusing Historical Observations in Natural Policy Gradient.
Proceedings of the Winter Simulation Conference, 2023

Bayesian Risk-Averse Q-Learning with Streaming Observations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Cognition Difference-Based Dynamic Trust Network for Distributed Bayesian Data Fusion.
IROS, 2023

Integrated Task and Motion Planning for Process-aware Source Seeking.
Proceedings of the American Control Conference, 2023

2022
Bayesian Learning Model Predictive Control for Process-Aware Source Seeking.
IEEE Control. Syst. Lett., 2022

Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs.
CoRR, 2022

Fixed Budget Ranking and Selection with Streaming Input Data.
Proceedings of the Winter Simulation Conference, 2022

Bayesian Risk Markov Decision Processes.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Multi-Objective Bayesian Optimization Under Input Noise.
Proceedings of the International Conference on Machine Learning, 2022

Risk-Aware Model Predictive Control Enabled by Bayesian Learning.
Proceedings of the American Control Conference, 2022

Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Solving Bayesian risk optimization via nested stochastic gradient estimation.
IISE Trans., 2021

A Bayesian Risk Approach to MDPs with Parameter Uncertainty.
CoRR, 2021

Dynamic Sampling Policy For Subset Selection.
Proceedings of the Winter Simulation Conference, 2021

A Bayesian Approach to Online Simulation Optimization with Streaming Input Data.
Proceedings of the Winter Simulation Conference, 2021

Contextual Ranking and Selection with Gaussian Processes.
Proceedings of the Winter Simulation Conference, 2021

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Risk Quantification in Stochastic Simulation under Input Uncertainty.
ACM Trans. Model. Comput. Simul., 2020

Domination Measure: A New Metric for Solving Multiobjective Optimization.
INFORMS J. Comput., 2020

Simulation Optimization by Reusing Past Replications: Don't Be Afraid of Dependence.
Proceedings of the Winter Simulation Conference, 2020

A Nested Simulation Optimization Approach for Portfolio Selection.
Proceedings of the Winter Simulation Conference, 2020

Bayesian Optimization of Risk Measures.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Towards Understanding the Importance of Noise in Training Neural Networks.
CoRR, 2019

Fixed Confidence Ranking and Selection Under Input Uncertainty.
Proceedings of the 2019 Winter Simulation Conference, 2019

Towards Understanding the Importance of Shortcut Connections in Residual Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Toward Understanding the Importance of Noise in Training Neural Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Solving the Dual Problems of Dynamic Programs via Regression.
IEEE Trans. Autom. Control., 2018

Weakly Coupled Dynamic Program: Information and Lagrangian Relaxations.
IEEE Trans. Autom. Control., 2018

A Bayesian Risk Approach to Data-driven Stochastic Optimization: Formulations and Asymptotics.
SIAM J. Optim., 2018

Experimental design and model reduction in systems biology.
Quant. Biol., 2018

Simulation optimization of risk measures with adaptive risk levels.
J. Glob. Optim., 2018

Gradient-Based Adaptive Stochastic Search for Simulation Optimization Over Continuous Space.
INFORMS J. Comput., 2018

Analyzing and provably improving fixed budget ranking and selection algorithms.
CoRR, 2018

Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations.
CoRR, 2018

Online Quantification of input uncertainty for parametric Models.
Proceedings of the 2018 Winter Simulation Conference, 2018

Provably Improving the Optimal Computing Budget Allocation Algorithm.
Proceedings of the 2018 Winter Simulation Conference, 2018

Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017
The empirical likelihood approach to quantifying uncertainty in sample average approximation.
Oper. Res. Lett., 2017

A Lagrangian search method for the P-median problem.
J. Glob. Optim., 2017

Robust ranking and selection with optimal computing budget allocation.
Autom., 2017

Ranking and selection under input uncertainty: A budget allocation formulation.
Proceedings of the 2017 Winter Simulation Conference, 2017

2016
Optimizing Conditional Value-at-Risk via gradient-based adaptive stochastic search.
Proceedings of the Winter Simulation Conference, 2016

Preface.
Proceedings of the Winter Simulation Conference, 2016

Optimal computing budget allocation with input uncertainty.
Proceedings of the Winter Simulation Conference, 2016

2015
Information Relaxation and Dual Formulation of Controlled Markov Diffusions.
IEEE Trans. Autom. Control., 2015

Population model-based optimization.
J. Glob. Optim., 2015

Estimation of conditional value-at-risk for input uncertainty with budget allocation.
Proceedings of the 2015 Winter Simulation Conference, 2015

Simulation optimization when facing input uncertainty.
Proceedings of the 2015 Winter Simulation Conference, 2015

A sequential experiment design for input uncertainty quantification in stochastic simulation.
Proceedings of the 2015 Winter Simulation Conference, 2015

Quantifying uncertainty in sample average approximation.
Proceedings of the 2015 Winter Simulation Conference, 2015

A model-based approach to multi-objective optimization.
Proceedings of the 2015 Winter Simulation Conference, 2015

2014
Model-Based Annealing Random Search with Stochastic Averaging.
ACM Trans. Model. Comput. Simul., 2014

Gradient-Based Adaptive Stochastic Search for Non-Differentiable Optimization.
IEEE Trans. Autom. Control., 2014

Particle Filtering Framework for a Class of Randomized Optimization Algorithms.
IEEE Trans. Autom. Control., 2014

Simulation optimization via gradient-based stochastic search.
Proceedings of the 2014 Winter Simulation Conference, 2014

An iterative algorithm for sampling from manifolds.
Proceedings of the 2014 Winter Simulation Conference, 2014

2013
Optimal Stopping Under Partial Observation: Near-Value Iteration.
IEEE Trans. Autom. Control., 2013

Optimal Stopping of Partially Observable Markov Processes: A Filtering-Based Duality Approach.
IEEE Trans. Autom. Control., 2013

Sequential Monte Carlo simulated annealing.
J. Glob. Optim., 2013

True martingales for upper bounds on Bermudan option prices under jump-diffusion processes.
Proceedings of the Winter Simulations Conference: Simulation Making Decisions in a Complex World, 2013

Population model-based optimization with sequential Monte Carlo.
Proceedings of the Winter Simulations Conference: Simulation Making Decisions in a Complex World, 2013

2012
Efficient Selection of a Set of Good Enough Designs With Complexity Preference.
IEEE Trans Autom. Sci. Eng., 2012

Combining gradient-based optimization with stochastic search.
Proceedings of the Winter Simulation Conference, 2012

Parameterized penalties in the dual representation of Markov decision processes.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

2011
Pricing American options under partial observation of stochastic volatility.
Proceedings of the Winter Simulation Conference 2011, 2011

2010
Solving Continuous-State POMDPs via Density Projection.
IEEE Trans. Autom. Control., 2010

A new population-based simulated annealing algorithm.
Proceedings of the 2010 Winter Simulation Conference, 2010

Efficient simulation budget allocation for selecting the best set of simplest good enough designs.
Proceedings of the 2010 Winter Simulation Conference, 2010

Simulation method for solving hybrid influence diagrams in decision making.
Proceedings of the 2010 Winter Simulation Conference, 2010

2009
Particle Filtering for Stochastic Control and Global Optimization.
PhD thesis, 2009

A Numerical Method for Financial Decision Problems under Stochastic Volatility.
Proceedings of the 2009 Winter Simulation Conference, 2009

2008
A particle filtering framework for randomized optimization algorithms.
Proceedings of the 2008 Winter Simulation Conference, Global Gateway to Discovery, 2008

A density projection approach to dimension reduction for continuous-state POMDPs.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008


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