Personalized optimization with user's feedback
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Publication:2665405
DOI10.1016/j.automatica.2021.109767OpenAlexW3174613136MaRDI QIDQ2665405
Julien Monteil, Emiliano Dall'Anese, Andrea Simonetto, Andrey Bernstein
Publication date: 19 November 2021
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1905.00775
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- Posterior consistency of Gaussian process prior for nonparametric binary regression
- Rates of contraction of posterior distributions based on Gaussian process priors
- Recovering Markov models from closed-loop data
- Markov decision processes with sequential sensor measurements
- Vehicular platoons in cyclic interconnections
- On \(\mathcal{L}_\infty\) string stability of nonlinear bidirectional asymmetric heterogeneous platoon systems
- Convex Optimization: Algorithms and Complexity
- Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations
- Online Distributed Convex Optimization on Dynamic Networks
- A Parametric Nonconvex Decomposition Algorithm for Real-Time and Distributed NMPC
- Online Learning and Online Convex Optimization
- Information Consistency of Nonparametric Gaussian Process Methods
- Level Sets and Extrema of Random Processes and Fields
- Prospect Theory: An Analysis of Decision under Risk
- Distributed Charging Control of Electric Vehicles Using Online Learning
- Fast Convergence Rates for Distributed Non-Bayesian Learning
- Distributed Online Optimization in Dynamic Environments Using Mirror Descent
- Decentralized Online Learning With Kernels
- Prediction-Correction Algorithms for Time-Varying Constrained Optimization
- Online Learning With Inexact Proximal Online Gradient Descent Algorithms
- Online Primal-Dual Methods With Measurement Feedback for Time-Varying Convex Optimization
- Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization
- An Incentive-Based Online Optimization Framework for Distribution Grids
- Gaussian Processes for Learning and Control: A Tutorial with Examples
- Douglas--Rachford Splitting and ADMM for Nonconvex Optimization: Tight Convergence Results
- Individual Regret Bounds for the Distributed Online Alternating Direction Method of Multipliers
- Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
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