3 citations · 7 across the 9 of their papers we have counts for
9 papers
Bayesian Risk-averse Model Predictive Control with Consistency and Stability Guarantees
Yingke Li, Yifan Lin, Enlu Zhou +1
Model Predictive Control (MPC) is a powerful framework for constrained control, but its performance and safety can be severely degraded when the prediction model is learned online…
Bayesian Risk-Sensitive Policy Optimization For MDPs With General Loss Functions
Xiaoshuang Wang, Yifan Lin, Enlu Zhou
Motivated by many application problems, we consider Markov decision processes (MDPs) with a general loss function and unknown parameters. To mitigate the epistemic uncertainty asso…
Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
Yifan Lin, Yuhao Wang, Enlu Zhou
Reinforcement learning provides a mathematical framework for learning-based control, whose success largely depends on the amount of data it can utilize. The efficient utilization o…
Episodic Bayesian Optimal Control with Unknown Randomness Distributions
Alexander Shapiro, Enlu Zhou, Yifan Lin +1
Stochastic optimal control with unknown randomness distributions has been studied for a long time, encompassing robust control, distributionally robust control, and adaptive contro…
Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes
Yifan Lin, Enlu Zhou
We consider infinite-horizon Markov Decision Processes where parameters, such as transition probabilities, are unknown and estimated from data. The popular distributionally robust…
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs
Yifan Lin, Yuhao Wang, Enlu Zhou
In this paper we consider the contextual multi-armed bandit problem for linear payoffs under a risk-averse criterion. At each round, contexts are revealed for each arm, and the dec…
Bayesian Stochastic Gradient Descent for Stochastic Optimization with Streaming Input Data
Tianyi Liu, Yifan Lin, Enlu Zhou
We consider stochastic optimization under distributional uncertainty, where the unknown distributional parameter is estimated from streaming data that arrive sequentially over time…
Bayesian Distributionally Robust Optimization
Alexander Shapiro, Enlu Zhou, Yifan Lin
We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Ba…
Bayesian Risk Markov Decision Processes
Yifan Lin, Yuxuan Ren, Enlu Zhou
We consider finite-horizon Markov Decision Processes where parameters, such as transition probabilities, are unknown and estimated from data. The popular distributionally robust ap…