Yinlam Chow

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In this paper we address the problem of decision making within a Markov decision process (MDP) framework where risk and modeling errors are taken into account. Our approach is to minimize a risk-sensitive conditional-value-at-risk (CVaR) objective, as opposed to a standard risk-neutral expectation. We refer to such problem as CVaR MDP. Our first(More)
— In this paper we present a dynamic programing approach to stochastic optimal control problems with dynamic, time-consistent risk constraints. Constrained stochastic optimal control problems, which naturally arise when one has to consider multiple objectives, have been extensively investigated in the past 20 years; however, in most formulations, the(More)
We provide sampling-based algorithms for optimization under a coherent-risk objective. The class of coherent-risk measures is widely accepted in finance and operations research, among other fields, and encompasses popular risk-measures such as the conditional value at risk (CVaR) and the mean-semi-deviation. Our approach is suitable for problems in which(More)
In this paper we consider a stochastic deployment problem, where a robotic swarm is tasked with the objective of positioning at least one robot at each of a set of pre-assigned targets while meeting a temporal deadline. Travel times and failure rates are stochastic but related, inasmuch as failure rates increase with speed. To maximize chances of success(More)
The integration of intermittent and volatile renewable energy resources requires increased flexibility in the operation of the electric grid. Storage, broadly speaking, provides the flexibility of shifting energy over time; network, on the other hand, provides the flexibility of shifting energy over geographical locations. The optimal control of general(More)
In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger worst-case(More)
— In this paper, we present a discretization algorithm for the solution of stochastic optimal control problems with dynamic, time-consistent risk constraints. Previous works have shown that such problems can be cast as Markov decision problems on an augmented state space where a " constrained " form of Bellman's recursion can be applied. However, even if(More)
We consider one-way vehicle sharing systems where customers can pick a car at one station and drop it off at another (e.g., Zipcar, Car2Go). We aim to optimize the distribution of cars, and quality of service, by pricing rentals appropriately. However, with highly uncertain demands and other uncertain parameters (e.g., pickup and drop-off location, time,(More)