A parameter-free hedging algorithm

We study the problem of decision-theoretic online learning (DTOL). Motivated by practical applications, we focus on DTOL when the number of actions is very large. Previous algorithms for learning in this framework have a tunable learning rate parameter, and a barrier to using online-learning in practical applications is that it is not understood how to set this parameter optimally, particularly when the number of actions is large. In this paper, we offer a clean solution by proposing a novel and completely parameter-free algorithm for DTOL. We introduce a new notion of regret, which is more natural for applications with a large number of actions. We show that our algorithm achieves good performance with respect to this new notion of regret; in addition, it also achieves performance close to that of the best bounds achieved by previous algorithms with optimally-tuned parameters, according to previous notions of regret.Comment: Updated Versio

Molecular basis for modulation of the p53 target selectivity by KLF4

The tumour suppressor p53 controls transcription of various genes involved in apoptosis, cell-cycle arrest, DNA repair and metabolism. However, its DNA-recognition specificity is not nearly sufficient to explain binding to specific locations in vivo. Here, we present evidence that KLF4 increases the DNA-binding affinity of p53 through the formation of a loosely arranged ternary complex on DNA. This effect depends on the distance between the response elements of KLF4 and p53. Using nuclear magnetic resonance and fluorescence techniques, we found that the amino-terminal domain of p53 interacts with the KLF4 zinc fingers and mapped the interaction site. The strength of this interaction was increased by phosphorylation of the p53 N-terminus, particularly on residues associated with regulation of cell-cycle arrest genes. Taken together, the cooperative binding of KLF4 and p53 to DNA exemplifies a regulatory mechanism that contributes to p53 target selectivity

Quantifying the Cost of Learning in Queueing Systems

Queueing systems are widely applicable stochastic models with use cases in communication networks, healthcare, service systems, etc. Although their optimal control has been extensively studied, most existing approaches assume perfect knowledge of system parameters. Of course, this assumption rarely holds in practice where there is parameter uncertainty, thus motivating a recent line of work on bandit learning for queueing systems. This nascent stream of research focuses on the asymptotic performance of the proposed algorithms. In this paper, we argue that an asymptotic metric, which focuses on late-stage performance, is insufficient to capture the intrinsic statistical complexity of learning in queueing systems which typically occurs in the early stage. Instead, we propose the Cost of Learning in Queueing (CLQ), a new metric that quantifies the maximum increase in time-averaged queue length caused by parameter uncertainty. We characterize the CLQ of a single-queue multi-server system, and then extend these results to multi-queue multi-server systems and networks of queues. In establishing our results, we propose a unified analysis framework for CLQ that bridges Lyapunov and bandit analysis, which could be of independent interest

End-of-Horizon Load Balancing Problems: Algorithms and Insights

Effective load balancing is at the heart of many applications in operations. Often tackled via the balls-into-bins paradigm, seminal results have shown that a limited amount of flexibility goes a long way in order to maintain (approximately) balanced loads throughout the decision-making horizon. This paper is motivated by the fact that balance across time is too stringent a requirement for some applications; rather, the only desideratum is approximate balance at the end of the horizon. In this work we design ``limited-flexibility'' algorithms for three instantiations of the end-of-horizon balance problem: the balls-into-bins problem, opaque selling strategies for inventory management, and parcel delivery for e-commerce fulfillment. For the balls-into-bins model, we show that a simple policy which begins exerting flexibility toward the end of the time horizon (i.e., when $\Theta\left(\sqrt{T\log T}\right)$ periods remain), suffices to achieve an approximately balanced load (i.e., a maximum load within ${O}(1)$ of the average load). Moreover, with just a small amount of adaptivity, a threshold policy achieves the same result, while only exerting flexibility in ${O}\left(\sqrt{T}\right)$ periods, matching a natural lower bound. We then adapt these algorithms to develop order-wise optimal policies for the opaque selling problem. Finally, we show via a data-driven case study that the adaptive policy designed for the balls-into-bins model can be modified to (i) achieve approximate balance at the end of the horizon and (ii) yield significant cost savings relative to policies which either never exert flexibility, or exert flexibility aggressively enough to achieve anytime balance. The unifying motivation behind our algorithms is the observation that exerting flexibility at the beginning of the horizon is likely wasted when system balance is only evaluated at the end

Adjoint-based sensitivity analysis of ignition in a turbulent reactive shear layer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143014/1/6.2017-0846.pd