An Adaptive Mechanism for Accurate Query Answering under Differential Privacy

We propose a novel mechanism for answering sets of count- ing queries under differential privacy. Given a workload of counting queries, the mechanism automatically selects a different set of "strategy" queries to answer privately, using those answers to derive answers to the workload. The main algorithm proposed in this paper approximates the optimal strategy for any workload of linear counting queries. With no cost to the privacy guarantee, the mechanism improves significantly on prior approaches and achieves near-optimal error for many workloads, when applied under (\epsilon, \delta)-differential privacy. The result is an adaptive mechanism which can help users achieve good utility without requiring that they reason carefully about the best formulation of their task.Comment: VLDB2012. arXiv admin note: substantial text overlap with arXiv:1103.136

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences

A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

Individually adapted sequential Bayesian designs for conjoint choice experiments.

In this paper, we propose an efficient individually adapted sequential Bayesian approach for constructing conjoint choice experiments. It uses Bayesian updating, a Bayesian analysis and a Bayesian design criterion for generating choice-set-designs for each individual respondent based on previous answers of that particular respondent. The proposed design approach is compared with two non-adaptive design approaches (the average customization design proposed by Arora and Huber 2001 and the nearly orthogonal design constructed with Sawtooth software) under various degree of response error and respondent heterogeneity. The simulation study shows that the individually adapted sequential Bayesian approach leads to designs which are robust not only to respondent heterogeneity but also to response error. It turns out that the proposed method outperforms the benchmark methods in all scenarios that we have looked at. In particular, for conditions with high response error (the responses from a respondent can hardly provide proper information about the individual-level parameter and is therefore very challenging for individually adapted choice designs), our approach leads to substantially improvement not only in the precision of the parameter estimates but also in the predictive accuracy when the respondent heterogeneity is large. The new method therefore overcomes the limitation of the recently proposed adaptive polyhedral choice-based question design approach by Toubia et al. (2004), whose method performs well only when the response error is low. Furthermore, our study provides compelling evidence that adapting each respondent's choice sets based on the previous responses of that particular respondent in a Bayesian framework enables one to capture more information for the individual- level parameters and therefore also on the population-level parameters. It is shown that it is substantially better to employ the adaptive approach when the response heterogeneity is high.Adaptive Bayesian design; Conjoint choice experiments; Respondent heterogeneity; Response error;