Linqi Song

Demand Side Management in Smart Grids Using a Repeated Game Framework

Demand-side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers the differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested and foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

Online Learning in Large-Scale Contextual Recommender Systems

In this paper, we propose a novel large-scale, context-aware recommender system that provides accurate recommendations, scalability to a large number of diverse users and items, differential services, and does not suffer from “cold start” problems. Our proposed recommendation system relies on a novel algorithm which learns online the item preferences of users based on their click behavior, and constructs online item-cluster trees. The recommendations are then made by choosing an item-cluster level and then selecting an item within that cluster as a recommendation for the user. This approach is able to significantly improve the learning speed when the number of users and items is large, while still providing high recommendation accuracy. Each time a user arrives at the website, the system makes a recommendation based on the estimations of item payoffs by exploiting past context arrivals in a neighborhood of the current users context. It exploits the similarity of contexts to learn how to make better recommendations even when the number and diversity of users and items is large. This also addresses the cold start problem by using the information gained from similar users and items to make recommendations for new users and items. We theoretically prove that the proposed algorithm for item recommendations converges to the optimal item recommendations in the long-run. We also bound the probability of making a suboptimal item recommendation for each user arriving to the system while the system is learning. Experimental results show that our approach outperforms the state-of-the-art algorithms by over 20 percent in terms of click through rates.

Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening

Clinicians need to routinely make management decisions about patients who are at risk for a disease such as breast cancer. This paper presents a novel clinical decision support tool that is capable of helping physicians make diagnostic decisions. We apply this support system to improve the specificity of breast cancer screening and diagnosis. The system utilizes clinical context (e.g., demographics, medical history) to minimize the false positive rates while avoiding false negatives. An online contextual learning algorithm is used to update the diagnostic strategy presented to the physicians over time. We analytically evaluate the diagnostic performance loss of the proposed algorithm, in which the true patient distribution is not known and needs to be learned, as compared with the optimal strategy where all information is assumed known, and prove that the false positive rate of the proposed learning algorithm asymptotically converges to the optimum. In addition, our algorithm also has the important merit that it can provide individualized confidence estimates about the accuracy of the diagnosis recommendation. Moreover, the relevancy of contextual features is assessed, enabling the approach to identify specific contextual features that provide the most value of information in reducing diagnostic errors. Experiments were conducted using patient data collected at a large academic medical center. Our proposed approach outperforms the current clinical practice by 36% in terms of false positive rate given a 2% false negative rate.

Personalized Active Learning for Activity Classification Using Wireless Wearable Sensors

Enabling accurate and low-cost classification of a range of motion activities is important for numerous applications, ranging from disease treatment and in-community rehabilitation of patients to athlete training. This paper proposes a novel contextual online learning method for activity classification based on data captured by low-cost, body-worn inertial sensors, and smartphones. The proposed method is able to address the unique challenges arising in enabling online, personalized and adaptive activity classification without requiring training phase from the individual. Another key challenge of activity classification is that the labels may change over time, as the data as well as the activity to be monitored evolve continuously, and the true label is often costly and difficult to obtain. The proposed algorithm is able to actively learn when to ask for the true label by assessing the benefits and costs of obtaining them. We rigorously characterize the performance of the proposed learning algorithm and Our experiments show that the proposed algorithm outperforms existing algorithms.

Content-type coding

This paper is motivated by the observation that, in many cases, we do not need to serve specific messages, but rather, any message within a content-type. Content-type traffic pervades a host of applications today, ranging from search engines and recommender networks to newsfeeds and advertisement networks. The paper asks a novel question: if there are benefits in designing network and channel codes specifically tailored to contenttype requests. It provides three examples of content-type formulations to argue that, indeed in some cases we can have significant such benefits.

A pliable index coding approach to data shuffling

A promising area that has recently emerged, is on how to use index coding to improve the communication efficiency in distributed computing systems, especially for data shuffling in iterative computations. In this paper, we posit that pliable index coding can offer a more efficient framework for data shuffling, as it can better leverage the many possible shuffling choices to reduce the number of transmissions. We theoretically analyze pliable index coding under data shuffling constraints, and design an hierarchical data-shuffling scheme that uses pliable index coding as a component. We find transmission benefits up to O(ns/m) over index coding, where ns/m is the average number of workers caching a message, and m, n, and s are the numbers of messages, workers, and cache size, respectively.

Non-stationary demand side management method for smart grids

Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. The consumers choose their power consumption patterns according to different prices charged at different times of the day. Importantly, consumers incur discomfort costs from altering their power consumption patterns. Existing works propose stationary strategies for consumers that myopically minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested consumers as a repeated energy scheduling game which foresightedly minimizes their long-term total costs. We then propose a novel methodology for determining optimal nonstationary DSM strategies in which consumers can choose different daily power consumption patterns depending on their preferences and routines, as well as on their past history of actions. We prove that the existing stationary strategies are suboptimal in terms of long-term total billing and discomfort costs and that the proposed strategies are optimal and incentive-compatible (strategy-proof). Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.

Clustering Based Online Learning in Recommender Systems: A Bandit Approach

A big challenge for the design and implementation of large-scale online services is determining what items to recommend to their users. For instance, Netflix makes movie recommendations; Amazon makes product recommendations; and Yahoo! makes webpage recommendations. In these systems, items are recommended based on the characteristics and circumstances of the users, which are provided to the recommender as contexts (e.g., search history, time, and location). The task of building an efficient recommender system is challenging due to the fact that both the item space and the context space are very large. Existing works either focus on a large item space without contexts, large context space with small number of items, or they jointly consider the space of items and contexts together to solve the online recommendation problem. In contrast, we develop an algorithm that does exploration and exploitation in the context space and the item space separately, and develop an algorithm that combines clustering of the items with information aggregation in the context space. Basically, given a users context, our algorithm aggregates its past history over a ball centered on the users context, whose radius decreases at a rate that allows sufficiently accurate estimates of the payoffs such that the recommended payoffs converge to the true (unknown) payoffs. Theoretical results show that our algorithm can achieve a sublinear learning regret in time, namely the payoff difference of the oracle optimal benchmark, where the preferences of users on certain items in certain context are known, and our algorithm, where the information is incomplete. Numerical results show that our algorithm significantly outperforms (over 48%) the existing algorithms in terms of regret.

A polynomial-time algorithm for pliable index coding

Pliable index coding considers a server with m messages and n clients where each client has as side information a subset of the messages. We seek to minimize the number of transmissions the server should make, so that each client receives (any) one message she does not already have. Previous work has shown that the server can achieve this using at most O(log2(n)) transmissions and needs at least Ω(log(n)) transmissions in the worst case, but finding a code of optimal length is NP-hard. In this paper, we design a polynomial-time algorithm that uses less than O(log2(n)) transmissions, i.e., almost worst-case optimal. We also establish a connection between the pliable index coding problem and the minrank problem over a family of mixed matrices.

Context-driven online learning for activity classification in wireless health

Enabling accurate and low-cost classification of a range of motion activities is of significant importance for wireless health through body worn inertial sensors and smartphones, due to the need by healthcare and fitness professonals to monitor exercises for quality and compliance. This paper proposes a novel contextual multi-armed bandits approach for large-scale activity classification. The proposed method is able to address the unique challenges arising from scaling, lack of training data and adaptation by melding context augmentation and continuous online learning into traditional activity classification. We rigorously characterize the performance of the proposed learning algorithm and prove that the learning regret (i.e. reward loss) is sublinear in time, thereby ensuring fast convergence to the optimal reward as well as providing short-term performance guarantees. Our experiments show that the proposed algorithm outperforms existing algorithms in terms of both providing higher classification accuracy as well as lower energy consumption.