A 'smart microgrid' refers to a distribution network for electrical energy, starting from electricity generation to its transmission and storage with the ability to respond to dynamic changes in energy supply ...
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The Internet is plagued with congestion problems of growing severity which are worst at peak periods. In this paper, we compare two schemes that incentivize users to shift part of their usage from the peak-time to the...
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We introduce new online and batch algorithms that are robust to data with missing features, a situation that arises in many practical applications. In the online setup, we allow for the comparison hypothesis to change...
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We introduce new online and batch algorithms that are robust to data with missing features, a situation that arises in many practical applications. In the online setup, we allow for the comparison hypothesis to change as a function of the subset of features that is observed on any given round, extending the standard setting where the comparison hypothesis is fixed throughout. In the batch setup, we present a convex relaxation of a non-convex problem to jointly estimate an imputation function, used to fill in the values of missing features, along with the classification hypothesis. We prove regret bounds in the online setting and Rademacher complexity bounds for the batch i.i.d. setting. The algorithms are tested on several ucI datasets, showing superior performance over baseline imputation methods.
We propose a block-diagonal structured model order reduction (BDSM) scheme for fast power grid analysis. Compared with existing power grid model order reduction (MOR) methods, BDSM has several advantages. First, unlik...
This work introduces Divide-Factor-Combine (DFC), a parallel divide-and-conquer framework for noisy matrix factorization. DFC divides a large-scale matrix factorization task into smaller subproblems, solves each subpr...
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ISBN:
(纸本)9781618395993
This work introduces Divide-Factor-Combine (DFC), a parallel divide-and-conquer framework for noisy matrix factorization. DFC divides a large-scale matrix factorization task into smaller subproblems, solves each subproblem in parallel using an arbitrary base matrix factorization algorithm, and combines the sub-problem solutions using techniques from randomized matrix approximation. Our experiments with collaborative filtering, video background modeling, and simulated data demonstrate the near-linear to super-linear speed-ups attainable with this approach. Moreover, our analysis shows that DFC enjoys high-probability recovery guarantees comparable to those of its base algorithm.
Using least-squares with an l_(1)-norm penalty is well-known to encourage sparse solutions. In this article, we propose an algorithm that performs online least-squares estimation of a time varying system with a l_(1)-...
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ISBN:
(纸本)9781612848006
Using least-squares with an l_(1)-norm penalty is well-known to encourage sparse solutions. In this article, we propose an algorithm that performs online least-squares estimation of a time varying system with a l_(1)-norm penalty on the variations of the state estimate, leading to state estimates that exhibit few "jumps" over time. The algorithm analytically computes a path to update the state estimate as a new observation becomes available. The algorithm performs computationally efficient and numerically robust state estimation for time varying systems in which the dynamics are slow compared to the sampling rate. We use the algorithm for arterial traffic estimation with streaming probe vehicle data provided by the Mobile Millennium system and show a significant improvement in the estimation capabilities compared to a baseline model of traffic estimation. The estimation framework filters out the inherent noise of traffic dynamics and improves the interpretability and accuracy of the results. Results from an implementation in San Francisco on a network of more than 800 links using a fleet of 500 taxis sending their location every minute illustrate the possibility to use the algorithm to solve important practical estimation problems.
In this paper we present an exploratory pair-programming study aimed at investigating how programmers use a tool and language designed for performing crosscutting change tasks. Through a qualitative analysis of the pa...
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We propose a general framework for the design of securities markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient markets tailored to an arbitrar...
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Under many distributed protocols, the prescribed behavior for participants is to behave greedily, i.e., to repeatedly "best respond" to the others' actions. We present recent work (Proc. ICS'11) wher...
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We present a new framework for auction design and analysis that we term "best-response auctions". We use this framework to show that the simple and myopic best-response dynamics converge to the VCG outcome a...
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