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- Milind Rao, Tara Javidi, Yonina C. Eldar, Andrea J. Goldsmith
- 2017 IEEE International Conference on Acoustics…
- 2017

We consider the problem of estimating the covariance matrix and the transition matrix of vector autoregressive (VAR) processes from partial measurements. This model encompasses settings where there are limitations in the data acquisition of the underlying measurement systems so that data is lost or corrupted by noise. An estimator for the covariance matrix… (More)

- Milind Rao, Tara Javidi, Yonina C. Eldar, Andrea J. Goldsmith
- 2017 IEEE International Symposium on Information…
- 2017

We consider the problem of estimating the parameters of a vector autoregressive (VAR) process from low-dimensional random projections of the observations. This setting covers the cases where we take compressive measurements of the observations or have limits in the data acquisition process associated with the measurement system and are only able to… (More)

- Xin Jin, Milind Rao, Abbas Kazerouni
- 2017

We solve the contextual question answering problem, which is an essential part in many automated question-answering datasets. Recently the SQuAD dataset [1] was uploaded and there were several deep learning approaches proposed to solve this. We implement a modified version of one of them, the Dynamic Coattention model as well as simple baseline.

This research was supported in part by TI Stanford Graduate Fellowship, and in part by the NSF under CPS Synergy grant 1330081. M. Rao, A. Kipnis, and A. Goldsmith are with the Dept. of Electrical Engineering, Stanford University, Stanford, CA 94305, USA (e-mail: milind@stanford.edu, kipnisal@stanford.edu, andrea@ee.stanford.edu). T. Javidi is with the… (More)

We consider a wind power producer (WPP) participating in a dynamically evolving two settlement power market. We study the utility of energy storage for a WPP in maximizing its expected profit. With random wind and price processes, the optimal forward contract and storage charging/discharging decisions are formulated as solutions of an infinite horizon… (More)

M. Rao and A. Goldsmith are with the Dept. of Electrical Engineering, Stanford University, Stanford, CA 94305, USA (e-mail: milind@stanford.edu, andrea@ee.stanford.edu). T. Javidi is with the Dept. of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA (e-mail: tjavidi@ucsd.edu). Y. Eldar is with the Dept. of… (More)

The problem of learning the parameters of a vector autoregressive (VAR) process from partial random measurements is considered. This setting arises due to missing data or data corrupted by multiplicative bounded noise. We present an estimator of the covariance matrix of the evolving statevector from its partial noisy observations. We analyze the… (More)

This work introduces a class of molecular timing (MT) channels, where information is modulated on the release timing of multiple indistinguishable information particles and decoded from the times of arrival at the receiver. The particles are assumed to have a finite lifetime. The capacity of the MT channel, as well as an upper bound on this capacity, are… (More)

Most machine learning algorithms are theoretically designed and practically applied with the consideration that the training data and test data arise from a common distribution. In several applications in natural language processing ranging from information extraction or sentiment analysis, classification algorithms are trained using a limited set of… (More)

Wind power producers (WPPs) that sell power in forward power markets would like to minimize their operating costs which increase with generation uncertainty. In this work, the value of energy storage for reducing such costs is studied. In particular, profit maximization is considered for a WPP who participates in a two-settlement (forward and real time)… (More)