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Optimal Ranking of Test Items using the Rasch Model
(2016)2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)We study the problem of ranking test items, i.e., the ordering of items according to the amount of information they provide on the latent trait of the respondents. We focus on educational applications, where instructors are interested in ranking questions so as to select a small set of informative questions in order to efficiently assess the students' understanding on the course material. Using the Rasch model for modeling student responses, ...Conference Paper -
Recovery Guarantees for Restoration and Separation of Approximately Sparse Signals
(2011)2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton)In this paper, we present performance guarantees for the recovery and separation of signals that are approximately sparse in some general (i.e., basis, frame, over-complete, or incomplete) dictionary but corrupted by a combination of measurement noise and interference that is sparse in a second general dictionary. Applications covered by this framework include the restoration of signals impaired by impulse noise, narrowband interference, ...Conference Paper -
Time-varying Learning and Content Analytics via Sparse Factor Analysis
(2014)Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningWe propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for educational applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA) that jointly traces learner concept knowledge over time, analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, ...Conference Paper -
CS-MUVI: Video compressive sensing for spatial-multiplexing cameras
(2012)2012 IEEE International Conference on Computational Photography (ICCP)Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes ...Conference Paper -
Matrix Recovery from Quantized and Corrupted Measurements
(2014)2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)This paper deals with the recovery of an unknown, low-rank matrix from quantized and (possibly) corrupted measurements of a subset of its entries. We develop statistical models and corresponding (multi-)convex optimization algorithms for quantized matrix completion (Q-MC) and quantized robust principal component analysis (Q-RPCA). In order to take into account the quantized nature of the available data, we jointly learn the underlying ...Conference Paper -
A Robust and Efficient Method to Recover Neural Events from Noisy and Corrupted Data
(2013)2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)In a variety of neural data analysis problems, “neural events” such as action potentials (APs) or post-synaptic potentials (PSPs), must be recovered from noisy and possibly corrupted measurements. For instance, in calcium imaging, an AP or group of APs generate a stereotyped calcium signal with a quick rise time and slow decay. In this work, we develop a general-purpose method for: (i) learning a template waveform that signifies the ...Conference Paper -
Bayesian Pairwise Collaboration Detection in Educational Datasets
(2013)2013 IEEE Global Conference on Signal and Information ProcessingOnline education affords the opportunity to revolutionize learning by providing access to high-quality educational resources at low costs. The recent popularity of so-called MOOCs (massive open online courses) further accelerates this trend. However, these exciting advancements result in several challenges for the course instructors. Among these challenges is the detection of collaboration between learners on online tests or take-home ...Conference Paper -
Subspace Clustering with Dense Representations
(2013)2013 IEEE International Conference on Acoustics, Speech and Signal ProcessingUnions of subspaces have recently been shown to provide a compact nonlinear signal model for collections of high-dimensional data, such as large collections of images or videos. In this paper, we introduce a novel data-driven algorithm for learning unions of subspaces directly from a collection of data; our approach is based upon forming minimum l2 -norm (least-squares) representations of a signal with respect to other signals in the ...Conference Paper -
Dictionary learning from sparsely corrupted or compressed signals
(2012)2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)In this paper, we investigate dictionary learning (DL) from sparsely corrupted or compressed signals. We consider three cases: I) the training signals are corrupted, and the locations of the corruptions are known, II) the locations of the sparse corruptions are unknown, and III) DL from compressed measurements, as it occurs in blind compressive sensing. We develop two efficient DL algorithms that are capable of learning dictionaries from ...Conference Paper -