Paramveer S. Dhillon

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NeurRecently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks. However, most current approaches are slow to train, do not model the context of the word, and lack theoretical grounding. In this paper, we present a new learning(More)
Anatomical and behavioural work on primates has shown bilateral innervation of axial and proximal limb muscles, and contralateral control of distal limb muscles. The following study examined if a clear boundary exists between the distal and proximal upper limb muscles that are controlled contralaterally or bilaterally. The right motor cortical area(More)
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a principal component analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares(More)
We contribute a novel and interpretable dimensionality reduction strategy, eigenanatomy, that is tuned for neuroimaging data. The method approximates the eigendecomposition of an image set with basis functions (the eigenanatomy vectors) that are sparse, unsigned and are anatomically clustered. We employ the eigenanatomy vectors as anatomical predictors to(More)
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an elegant way of incorporating arbitrary sparsity patterns in the feature space by using two-part MDL coding schemes. We present MIC based models for the problems of grouped feature(More)
We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations (p n). The standard way to solve ridge regression in this setting works in the dual space and gives a running time of O(n 2 p). Our algorithm Subsampled Randomized Hadamard Transform-Dual Ridge Regression (SRHT-DRR) runs in time O(np(More)
Recently there has been substantial interest in using spectral methods to learn generative sequence models like HMMs. Spectral methods are attractive as they provide globally consistent estimates of the model parameters and are very fast and scalable, unlike EM methods , which can get stuck in local minima. In this paper, we present a novel extension of(More)
An important cue to high level scene understanding is to analyze the objects in the scene and their behavior and interactions. In this paper, we study the problem of classification of activities in videos, as this is an integral component of any scene understanding system, and present a novel approach for recognizing human action categories in videos by(More)
We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data (n p). We propose three methods which solve the big data problem by subsampling the covariance matrix using either a single or two stage estimation. All three run in the order of size of input i.e. O(np) and our best method, Uluru, gives an error bound of(More)
In this paper we propose a new approach for semi-supervised structured output learning. Our approach uses relaxed labeling on un-labeled data to deal with the combinatorial nature of the label space and further uses domain constraints to guide the learning. Since the overall objective is non-convex, we alternate between the optimization of the model(More)