21 accepted papers
Autoregressive models play an important role in a variety of applications including finance, engineering, sciences, and agriculture. While for some models (e.g., physics-based models) parameters are known, in other domains the parameters may not be available. This paper deals with the estimation of…
Matrix completion plays an important role in machine learning and signal processing, with applications ranging from recommender systems to image inpainting. Many approaches have been considered to solve the problem and some offer computationally efficient solutions. In particular, a highly-efficient…
Low-rank matrix completion, the process of estimating a low-rank matrix from a small subset of its entries, has many applications including collaborative filtering and system identification. Many algorithms have been considered to address this problem. Coordinate descent has been previously proposed…
This paper considers the problem of performing post-attack forensic analysis for a test-time attack on a machine learning model. A test-time attack can be represented as a mapping that receives a benign test example as the input and outputs a falsified version of it. Given a set of attacked examples…
We consider the problem of designing a robust classifier in the presence of an adversary who aims to degrade classification performance by elaborately falsifying the test instance. We propose a model-agnostic defense approach wherein the true class label of the falsified instance is inferred by anal…
Factorization-based gradient descent is a scalable and efficient algorithm for solving low-rank matrix completion. Recent progress in structured non-convex optimization has offered global convergence guarantees for gradient descent under certain statistical assumptions on the low-rank matrix and the…
The problem of making the classifier design resilient to test data falsification is considered. In the literature, a few countermeasures have been proposed to defend machine learning algorithms against test data falsification, but a common assumption employed therein is that feature entries of test…
The hyperspectral unmixing problem arises in remote sensing, chemometrics, and biomedical engineering applications. The spectral signature of a single pixel in a hyperspectral cube can be represented as a non-negative combination of non-negative signatures from various materials contained in the phy…
This paper introduces the use of adaptive restart to accelerate iterative hard thresholding (IHT) for low-rank matrix completion. First, we analyze the local convergence of accelerated IHT in the non-convex setting of matrix completion problem (MCP). We prove the linear convergence rate of the accel…
We present a momentum-based accelerated iterative hard thresholding (IHT) for low-rank matrix completion. We analyze the convergence of the proposed Heavy Ball (HB) accelerated IHT near the solution and provide optimal step size parameters that guarantee the fastest rate of convergence. Since the op…
Clustering is widely used for exploratory data analysis in a variety of applications. Traditionally clustering is studied as an unsupervised task where no human inputs are provided. A recent trend in clustering is to leverage user provided side information to better infer the clustering structure in…
Multiple-instance learning is a framework for learning from data consisting of bags of instances labeled at the bag level. A common assumption in multi-instance learning is that a bag label is positive if and only if at least one instance in the bag is positive. In practice, this assumption may be v…
Recognizing and localizing a recurring pattern is a problem with a variety of applications such as classification and localization of home appliances from their activation signals and estimating the relative alignment between records of a natural repetitive electrocardiography (ECG) signals in Bio-m…
Convolutional neural networks (CNNs) are powerful tools for classification of visual inputs. An important property of CNN is its restriction to local connections and sharing of local weights among different locations. In this paper, we consider the definition of appropriate local neighborhoods in CN…
We present an online method to learn recurring time-frequency patterns from spectrograms. Our method relies on a convolutive decomposition that estimates sequences of spectra into time-frequency patterns and their corresponding activation signals. This method processes one spectrogram at a time such…
In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where the goal is to segment bird syllables from the recording and predict the bird species for the syllables. Traditional…
We study sparse gross error correction for state estimation in a non-linear sensing system. We consider a practical assumption that gross errors are sparse, and their locations tend to be invariant over a few consecutive measurement periods. Under the assumption, a robust state estimation and error…
Activity recognition from sensor data has spurred a great deal of interest due to its impact on health care. Prior work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort to produce training da…
In this paper, we present an approach for learning in the semi-supervised setting in the presence of novel class instances. In this setting, data consists of a labeled portion and an unlabeled portion that contains novel class instances along with unlabeled known class instances. Novel class instanc…
Multi-instance multi-label learning (MIML) is a framework for learning in the presence of label ambiguity. In MIML, experts provide labels for groups of instances (bags), instead of directly providing a label for every instance. When labeling efforts are focused on a set of target classes, instances…
A common framework of identifying bird species from audio recordings involves detecting bird song segments, which will be subsequently input to a classifier. In-field recordings are contaminated with various environmental noise. For such recordings, supervised segmentation has been observed to outpe…