24 accepted papers
We study the problem of quickest change detection (QCD) when the post-change distribution has parametric uncertainty. The generalized likelihood ratio (GLR) cumulative sum (CuSum) procedure is known to be asymptotically optimum in this setting. However, this rule requires significant memory and comp…
This paper addresses the problem of quickest change detection of an unknown mean-shift in multiple Gaussian data streams. We propose a novel extension of the window-limited CuSum (WL-CuSum) test which utilizes the James-Stein estimator to improve detection performance. Compared to traditional maximu…
State of the art methods for target tracking with sensor management (or controlled sensing) are model-based and are obtained through solutions to Partially Observable Markov Decision Process (POMDP) formulations. In this paper a Reinforcement Learning (RL) approach to the problem is explored for the…
The problem of quickest detection of a change in the distribution of streaming data is considered. It is assumed that the pre-change distribution is known, while the only information about the post-change is through a (small) set of labeled data. This post-change data is used in a data-driven minima…
Compressed Stochastic Gradient Descent (SGD) algorithms have been proposed to address the communication bottleneck in distributed and decentralized optimization problems such as federated machine learning. Many existing compressed SGD algorithms use non-adaptive step-sizes (constant or diminishing)…
The problem of quickest change detection in a sequence of independent observations is considered. The pre-change distribution is assumed to be known, while the post-change distribution is completely unknown. A window-limited leave-one-out (LOO) CuSum test is developed, which does not assume any know…
The problem of robust binary hypothesis testing is studied. Under both hypotheses, the data-generating distributions are assumed to belong to uncertainty sets constructed through moments; in particular, the sets contain distributions whose moments are centered around the empirical moments obtained f…
The problem of quickest detection of a change in the distribution of a sequence of independent observations is considered. The prechange distribution is assumed to be known and stationary, while the post-change distributions are assumed to evolve in a pre-determined non-stationary manner with some p…
The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, compress and send them to the fusion center through a network of relays. The network imposes capacity constraints on the…
The problem of quickest growing dynamic anomaly detection in sensor networks is studied. Initially, the observations at the sensors, which are sampled sequentially by the decision maker, are generated according to a pre-change distribution. At some unknown but deterministic time instant, a dynamic a…
An adversarial multi-user multi-armed bandit framework is used to develop algorithms for uncoordinated spectrum access. It is assumed that the number of users is unknown, and that users receive zero reward on collision. The users do not coordinate with each other, and an adversary chooses different…
The problem of quickest detection of significant events in networks is studied. A distributed setting is investigated, where there is no fusion center, and each node only communicates with its neighbors. After an event occurs in the network, a number of nodes are affected, which changes the statisti…
Model change detection is studied, in which there are two sets of samples that are independently and identically distributed (i.i.d.) according to a pre-change probabilistic model with parameter θ, and a post-change model with parameter θ', respectively. The goal is to detect whether the change in t…
We consider the problem of quickest detection of dynamic events in sensor networks. After an event occurs, a number of sensors are affected and undergo a change in the statistics of their observations. We assume that the event is dynamic and can propagate with time, i.e., different sensors perceive…
Degrees of freedom (DoF) is studied in the downlink of a heterogenous wireless network modeled as a two-layered interference network. The first layer of the interference network is the backhaul layer between macro base stations (MBs) and small cell base stations (SBs), which is modeled as a Wyner ty…
In multichannel sequential change detection, multiple sensors monitor a system in which an abrupt change occurs at some unknown time and is perceived by an unknown subset of sensors. The goal is to detect this change quickly, while controlling the rate of false alarms. In the traditional asymptotic…
The problem of transient quickest change detection (QCD) is studied, in which the change from the initial to the final phase does not happen instantaneously, but after a series of cascading transient phases of finite durations, each one corresponding to a different probability distribution. The goal…
This paper considers the problem of quickest detection of a change in distribution under the assumption that the pre-change distribution π is known, and the post-change distribution μ is unknown and belongs to a general class of distributions. Using the knowledge of the pre-change distribution π, th…
A framework is introduced for solving a sequence of slowly changing optimization problems, including those arising in regression and classification applications, using optimization algorithms such as stochastic gradient descent (SGD). The optimization problems change slowly in the sense that the min…
We propose a statistical algorithm for detecting line outages in a power system and show that it has better performance than other schemes proposed in the literature. Our algorithm is based on the Cumulative Sum (CuSum) test from the Quickest Change Detection (QCD) literature. It exploits the statis…
Multiple observation sequences are collected, among which there is a small subset of outliers. A sequence is considered an outlier if the observations therein are generated by a mechanism different from that generating the observations in the majority of sequences. In the universal setting, the goal…
In this paper, we study the rate of decay of the probability of error for distinguishing between a sparse signal with noise, modeled as a sparse mixture, from pure noise. This problem has many applications in signal processing, evolutionary biology, bioinformatics, astrophysics and feature selection…
The following detection problem is studied, in which there are M sequences of samples out of which one outlier sequence needs to be detected. Each typical sequence contains n independent and identically distributed (i.i.d.) continuous observations from a known distribution π, and the outlier sequenc…
In outlier hypothesis testing, multiple observation sequences are collected, a small subset of which are outliers. Observations in an outlier sequence are generated by a mechanism different from that generating the observations in the majority of sequences. The goal is to best discern all the outlie…