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Found 23 papers, 2 papers with code
Distributed and Rate-Adaptive Feature Compression
Our goal is to design a feature compression {scheme} that can adapt to the varying communication constraints, while maximizing the inference performance at the fusion center.
Quickest Change Detection with Leave-one-out Density Estimation
The problem of quickest change detection in a sequence of independent observations is considered.
Controlling a Markov Decision Process with an Abrupt Change in the Transition Kernel
We consider the control of a Markov decision process (MDP) that undergoes an abrupt change in its transition kernel (mode).
Adaptive Step-Size Methods for Compressed SGD
Compressed Stochastic Gradient Descent (SGD) algorithms have been recently proposed to address the communication bottleneck in distributed and decentralized optimization problems, such as those that arise in federated machine learning.
Multiple Testing Framework for Out-of-Distribution Detection
While a number of tests for OOD detection have been proposed in prior work, a formal framework for studying this problem is lacking.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Quickest Change Detection in the Presence of Transient Adversarial Attacks
We study a monitoring system in which the distributions of sensors' observations change from a nominal distribution to an abnormal distribution in response to an adversary's presence.
Quickest Change Detection with Non-Stationary Post-Change Observations
For the case where the post-change distributions have parametric uncertainty, a window-limited (WL) generalized likelihood-ratio (GLR) CuSum procedure is developed and is shown to achieve the universal lower bound asymptotically.
Non-Parametric Quickest Mean Change Detection
For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero.
Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits
In contrast to previous work on channel allocation using the MAB framework, APs are permitted to choose multiple channels for transmission.
Non-Parametric Quickest Detection of a Change in the Mean of an Observation Sequence
We study the problem of quickest detection of a change in the mean of an observation sequence, under the assumption that both the pre- and post-change distributions have bounded support.
Dynamic Spectrum Access using Stochastic Multi-User Bandits
In contrast to prior work, it is assumed that rewards can be non-zero even under collisions, thus allowing for the number of users to be greater than the number of channels.
Robust Mean Estimation in High Dimensions via $\ell_0$ Minimization
We study the robust mean estimation problem in high dimensions, where $\alpha <0. 5$ fraction of the data points can be arbitrarily corrupted.
Sequential Controlled Sensing for Composite Multihypothesis Testing
The distribution of observations collected under each control is assumed to follow a single-parameter exponential family distribution.
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access
Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems.
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions
Within this setup, where the number of players is allowed to be greater than the number of arms, we present a policy that achieves near order-optimal expected regret of order $O(\log^{1 + \delta} T)$ for some $0 < \delta < 1$ over a time-horizon of duration $T$.
Information-Theoretic Understanding of Population Risk Improvement with Model Compression
We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in the empirical risk with model compression.
Tightening Mutual Information Based Bounds on Generalization Error
The bound is derived under more general conditions on the loss function than in existing studies; nevertheless, it provides a tighter characterization of the generalization error.
Model change detection with application to machine learning
The goal is to detect whether the change in the model is significant, i. e., whether the difference between the pre-change parameter and the post-change parameter $\|\theta-\theta'\|_2$ is larger than a pre-determined threshold $\rho$.
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access
The algorithms in both stochastic and adversarial scenarios are extended to the dynamic case where the number of users in the system evolves over time and are shown to lead to sub-linear regret.
Active and Adaptive Sequential learning
Furthermore, an estimator of the change in the learning problems using the active learning samples is constructed, which provides an adaptive sample size selection rule that guarantees the excess risk is bounded for sufficiently large number of time steps.
Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection
A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences.
Adaptive Sequential Optimization with Applications to Machine Learning
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).
Quickest Change Detection
The problem of detecting changes in the statistical properties of a stochastic system and time series arises in various branches of science and engineering.
Statistics Theory Information Theory Information Theory Optimization and Control Probability Applications Statistics Theory
