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Found 37 papers, 13 papers with code
Advanced Tutorial: Label-Efficient Two-Sample Tests
This tutorial extends active learning concepts to two-sample testing within this \textit{label-costly} setting while maintaining statistical validity and high testing power.
Structure Learning in Gaussian Graphical Models from Glauber Dynamics
Glauber dynamics, also called the Gibbs sampler, is a Markov chain that sequentially updates the variables of the underlying model based on the statistics of the remaining model.
Learning Networks from Wide-Sense Stationary Stochastic Processes
Under a novel mutual incoherence condition and certain sufficient conditions on $(n, p, d)$, we show that the ML estimate recovers the sparsity pattern of $L^\ast$ with high probability, where $d$ is the maximum degree of the graph underlying $L^{\ast}$.
Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching
Large-scale federated learning (FL) over wireless multiple access channels (MACs) has emerged as a crucial learning paradigm with a wide range of applications.
Unraveling overoptimism and publication bias in ML-driven science
Validity concerns are underscored by findings of an inverse relationship between sample size and reported accuracy in published ML models, contrasting with the theory of learning curves where accuracy should improve or remain stable with increasing sample size.
Active Sequential Two-Sample Testing
The classification model is adaptively updated and used to predict where the (unlabelled) features have a high dependency on labels; labeling the ``high-dependency'' features leads to the increased power of the proposed testing framework.
Differential Analysis for Networks Obeying Conservation Laws
Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data -- the so called differential network analysis problem.
Robust Model Selection of Gaussian Graphical Models
We first characterize the equivalence class up to which general graphs can be recovered in the presence of noise.
Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise
The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented.
Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)
We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse summaries.
Learning the Structure of Large Networked Systems Obeying Conservation Laws
Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws.
A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets.
A label-efficient two-sample test
Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis).
Quantifying the Controllability of Coarsely Characterized Networked Dynamical Systems
In contrast, in this paper the controllability aspects of the coarse system are derived from coarse summaries {\em without} knowledge of the fine-scale structure.
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
In this paper, we consider the pure exploration problem in stochastic multi-armed bandits where the similarities between the arms are captured by a graph and the rewards may be represented as a smooth signal on this graph.
State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability.
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
Our objective is to develop conditions on the graph structure, the quantity, and properties of measurements, under which we can recover the community organization in this coarse graph.
Finding the Homology of Decision Boundaries with Active Learning
We theoretically analyze the proposed framework and show that the query complexity of our active learning algorithm depends naturally on the intrinsic complexity of the underlying manifold.
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
The dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented.
On the alpha-loss Landscape in the Logistic Model
We analyze the optimization landscape of a recently introduced tunable class of loss functions called $\alpha$-loss, $\alpha \in (0,\infty]$, in the logistic model.
On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems
We consider the problem of recovering a complex vector $\mathbf{x}\in \mathbb{C}^n$ from $m$ quadratic measurements $\{\langle A_i\mathbf{x}, \mathbf{x}\rangle\}_{i=1}^m$.
Regularization via Structural Label Smoothing
Regularization is an effective way to promote the generalization performance of machine learning models.
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization
We introduce a tunable loss function called $\alpha$-loss, parameterized by $\alpha \in (0,\infty]$, which interpolates between the exponential loss ($\alpha = 1/2$), the log-loss ($\alpha = 1$), and the 0-1 loss ($\alpha = \infty$), for the machine learning setting of classification.
Thresholding Graph Bandits with GrAPL
The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold.
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.
Ultra Large-Scale Feature Selection using Count-Sketches
We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.
Coalescent-based species tree estimation: a stochastic Farris transform
As a corollary, we also obtain a new identifiability result of independent interest: for any species tree with $n \geq 3$ species, the rooted species tree can be identified from the distribution of its unrooted weighted gene trees even in the absence of a molecular clock.
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
In this paper we develop a novel computational sensing framework for sensing and recovering structured signals.
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design.
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
However, in many cases, cheap approximations to $\func$ may be obtainable.
The Multi-fidelity Multi-armed Bandit
We study a variant of the classical stochastic $K$-armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available.
Multi-fidelity Gaussian Process Bandit Optimisation
However, in many cases, cheap approximations to $f$ may be obtainable.
Active Learning Algorithms for Graphical Model Selection
The problem of learning the structure of a high dimensional graphical model from data has received considerable attention in recent years.
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification
This paper investigates the problem of active learning for binary label prediction on a graph.
Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method
We consider the problem of estimating the evolutionary history of a set of species (phylogeny or species tree) from several genes.
