9 accepted papers
This paper introduces the Collaborative Likelihood-ratio Estimation problem, which is relevant for applications involving multiple statistical estimation tasks that can be mapped to the nodes of a fixed graph expressing pairwise task similarity. Each graph node $v$ observes i.i.d data from two unkno…
In many machine learning applications acquiring reliable ground-truth labels is costly, or unfeasible, leading practitioners to rely on crowdsourcing and aggregation of noisy human annotations. When labels are subjective, however, aggregation may amplify individual biases, particularly with respect …
Vertex-level clustering for directed graphs (digraphs) remains challenging as edge directionality breaks the key assumptions underlying popular spectral methods, which also incur the overhead of eigen-decomposition. This paper proposes *Parametrized Power Iteration Clustering* (ParPIC), a random-wal…
Multiple two-sample test problem in a graph-structured setting is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node $v$ in fixed graph deals with a two-sample testing problem between two node-specific probability density functions, $p_v$ and $q_v$. The goal is to ide…
In this paper, we present a deterministic particle-based method for global optimization of continuous Sobolev functions, called *Stein Boltzmann Sampling* (SBS). SBS initializes uniformly a number of particles representing candidate solutions, then uses the *Stein Variational Gradient Descent* (SVGD…
Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning. A usual approach for addressing this problem is the likelihood-ratio estimation (LRE) between $p$ and $q$, which -to our best knowledg…
This paper addresses the problem of segmenting a stream of graph signals: we aim to detect changes in the mean of the multivariate signal defined over the nodes of a known graph. We propose an offline algorithm that relies on the concept of graph signal stationarity and allows the convenient transla…
This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly. The aim is to identify both the moments at which significant changes occur in the Ising model, as well as the underlying graph structures. For this purpose, we propose to e…
In this paper, we present a method for learning an underlying graph topology using observed graph signals as training data. The novelty of our method lies on the combination of two assumptions that are imposed as constraints to the graph learning process: i) the standard assumption used in the liter…