A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper develops methods to infer membership probabilities and rank network nodes using the DCMM model.
In the present paper, we studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type p…
Proposes a new model for mixed membership in Gaussian mixture.
New method bounds membership inference attack success using mutual information.
Work in the classification literature has shown that in computing a classification function, one need not know the class membership of all observations in the training set; the unlabeled observations still provide information on the marginal distribution of the feature set, and can thus contribute to increased classifi…
The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a n…
New model quantifies how much machine learning models can reveal about individual data usage.
Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node at- tributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochas…
Paper revisits set membership estimation for linear systems with relaxed disturbance bounds.
Null-Calibrated Conformal Selection via Target-Membership Scores
Paper tackles MIAs vulnerability by controlling FDR, providing guarantees on false discoveries.
NoisyMix boosts model robustness to common corruptions.
Paper extends Bayes Theorem for interval probability estimates.
New research limits how well attackers can guess if data points were in a model's training set.
The paper analyzes DeepWalk and node2vec for community detection in stochastic blockmodels.
We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually ex…
Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods …
In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, membership attack aims to check whether a given instance x was used in the training data or not. A co-me…
New method for mixed memberships using symmetrized Laplacian inverse matrix.
The paper proposes a new model to analyze directed networks and accurately estimate community memberships.
Paper evaluates membership inference attacks on transfer learning models.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
The \emph{Mixed-Membership Stochastic Blockmodel (MMSB)} is a popular framework for modeling social network relationships. It can fully exploit each individual node's participation (or membership) in a social structure. Despite its powerful representations, this model makes an assumption that the distributions of relat…
Proposes a new privacy notion for membership inference attacks on machine learning models.
Most classification methods provide either a prediction of class membership or an assessment of class membership probability. In the case of two-group classification the predicted probability can be described as "risk" of belonging to a "special" class . When the required output is a set of ordinal-risk groups, a discr…
Probabilistic fair clustering tackles uncertain group membership.
Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's training dataset. The serious privacy concerns due to the membership inference have motivated multiple defenses against MIAs, e.g., differential pr…
New attacks can infer model training membership using only label predictions, not confidence.
G-FIGS uses instance weights to create interpretable models from diverse data.
Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions. It is of interest to decontaminate mutual contamination models, i.e., to recover the base distributions ei…
Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential partition. In this paper, we propose to construct these mass functions by bootstrapping finite mixture models. In the first step, we compute b…
Directional and pairwise measurements are often used to model inter-relationships in a social network setting. The Mixed-Membership Stochastic Blockmodel (MMSB) was a seminal work in this area, and many of its capabilities were extended since then. In this paper, we propose the \emph{Dynamic Infinite Mixed-Membership s…
Method estimates network connectivity and dimensionality from multiple networks.
Logistic regression can handle noisy labels effectively when labels are imperfectly assigned by multiple experts.
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…
A new model estimates mixed memberships for categorical data with weighted responses.
We consider the problem of estimating community memberships of nodes in a network, where every node is associated with a vector determining its degree of membership in each community. Existing provably consistent algorithms often require strong assumptions about the population, are computationally expensive, and only p…
New framework tackles stochastic latent subgroup heterogeneity in online decision-making.
New algorithms improve spectral clustering for finite mixture models.
Membership inference determines, given a sample and trained parameters of a machine learning model, whether the sample was part of the training set. In this paper, we derive the optimal strategy for membership inference with a few assumptions on the distribution of the parameters. We show that optimal attacks only depe…
New attacks reveal membership in label-only ML models.
We show that the Membership Problem for finitely generated subgroups of 3-manifold groups is solvable.
Log-Loss scores expose membership privacy breaches.
Given a system of equations in a "random" finitely generated subgroup of the braid group, we show how to find a small ordered list of elements in the subgroup, which contains a solution to the equations with a significant probability. Moreover, with a significant probability, the solution will be the first in the list.…
New model for detecting communities in weighted bipartite networks.
New methods estimate mixed memberships in multi-layer networks.
New algorithm learns halfspaces with membership queries, achieving near optimal label complexity.
Mixed-SCORE+ improves community detection in weak signal networks.