PM-LDA models allow partial topic memberships for image patches.
problem Crisp segmentation limits topic models to one topic per image patch.
method Developed PM-LDA for partial topic memberships and an estimation algorithm.
result PM-LDA can produce both crisp and soft segmentations.
Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In the…
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…
Proposes a new model for mixed membership in Gaussian mixture.
problem Limited to single component membership in Gaussian mixture models.
method Mixed membership sub-Gaussian model, spectral algorithm.
result Estimation error can be made arbitrarily small with high probability.
Robust fuzzy clustering for EEG driver alertness with outlier detection.
problem Ambiguous state boundaries in multivariate time series data.
method RFCPCA, a robust fuzzy subspace-clustering method for MTS.
result RFCPCA improves clustering accuracy and characterizes uncertainty and outliers in MTS.
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…
We introduce the nonparametric metadata dependent relational (NMDR) model, a Bayesian nonparametric stochastic block model for network data. The NMDR allows the entities associated with each node to have mixed membership in an unbounded collection of latent communities. Learned regression models allow these memberships…
This paper analyzes how differential privacy and data skewness affect membership inference attacks.
problem Membership inference attacks on privately trained models.
method Developed MPLens system to evaluate membership inference vulnerability.
result Membership inference risk is higher with skewed training data and differential privacy has trade-offs.
New co-membership attack method for deep generative models.
problem Check if instances were used in training data.
method Optimize attacker network to reproduce instances and use reconstruction error.
result Co-membership attacks are more powerful than single attacks.
New method for mixed memberships using symmetrized Laplacian inverse matrix.
problem Mixed memberships in community detection.
method Spectral clustering on symmetrized Laplacian inverse matrix.
result Mixed-SLIM methods outperform state-of-the-art methods.
The paper proposes a new model to analyze directed networks and accurately estimate community memberships.
problem Modeling and estimating community memberships in directed networks with heterogeneous degrees.
method Directed Degree Corrected Mixed Membership (DiDCMM) model and DiMSC algorithm.
result The proposed DiMSC algorithm is asymptotically consistent and provides error bounds for community membership vectors.
New defense DMP preserves ML model utility while enhancing membership privacy.
problem Membership inference attacks (MIAs) on machine learning models.
method Knowledge distillation to train ML models with membership privacy.
result DMP provides significantly better tradeoffs between membership privacy and classification accuracies.
Paper evaluates membership inference attacks on transfer learning models.
problem Evaluating membership inference attacks on transfer learning models.
method Shadow model training strategy to derive data for membership inference classifier.
result Membership inference attacks can achieve effective performance against transfer learning models.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
problem Modeling directed mixed membership networks with distinct community structures.
method Directed Mixed Membership Stochastic Blockmodel (DiMMSB) with DiSP algorithm.
result DiSP algorithm is asymptotically consistent and outperforms competitors.
Global optimization algorithm finds sparse mixed membership matrix factorization's global optimum.
problem Sparse mixed membership matrix factorization problems with local optima.
method Derives a global optimization algorithm for sparse mixed membership matrix factorization.
result Guaranteed ε-global optimum across random initializations and multiple modes. Paper improves community detection in sparse networks with node covariates.
problem Community detection in sparse networks with node covariates.
method Proposes a simple optimization framework for sparse networks with node covariates.
result Proposed method outperforms other existing methodologies in various simulated and real data examples.
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.
problem Membership inference attacks on machine learning models.
method Introduces f-Membership Inference Privacy (f-MIP) and μ-Gaussian Membership Inference Privacy (μ-GMIP) to quantify and mitigate privacy risks. result Analyzes likelihood ratio-based attacks and derives μ-GMIP guarantees for stochastic gradient descent (SGD) models. New adaptive filters reduce energy consumption in electronic devices.
problem Reducing energy consumption in adaptive filtering algorithms.
method Data-selective adaptive filters, set-membership (SM) filters, trinion and quaternion systems, partial-updating, LMS and recursive LMS algorithms.
result Improved adaptive filtering algorithms with reduced computational complexity and enhanced stability.
The paper develops methods to infer membership probabilities and rank network nodes using the DCMM model.
problem Understanding the latent structure of network data, especially in mixed-membership models.
method Degree-Corrected Mixed Membership (DCMM) model, novel finite-sample expansion, asymptotic distributions, confidence intervals, multiplier bootstrap method.
result Valid inference on membership probabilities and node rankings, quantifying uncertainty.
Paper finds optimal membership inference strategies for machine learning models.
problem Determining if a sample was part of the training set of a machine learning model.
method Derives optimal strategies for membership inference with assumptions on parameter distribution, showing that black-box attacks are as good as white-box attacks.
result Optimal strategies are not tractable, leading to approximations that outperform existing methods.
Probabilistic fair clustering tackles uncertain group membership.
problem Fair clustering with imperfect group membership.
method Probabilistic algorithms for metric graphs and metric membership.
result Approximation ratio guarantees for fair clustering.
New method estimates node community memberships in networks.
problem Estimating community memberships of nodes in networks with overlapping communities.
method Sharp eigenvector deviation bounds for Mixed Membership Stochastic Blockmodel (MMSB).
result Uniform rates of convergence for node community membership vectors.
Paper addresses decontamination of mixed distributions in machine learning.
problem Inferring base distributions from mixed samples with label noise.
method General setting with arbitrary probability spaces; sufficient conditions for identifiability; algorithms for infinite and finite samples.
result Sufficient conditions and algorithms for various machine learning problems.
New attacks can infer model training membership using only label predictions, not confidence.
problem Inferring whether a data point was used to train a machine learning model.
method Evaluate model's predicted labels under perturbations to infer membership.
result Label-only attacks perform as well as confidence-based attacks and break defenses that rely on confidence masking.
New method bounds membership inference attack success using mutual information.
problem Vulnerability of deep neural networks to membership inference attacks.
method Extended Fano's inequality to measure mutual information between inputs and activations.
result Empirical evaluation shows strong correlation between mutual information and model susceptibility.
Membership inference attacks reveal if records were in model training data.
problem Machine learning models leak information about training data records.
method Train an inference model to distinguish between training and non-training data.
result Machine learning models can be vulnerable to membership inference attacks.
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…
Paper improves variational inference for complex models.
problem Improving statistical accuracy of variational inference in high-dimensional models.
method Developed a general framework for MFVI and proposed a partially grouped VI algorithm.
result Proposed algorithm works and outperforms vanilla MFVI in mixed membership stochastic blockmodel.
BFPM relaxes fuzzy and possibilistic clustering restrictions, allowing full memberships in multiple clusters.
problem Issues with previous fuzzy and possibilistic clustering methods, especially overlapping clusters.
method BFPM allows larger membership values and multiple full memberships per object.
result BFPM overcomes conventional methods' limitations and improves data object tracking.
A new model estimates mixed memberships for categorical data with weighted responses.
problem Limited applicability of existing GoM model to weighted categorical data.
method Proposes Weighted Grade of Membership (WGoM) model, relaxing distribution constraints.
result WGoM can describe any response matrix with finite distinct elements.
The classical k-means algorithm for partitioning n points in Rd into k clusters is one of the most popular and widely spread clustering methods. The need to respect prescribed lower bounds on the cluster sizes has been observed in many scientific and business applications. In this paper, we present an…
New attacks reveal membership in label-only ML models.
problem Vulnerability of ML models to membership inference attacks.
method Developed decision-based membership inference attacks.
result Label-only exposures are vulnerable to membership leakage.
New model quantifies how much machine learning models can reveal about individual data usage.
problem Measuring and reducing the leakage of membership information from machine learning models.
method Using information theory, conditional mutual information leakage, and Kullback-Leibler divergence to quantify and bound the leakage.
result The amount of membership information leakage is reduced by adding Gaussian (ε,δ)-differentially-private additive noises. Log-Loss scores expose membership privacy breaches.
problem Privacy leakage from statistical aggregates like Log-Loss scores.
method Proved that Log-Loss scores enable full accuracy membership inference in a single query.
result Complete membership privacy breach is possible with Log-Loss scores.
We show that the Membership Problem for finitely generated subgroups of 3-manifold groups is solvable.
Paper presents membership encoding for deep learning models to protect training data.
problem Protecting training data from inference attacks and ensuring copyright.
method Membership encoding algorithm for deep neural networks.
result Membership information can be encoded for a subset of training data, robust to model compression and fine-tuning.
New model for detecting communities in weighted bipartite networks.
problem No model for community detection in overlapping bipartite weighted networks.
method Introduces BiMMDF model allowing any distribution with block structure.
result Efficient algorithm with theoretical guarantee of consistent estimation.
New methods estimate mixed memberships in multi-layer networks.
problem Complex community structure in multi-layer networks.
method Spectral methods using eigen-decomposition of aggregate matrices.
result Theoretical guarantees and empirical validation for mixed membership estimation.
New algorithm learns halfspaces with membership queries, achieving near optimal label complexity.
problem Learning halfspaces with membership queries.
method Proposed a new algorithm for learning halfspaces with membership queries, proving near optimal label complexity.
result Achieves near optimal label complexity for learning halfspaces.
Mixed-SCORE+ improves community detection in weak signal networks.
problem Detecting communities in weak signal networks.
method Proposes Mixed-SCORE+ combining properties of Mixed-SCORE and SCORE+.
result Significantly improves detection error rates on Polblogs and weak signal networks.
In this article we discuss some of the consequences of the mixed membership perspective on time series analysis. In its most abstract form, a mixed membership model aims to associate an individual entity with some set of attributes based on a collection of observed data. Although much of the literature on mixed members…
Study reveals how machine translation models can leak data privacy.
problem Detecting if a specific data sample was used to train machine translation models.
method Defined membership inference problem for sequence-to-sequence models, tested on machine translation models.
result Machine translation models can leak private information about training data.
SGLD protects membership privacy in Bayesian deep learning.
problem Preventing membership attack in Bayesian deep learning.
method Theoretical framework based on information leakage analysis.
result SGLD prevents information leakage to a certain extent.
Paper introduces privacy mechanism for machine learning models to prevent tracing attacks.
problem Preventing adversaries from identifying which data points were part of a model's training set.
method Introduces adversarial regularization to train models that are robust against membership inference attacks.
result Models trained with this method are indistinguishable from those trained on other data points, achieving both high utility and high privacy.
A new model for detecting overlapping communities in weighted networks.
problem Community detection in overlapping weighted networks with mixed membership and edge weights.
method Mixed membership distribution-free (MMDF) model with an efficient spectral algorithm and fuzzy weighted modularity.
result The MMDF model can estimate community memberships and evaluate community quality for weighted networks.
Robust Trimmed k-means improves clustering with outliers and mixed data.
problem Real-world data often contains outliers and mixed membership clusters, complicating traditional clustering methods.
method Proposes Robust Trimmed k-means (RTKM) that robustifies k-means for both single- and multi-membership data.
result RTKM outperforms other methods on multi-membership data with outliers and single membership data with outliers.
New method uses model's generalization gap to predict membership inference attacks.
problem Predicting membership inference attacks on machine learning models.
method Uses the model's generalization gap as a metric to estimate the vulnerability to membership inference attacks.
result Demonstrates that the model's generalization gap provides an upper bound on MIA security.