BSD is a Bayesian framework for analyzing neural spectral data.
problem Challenges in statistical analysis and group-level comparisons of neural power spectra.
method Bayesian Spectral Decomposition (BSD) for parametric models of neural spectra.
result BSD outperforms existing methods in model selection and parameter estimation.
Develops a method to estimate uncertainty for group-level recommendations in matrix completion.
problem Uncertainty estimation for group-level recommendations in matrix completion.
method Structured conformal inference method combining any matrix completion algorithm.
result Stronger group-level guarantees through structured calibration.
Framework infers coordination strategies from movement data.
problem Inferring individual movement strategies from group data.
method Formalizes Coordination Strategy Inference Problem; provides methodology to infer strategies.
result Framework accurately infers strategies in simulated and real-world datasets.
New model clusters cells and individuals, revealing genetic influences on cell types.
problem Clustering nested data with group-level and observation-level variables.
method Nested Atoms Model (NAM), Bayesian nonparametric approach.
result Identifies clusters of genetically similar individuals with homogeneous cell-type profiles.
We present a Bayesian nonparametric framework for multilevel clustering which utilizes group-level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-m…
We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach, which combines ideas from transfer learning, deep learning and multi-instance learning, reduces t…
Develops new tests for high-dimensional models with mixed signal strengths.
problem Challenges in testing models with many signals and high-dimensional data.
method Moment matching formulation for developing new tests.
result Demonstrates optimality of GRIP test for various model types.
FGSV defends against shell company attacks in group data valuation.
problem Shell company attacks on group-level data valuation.
method Developed a provably fast and accurate approximation algorithm for FGSV.
result Empirical results show significant improvement in computational efficiency and accuracy.
We present the discrete infinite logistic normal distribution (DILN), a Bayesian nonparametric prior for mixed membership models. DILN is a generalization of the hierarchical Dirichlet process (HDP) that models correlation structure between the weights of the atoms at the group level. We derive a representation of DILN…
Optimized deep learning architectures improve sensor fusion performance.
problem Sensor fusion in autonomous systems.
method Proposed two optimized architectures: coarser-grained and two-stage gated.
result Significant performance improvements and robustness in noisy conditions.
Unimodal approach for group-level emotion recognition without individual features.
problem Privacy issues in individual-based emotion recognition models.
method Frugal approach using global features, state-of-the-art and synthetic corpora.
result 59.13% accuracy on VGAF test set, 11th place in EmotiW Challenge 2020.
The paper analyzes how minority group imbalance affects neural network performance.
problem The impact of minority group imbalance on neural network performance.
method Formulated group imbalance problem with Gaussian Mixture Model, quantified sample complexity, convergence rate, and testing performance.
result Increasing the minority group fraction does not necessarily improve the generalization performance of the minority group.
Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the gr…
Dirichlet Process(DP) is a Bayesian non-parametric prior for infinite mixture modeling, where the number of mixture components grows with the number of data items. The Hierarchical Dirichlet Process (HDP), is an extension of DP for grouped data, often used for non-parametric topic modeling, where each group is a mixtur…
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automa…
Constellation learns group-level visual relationships for abstract reasoning.
problem Learning configurational properties of entire groups of objects.
method Introduces Constellation, a network that learns relational abstractions over static visual scenes.
result Offers a basis for abstract relational reasoning and sensory imagination.
GROOVE learns representations for weakly paired multimodal data.
problem Learning representations for high-content perturbation data with weakly paired samples.
method GroupCLIP contrastive loss integrated with an autoencoder framework.
result GROOVE performs on par with or outperforms existing approaches for cross-modal tasks.
Proposes a new RNN model for grouped sequential data with varying time intervals.
problem Implicitly models fixed time intervals between observations and lacks group-level effects.
method Mixed membership framework for RNN, learning group-level base parameter.
result Demonstrates dynamic topic modeling with evolving topic distributions over time.
In this review paper, we present several results on central extensions of the Lie algebra of symplectic (Hamiltonian) vector fields, and compare them to similar results for the Lie algebra of (exact) divergence free vector fields. In particular, we comment on universal central extensions and integrability to the group …
Accurate Bayesian analysis for mixed-effects studies.
problem Statistical significance and inter-individual differences in group studies.
method Variational Bayesian approach for hierarchical mixed-effects models.
result Accurate assessment of group-level effects and inter-individual differences.
Improves clustering fairness by learning fair clusters adaptively.
problem Fairness in deep clustering, especially for protected status variables.
method Formulates group-level fairness as ILP, integrates into discriminative deep clustering, refines learning algorithm.
result Consistently outperforms fair clustering algorithms on real-world datasets.
GCAO improves clustering of high-dimensional data by grouping low-density boundary points.
problem Stability and accuracy of clustering in high-dimensional, non-uniform data.
method Group-level optimization with gravitational attraction and optimization.
result GCAO outperforms 11 clustering methods on multiple datasets.
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso model at the indivi…
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.
method Causal inference, graph analysis, temporal complexity measures, machine learning.
result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.
We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control. Specifically, we want to use minimal supervision to learn a latent representation that reflects the semantics behind a specific grouping of the data, where within a group the…
Bayesian Additive Distribution Regression (DistBART) predicts distributions from grouped data.
problem Predicting distributions from grouped data with varying characteristics.
method Bayesian nonparametric approach using BART for modeling the regression function.
result Empirical and theoretical evidence supports DistBART's effectiveness in learning from low-dimensional marginals.
New framework for forecasting psychological processes from ILD.
problem Forecasting psychological processes at the individual level from ILD.
method A novel modeling framework addressing challenges in ILD.
result Improved forecasting of psychological processes at the individual level.
A new model identifies genetic risk factors using gene-level priors.
problem Identifying genetic risk factors from nucleotide-level genetic variants.
method Sparse Group Lasso with Group-level Graph structure (SGLGG) model.
result SGLGG effectively identifies phenotype-associated risk SNPs.
PR-GNN identifies salient brain regions for ASD biomarkers.
problem Identifying brain regions associated with neurological disorders.
method Pooling Regularized Graph Neural Network (PR-GNN) with novel salient region selection.
result PR-GNN outperforms baseline methods in ASD classification accuracy.
Identifies patient-specific root causes of disease using structural equation models.
problem Detecting significant variables in complex diseases that differ between patients.
method Defining patient-specific root causes as exogenous errors in a structural equation model, quantifying predictivity using Shapley values, and developing a fast algorithm called Root Causal Inference.
result Significant improvements in accuracy by uncovering root causes with large effect sizes at the individual level but clinically insignificant effect sizes at the group level.
Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a g…
New method for joint MEG/EEG source imaging improves accuracy.
problem Joint MEG/EEG source imaging for group studies.
method Minimum Wasserstein Estimates (MWE) using Optimal Transport.
result MWE produces more accurate source localization than standard methods.
Bayesian method for feature selection with grouping info using expectation propagation.
problem Feature selection with grouping info and sparsity constraints.
method Sparse-group Bayesian feature selection using expectation propagation.
result Our method outperforms existing methods in terms of feature selection accuracy and computational efficiency.
In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity. Sparse-Group Lasso has recently been introduced in the context of l…
We study proper, isometric actions of nonsolvable discrete groups Gamma on the 3-dimensional Minkowski space R^{2,1} as limits of actions on the 3-dimensional anti-de Sitter space AdS^3. To each such action is associated a deformation of a hyperbolic surface group Gamma_0 inside O(2,1). When Gamma_0 is convex cocompact…
Proposes a method to cluster fMRI data and estimate brain connectivity networks.
problem Clustering fMRI data to identify patient groups based on brain connectivity.
method Random covariance clustering model (RCCM) to cluster subjects and estimate individual and shared FC networks.
result RCCM outperforms other methods in clustering and FC network estimation, demonstrated through simulations and real data.
The paper compares fairness criteria in algorithmic decision-making.
problem Fairness in machine learning algorithms can lead to biases and disparities.
method The study examines three fairness criteria: Color-blind, Demographic Parity, and Equalized Odds.
result Equalized Odds (EO) is the only criterion that removes group-level disparity.
Develops a new criterion for subgroup fairness in algorithmic decision support.
problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.
An anologue of the Calabi invariant for Poisson manifolds is considered. For any Poisson manifold P, the Poisson bracket on C∞(P) extends to a Lie bracket on the space Ω1(P) of all differential one-forms, under which the space Z1(P) of closed one-forms and the space B1(P) of exact one-forms a…
The paper compares different fairness definitions under various worldviews.
problem Avoiding disparity amplification under different worldviews.
method Mathematical comparison of four fairness definitions using a theoretical framework.
result Different worldviews require different fairness definitions to avoid disparity amplification.
We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or addre…
The paper proposes a method to measure fairness through equality of effort using algorithmic recourse.
problem Measuring fairness through equality of effort in automated systems.
method Applying algorithmic recourse to quantify equality of effort, overcoming previous limitations.
result An algorithm for assessing equality of effort has been developed and validated.
This work introduces a fair learning method for diverse sensitive attributes.
problem Fairness in supervised learning with complex sensitive attributes.
method Neural network with a simple random sampler for fairness penalties.
result The method improves fairness and utility on benchmark data.
Personalized models using group attributes reduce performance, study finds.
problem Reducing performance of models using group attributes like race or gender.
method Formal conditions and collective preference guarantees to ensure fair use.
result Models personalized with group attributes reduce performance at a group level.
ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.
problem Detecting nuanced performance differences in classifier fairness.
method Study of ABROCA metric's statistical properties under various conditions.
result ABROCA distributions are skewed, inflating results by chance in imbalanced classes.
The (1,1)-length of a knot is a braid group invariant equaling its level number.
problem Finding a numerical invariant for knot positions.
method Using braid group on two points in the torus to describe and calculate the (1,1)-length. result The (1,1)-length equals the level number of a knot. The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the group lasso estimator either use gradient projection methods to update the entire coefficient vector simultaneously at e…
LCMQR improves prediction intervals by adapting to local heteroscedasticity.
problem Efficient and adaptive prediction intervals for local heteroscedasticity.
method LCMQR combines multi-quantile information with kernel-based localization.
result LCMQR constructs tighter intervals than prior methods, especially in heterogeneous environments.