Deconfounding scores improve causal effect estimation with weak overlap.
problem Challenges in causal treatment effect estimation due to weak overlap in high-dimensional data.
method Propose deconfounding scores to preserve identification and target estimation while improving overlap.
result Prognostic scores are overlap-optimal under a broad family of generalized linear models with Gaussian features.
Paper tackles unpredictable feature evolution in learning.
problem Learning with unpredictable feature evolution.
method Proposes PUFE method to fill incomplete overlapping period and formulate as matrix completion problem. Uses ensemble method to incorporate old and new feature spaces.
result Theoretical and experimental validation shows PUFE method can always follow the best base models.
Deconfounding scores improve causal effect estimation with weak overlap.
problem Poor overlap in treatment and control groups makes causal effect estimators brittle.
method Introduces feature representations that improve overlap without introducing bias.
result Deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data.
Overlapping clustering problem is an important learning issue in which clusters are not mutually exclusive and each object may belongs simultaneously to several clusters. This paper presents a kernel based method that produces overlapping clusters on a high feature space using mercer kernel techniques to improve separa…
A new VAE model identifies and estimates treatment effects with limited overlap.
problem Identifying and estimating treatment effects when subjects with certain features belong to a single treatment group.
method Developed a latent variable model to estimate a prognostic score, which is sufficient for treatment effects. The model is a new type of VAE called β-Intact-VAE.
result The model identifies individualized treatment effects and provides TE error bounds.
We find prominent similarities in the features of the time series for the (model earthquakes or) overlap of two Cantor sets when one set moves with uniform relative velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observation…
We find prominent similarities in the features of the time series for the overlap of two Cantor sets when one set moves with uniform relative velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations.
Deep learning methods improve overlapping speaker separation across languages and noise.
problem Overlapping speaker separation in realistic scenarios.
method Deep clustering and deep attractor networks.
result Deep learning methods are effective for a broad range of languages and can handle untrained languages with common features.
Unified detection of isolated and overlapping audio events using CNN-RNN.
problem Detecting both isolated and overlapping audio events simultaneously.
method Multi-label multi-task framework based on CNN-RNN, with sequential losses.
result Good generalization on isolated and overlapping audio event detection datasets.
Multitask learning can be effective when features useful in one task are also useful for other tasks, and the group lasso is a standard method for selecting a common subset of features. In this paper, we are interested in a less restrictive form of multitask learning, wherein (1) the available features can be organized…
Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups. For very large datasets and under standard sparsity constraints, hard thresholding methods have proven to be extremely efficient, but such methods require NP h…
This study improves author disambiguation without supervision using feature overlap.
problem Author name homonymy in the Web of Science.
method Probabilistic similarity measure based on feature overlap for agglomerative clustering.
result Our approach outperforms the trivial baseline and is state-of-the-art.
Classification with a sparsity constraint on the solution plays a central role in many high dimensional machine learning applications. In some cases, the features can be grouped together so that entire subsets of features can be selected or not selected. In many applications, however, this can be too restrictive. In th…
New metrics assess class overlap and imbalance in datasets.
problem Class overlap and imbalance make datasets hard to classify.
method Developed new metrics based on ball coverage by classes.
result Metrics correlate well with classifier performance.
OMP improves text classification accuracy with sparse models.
problem Overfitting in text classification due to high dimensionality.
method Greedy variable selection algorithm (OMP) and overlapping Group OMP.
result OMP and overlapping GOMP produce effective and very sparse models.
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into "color com…
Bayesian ptychography method reduces overlap for faster imaging.
problem Reduced overlap leads to large data volumes and long acquisition times.
method Generative model combined with MCMC for posterior sampling.
result Framework consistently outperforms iterative reconstruction methods with reduced overlap.
Proposes a meta-algorithm for classification with overlapping classes in high-energy physics.
problem Challenges of class overlap in binary classification.
method Combines bagging and boosting techniques with a randomization trick.
result Improves statistical significance of Higgs discovery.
A Fourier-based learning algorithm for multiclass classification.
problem Highly nonlinear multiclass classification problems.
method Smoothing technique with low-pass filters to calculate probability distributions.
result Probabilistic explanation for classification without kernel functions.
Sharp feature transitions revealed in extensive-width networks.
problem Learning hierarchical features from noisy queries in large networks.
method Information-theoretic analysis and heuristic decoupling argument.
result Sequential phase transitions in feature learnability and effective width.
We define the beta diffusion tree, a random tree structure with a set of leaves that defines a collection of overlapping subsets of objects, known as a feature allocation. A generative process for the tree structure is defined in terms of particles (representing the objects) diffusing in some continuous space, analogou…
FCPCA fuzzy clusters high-dimensional time series data efficiently.
problem Ambiguous clustering of multivariate time series data with overlapping distributions.
method FCPCA based on common principal component analysis.
result FCPCA outperforms existing methods in fuzzy clustering of multivariate time series.
New theory explains when pre-trained models can improve downstream tasks.
problem Lack of theoretical understanding of task similarity for transfer learning.
method Feature-centric viewpoint, theoretical results on transferability phase diagram.
result Transfer learning outperforms training from scratch when target task is well represented in feature space.
A new method speeds up overlapping group lasso computations.
problem Time-consuming optimization of overlapping group lasso on large-scale problems.
method Non-overlapping statistical approximation to overlapping group lasso.
result The proposed penalty is statistically equivalent to overlapping group lasso.
In medicine, visualizing chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosomes often overlap and it is necessary to identify and distinguish between the overlapping chromosomes. A segmentation solution that is fast and automated will enable scaling of co…
New method improves CATE estimation in low overlap regions.
problem Low overlap in CATE estimation leads to poor performance of meta-learners.
method Overlap-Adaptive Regularization (OAR) that regularizes models proportionally to overlap weights.
result OAR significantly improves CATE estimation in low-overlap settings.
Proposes a sensitivity framework to handle limited overlap in causal inference.
problem Limited overlap between treated and control groups in observational studies.
method Sensitivity framework based on worst-case confidence bounds on bias introduced by trimming.
result Protects against spurious findings by quantifying uncertainty in regions with limited overlap.
PACO uses consensus to improve overlapping patch processing.
problem Improving overlapping patch processing to avoid averaging issues.
method Develops a variational signal processing framework with ADMM-based algorithm for consensus.
result Efficient consensus solving for arbitrary patch decompositions.
Producing overlapping schemes is a major issue in clustering. Recent proposed overlapping methods relies on the search of an optimal covering and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of another meas…
The study simplifies assessing overlap in logistic regression models using empirical likelihood.
problem Assessing overlap in multidimensional logistic regression models.
method Translation of Silvapulle's condition to empirical likelihood maximization, mechanized with R code.
result Minimal overlapping structures are cataloged in dimensions less than four, providing rules for higher dimensions.
Estimates overlap in observational studies for causal effect estimation.
problem Overlap between treatment groups is crucial for causal effect estimation.
method Formalizes overlap estimation as a binary classification problem with Boolean rule classifiers.
result Rules provide interpretable explanations for causal conclusions.
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…
Community detection is a fundamental problem in network analysis which is made more challenging by overlaps between communities which often occur in practice. Here we propose a general, flexible, and interpretable generative model for overlapping communities, which can be thought of as a generalization of the degree-co…
RISA improves VFL by using imputed samples with low uncertainty.
problem Limited overlapping samples constrain VFL performance.
method Imputing non-overlapping samples and using evidence theory to select reliable imputed samples.
result Significant performance gains achieved, especially with limited overlapping samples.
As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to …
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community structure that maximizes a quality function associated with each snapshot of the network subject to a temporal smoothness c…
New mechanism detects overlap density for weak-to-strong generalization.
problem Understanding what aspects of data enable weak-to-strong generalization.
method Data-centric mechanism and overlap detection algorithm.
result Overlap density is a key factor in weak-to-strong generalization.
Paper presents a deep learning framework for classifying respiratory anomalies and lung diseases from sound recordings.
problem Classifying respiratory anomalies and lung diseases from respiratory sound recordings.
method The framework uses front-end feature extraction to transform sound into spectrograms, and a deep learning network to classify these features.
result The proposed deep learning system outperforms current state-of-the-art methods on the ICBHI benchmark dataset.
We calculate eigenvector overlaps between intersecting time periods of covariance matrices.
problem Analyzing overlapping time periods in covariance matrices.
method Girko linearisation and extended local laws.
result Computed eigenvector overlaps for intersecting time intervals.
Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains. While there exist many works on community detection based on connectivity structures, they suffer from either considering the overlapping or non-overlapping communities. In this w…
Develops fast inference for nonparametric Bayesian LFRM.
problem Inference in LFRM is challenging and slow.
method Small-variance asymptotics framework for nonparametric Bayesian LFRM.
result Deterministic inference algorithms are fast and competitive.
Unified approach for fair classification with overlapping groups.
problem Ensuring fairness across multiple overlapping groups in prediction problems.
method Probabilistic population analysis leading to Bayes-optimal classifier, unifying existing methods.
result Outperforms baselines in fairness-performance tradeoff on real datasets.
Overlap-Local-SGD improves distributed SGD by overlapping communication and computation.
problem High communication delay and node slowdown in distributed SGD.
method Adding an anchor model to synchronize local updates and pull them towards the anchor model.
result Overlap-Local-SGD speeds up distributed training and mitigates straggler effects.
Community detection using both graphs and social networks is the focus of many algorithms. Recent methods aimed at optimizing the so-called modularity function proceed by maximizing relations within communities while minimizing inter-community relations. However, given the NP-completeness of the problem, these algorith…
New LT-O-learners improve HLTE estimation with low overlap.
problem Challenges in estimating heterogeneous long-term treatment effects due to limited overlap.
method Introduces LT-O-learners that use custom overlap weights to downweight low-overlap samples.
result LT-O-learners provide robust HLTE estimates with lower variance in low-overlap regimes.
ION-C solves overlapping network integration problems efficiently.
problem Integrating overlapping networks with different datasets.
method Formulated as an ASP problem and solved with clingo.
result Significantly improved efficiency in runtime and solution graphs.
Deep learning classifies galaxies from Dark Energy Survey in just 8 minutes.
problem Classifying galaxies from large-scale surveys.
method Transfer learning from pre-trained neural networks.
result Achieved state-of-the-art accuracy of 99.6% in galaxy classification.
Study on Langevin dynamics for recovering planted signals in spiked matrix models.
problem Recovering a planted signal in spiked matrix models.
method Path-wise characterization of overlap using integro-differential equations and explicit formula derivation.
result Sharp phase transition in limiting overlap: positive in one regime, zero in another due to injected noise.