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.
A new clustering method using Bayesian techniques improves robustness and interpretability.
Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively utilize the word burstiness representation of documents and achieve decent performance, they do not explore the sequential information of text…
A method to improve clustering explainability using bagging and feature dropout.
Improved Bayesian uncertainty quantification using variational bagging.
BDMBC clusters data with varying densities using a new PLLS measure.
Develops RL algorithm for non-Markovian, non-stationary reward streams.
BayesBag improves reproducibility of Bayesian inference under model misspecification.
To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes. We describe an alternative training loss which clusters weakly labeled bags in latent space to inform…
We explore the performance of sample average approximation in comparison with several other methods for stochastic optimization when there is information available on the underlying true probability distribution. The methods we evaluate are (a) bagging; (b) kernel smoothing; (c) maximum likelihood estimation (MLE); and…
Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel…
Due to the success of the bag-of-word modeling paradigm, clustering histograms has become an important ingredient of modern information processing. Clustering histograms can be performed using the celebrated -means centroid-based algorithm. From the viewpoint of applications, it is usually required to deal with symm…
Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tw…
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
Bayesian models offer great flexibility for clustering applications---Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such a…
FBC clusters data fairly without needing cluster count.
In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations of random vectors with corresponding probability distribution, and that a bag is…
Bagging stabilizes models without distributional assumptions.
Entropy regularization improves interpretability of probabilistic clustering models.
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
The paper analyzes bagging in overparameterized learning, deriving risk properties and optimal subsample sizes.
Multi-instance learning (MIL) has a wide range of applications due to its distinctive characteristics. Although many state-of-the-art algorithms have achieved decent performances, a plurality of existing methods solve the problem only in instance level rather than excavating relations among bags. In this paper, we prop…
Bayesian methods detect clusters in noisy data more reliably.
Bayesian SAE model with spectral clustering and uncertainty quantification.
Sparse convex clustering is to cluster observations and conduct variable selection simultaneously in the framework of convex clustering. Although a weighted norm is usually employed for the regularization term in sparse convex clustering, its use increases the dependence on the data and reduces the estimation acc…
We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph cluste…
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the ba…
clusterBMA combines clustering results from multiple models using Bayesian model averaging.
The paper compares aggregated data labels in curated and random bags for machine learning models.
Bagging is a device intended for reducing the prediction error of learning algorithms. In its simplest form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and then averages the resulting prediction rules. We extend the definition of bagging from stati…
A new large-scale tabular benchmark for Learning from Label Proportions.
Unified Bayesian framework for clustered federated learning improves model performance.
Introduces BCVI, a Bayesian cluster validity index for better cluster selection.
Bagging can significantly improve the generalization performance of unstable machine learning algorithms such as trees or neural networks. Though bagging is now widely used in practice and many empirical studies have explored its behavior, we still know little about the theoretical properties of bagged predictions. In …
Bayesian method clusters time series with varying dynamics.
Under-bagging -NN improves performance on imbalanced classification.
In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich-Wasserstein metr…
This paper improves robust cluster enumeration for RES data.
The paper simplifies Bayesian posterior using clustering to make inference more manageable.
Federated learning for Bayesian clustering of large datasets.
The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bay…
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate o…
New approach for learning large Bayesian networks using feature clustering and compression.
In this paper we present a novel approach for extracting a Bag-of-Words (BoW) representation based on a Neural Network codebook. The conventional BoW model is based on a dictionary (codebook) built from elementary representations which are selected randomly or by using a clustering algorithm on a training dataset. A me…
Being among the easiest ways to find meaningful structure from discrete data, Latent Dirichlet Allocation (LDA) and related component models have been applied widely. They are simple, computationally fast and scalable, interpretable, and admit nonparametric priors. In the currently popular field of network modeling, re…
In the supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function is the Boolean OR. The learner observes a sample of bags and the bag labels, but not the instance labels that determine…
DCMAP optimizes clustering in Bayesian Networks with dependent costs.
Bayesian model clusters diverse 'omics data for disease subtyping.
Bayesian models overestimate clusters, but practical summaries can correct this.