arXiv research

Efficient algorithm for CLSBM reduces misclassification rate.

Study on how attention in prompt-tuning affects large language models.

Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications s…

In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular,…

Building performance discrepancies between building design and operation are one of the causes that lead many new designs fail to achieve their goals and objectives. One of main factors contributing to the discrepancy is occupant behaviors. Occupants responding to a new design are influenced by several factors. Existin…

This work studies the class of algorithms for learning with side-information that emerge by extending generative models with embedded context-related variables. Using finite mixture models (FMM) as the prototypical Bayesian network, we show that maximum-likelihood estimation (MLE) of parameters through expectation-maxi…

Develops an ℓ_p theory for PCA and spectral clustering.

Study optimal spectral estimator for semi-supervised node classification.

Spatially constrained Gaussian mixture models reduce covariance complexity.

Contextualized ML learns context-dependent effects using deep learning.

IDS improves reinforcement learning with contextual information.

The paper uses Gaussian mixture models for Bayesian networks and proposes an optimization algorithm.

Enhances community detection in correlated networks with node attributes.

SADCBO optimizes contextual variables by balancing relevance and cost.

Paper uses Gaussian mixture models and Wasserstein distance for schema matching.

The bundle approach and n-contextuality reveal quantum model contextuality.

Proposes a neural network for contextual regression.

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

Transformers model contextual relations using probabilistic measures, revealing their expressive power.

Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

New bounds on sample size for identifying mixture models with grouped samples.

The study explores whether model selection guarantees apply to contextual bandits.

Faster algorithm reduces contextual bandit regret with fewer offline regression calls.

Efficiently handles contextual bandits with diffusion models.

This study analyzes how RNNs process context in sentiment analysis.

Thompson Sampling bounds for contextual bandits with sub-Gaussian rewards.

CADR estimator improves inference for contextual bandit data.

Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification. When dealing with mixture models, there are many issues that the experimenter should be aware of and needs to solve. The MixEst toolbox is a powerful and user-friendly package for M…

The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…

New algorithm learns optimal exploration parameters for contextual bandits.

Survey on Bayesian inference for Gaussian mixture models.

The paper tackles learning varying DAG structures based on contextual features.

A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual at…

A framework for auto-tuning hyper-parameters in contextual bandit algorithms.

New algorithm improves learning efficiency in multi-task contextual bandits.

We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms that are designed solely for one of the regimes are known to be sub-optimal for the alternate regime…

When estimating finite mixture models, it is common to make assumptions on the mixture components, such as parametric assumptions. In this work, we make no distributional assumptions on the mixture components and instead assume that observations from the mixture model are grouped, such that observations in the same gro…

New algorithm for contextual dueling bandits achieves nearly optimal regret.

The thesis evaluates and compares extreme mixture models in finance and insurance.

New method for contextual bandit with missing rewards.

New method estimates mixture model components efficiently.

New method uses subtractive mixture models for approximate inference.

In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely us…

The paper tackles minimax optimality in continuum contextual bandits with Hölder continuity.

New algorithms improve spectral clustering for finite mixture models.

Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the Expectation Maximization (EM) algorithm, in which updates are easy to derive for a wide class of mixture models. However, the likelihood of…

LMC-TS uses MCMC for efficient posterior sampling in contextual bandits.