arXiv research

The paper tackles misspecification in contextual bandits by incorporating arm-specific variables.

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary dif…

This work introduces CAET, an algorithm for cost-aware pairwise pure exploration.

Optimal algorithm for high-dimensional stochastic linear bandits with sparse parameters.

A new method for adaptive experiments improves inference.

Optimal algorithm for identifying best-arm with minimal regret.

Optimism stabilizes Thompson Sampling for adaptive inference in multi-armed bandits.

Kernelized bandit algorithm tackles adaptive contextual bandits with single-index models.

New algorithm reduces regret in GLM bandits with tighter bounds.

The paper develops algorithms to minimize misallocation and identify the arm with the highest variance.

Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…

VC-PCR improves prediction by clustering correlated variables.

In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To …

Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains. However, current notions of variable importance are often tied to a specific predictive model. This is problematic: what if there were multiple well-performing predictive models, and …

A neural network finds causal relationships among latent variables.

Derives derivatives and geometric framework for functions with non-independent variables.

A new distance for mixed-variable, hierarchical datasets with meta variables.

Unified Bayesian Optimisation for mixed variables improves performance.

In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To th…

Random Forest variable importance is improved by class balancing techniques.

Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal input variables i…

Knoop enhances variable selection with over-parameterization and knockoffs.

A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden variables poses two problems: determining the relations to other variables in the model and determining the number of states of …

New method for fitting graphical models with latent variables using regularized conditional likelihood.

CIB compresses variables causally, preserving key causal interactions.

We generalize to the finite-state case the notion of the extreme effect variable $Y$ that accumulates all the effect of a variant variable $V$ observed in changes of another variable $X$. We conduct theoretical analysis and turn the problem of finding of an effect variable into a problem of a simultaneous decomposition…

Discond-VAE separates continuous and discrete factors in data.

A new method selects important variables for clustering from dependency networks.

The paper introduces methods to identify key variables discriminating between two datasets.

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables are useful in predicting clinical outcomes can be challenging. Advanced algorithms such as deep neural networks were designed to process …

This work presents entropic constraints from DAGs with hidden variables.

For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim t…

Proposes a two-stage method for selecting correlated predictors in high-dimensional data.

We propose a novel application of the Simultaneous Orthogonal Matching Pursuit (S-OMP) procedure for sparsistant variable selection in ultra-high dimensional multi-task regression problems. Screening of variables, as introduced in \cite{fan08sis}, is an efficient and highly scalable way to remove many irrelevant variab…

New method better identifies irrelevant variables for more accurate treatment effect estimation.

The paper proposes a method to stabilize predictions by identifying causal variables using a seed variable.

Random forest hyperparameters affect variable selection in omics studies.

Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…

A new variable importance measure for DRFs detects broader impacts on output distributions.

The paper investigates causal relationships in heart failure prediction using machine learning.

This review explores the use of machine learning in discovering collective variables for biomolecular dynamics.

Researchers identify latent variables and causal structures from nonlinear hierarchical models.

We propose a procedure for assigning a relevance measure to each explanatory variable in a complex predictive model. We assume that we have a training set to fit the model and a test set to check the out of sample performance. First, the individual relevance of each variable is computed by comparing the predictions in …

New algorithm for learning RBMs with sparse latent variables.

The paper shows how neural networks with less decision boundary variability generalize better.

Improved DSSMs for easier interpretable latent variables.

The architectures of deep neural networks (DNN) rely heavily on the underlying grid structure of variables, for instance, the lattice of pixels in an image. For general high dimensional data with variables not associated with a grid, the multi-layer perceptron and deep belief network are often used. However, it is freq…

Second-order economic theory considers new variables to improve price volatility predictions.