arXiv research

The paper proposes a method to identify subgroups with different treatment effects in time-to-event data.

Transforming sparse outcomes into dense process rewards for efficient reinforcement learning.

New algorithm learns sparse GLMs for binary outcomes efficiently.

New algorithm reduces semi-bandit regret using covariance estimates.

PEER tackles multi-response regression with incomplete outcomes efficiently.

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse. This structure is both parsimonious and highly interpretable, as it implies that the outcome is related…

Cluster analysis methods are used to identify homogeneous subgroups in a data set. In biomedical applications, one frequently applies cluster analysis in order to identify biologically interesting subgroups. In particular, one may wish to identify subgroups that are associated with a particular outcome of interest. Con…

Develops a new multivariate regression model for complex outcomes.

We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…

We derive fundamental sample complexity bounds for recovering sparse and structured signals for linear and nonlinear observation models including sparse regression, group testing, multivariate regression and problems with missing features. In general, sparse signal processing problems can be characterized in terms of t…

Two new estimators reduce costs and improve accuracy for EHR outcome prediction.

Improved AutoDML estimator for causal inference using outcome-adapted shared covariate representation.

This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations…

Develops method to assess feature importance in black-box models for unconditional distribution.

Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.

We develop a novel method for counterfactual analysis based on observational data using prediction intervals for units under different exposures. Unlike methods that target heterogeneous or conditional average treatment effects of an exposure, the proposed approach aims to take into account the irreducible dispersions …

Improves treatment effect estimation by reducing sample size needed.

Adaptive prediction timing improves healthcare outcomes by predicting patient events at the right frequency.

Efficiently identifies important variables in binary outcomes using variational Bayes.

The paper analyzes sparse high-dimensional linear regression with random design and unknown error variance, providing adaptiveness and concentration rates.

New approach uses unlabeled prior data to accelerate exploration in sparse reward tasks.

Optimal multitask learning method for sparse heterogeneous datasets.

New algorithm makes machine learning fairer by removing bias from data.

Develops a SAS approach for high-dimensional risk prediction using unlabeled data.

Algorithm optimizes bandit decisions with changing action sets using Gaussian processes.

Successfully navigating a complex environment to obtain a desired outcome is a difficult task, that up to recently was believed to be capable only by humans. This perception has been broken down over time, especially with the introduction of deep reinforcement learning, which has greatly increased the difficulty of tas…

The paper shows how instrumental variables can help identify sparse causal effects in linear models.

Gene expression data represents a unique challenge in predictive model building, because of the small number of samples $(n)$ compared to the huge amount of features $(p)$. This "$n<<p$" property has hampered application of deep learning techniques for disease outcome classification. Sparse learning by incorporating ex…

Kernel means are frequently used to represent probability distributions in machine learning problems. In particular, the well known kernel density estimator and the kernel mean embedding both have the form of a kernel mean. Unfortunately, kernel means are faced with scalability issues. A single point evaluation of the …

Exact simulation of correlated binary outcomes using PMF constraints and linear programming.

We demonstrate that, in the classical non-stochastic regret minimization problem with $d$ decisions, gains and losses to be respectively maximized or minimized are fundamentally different. Indeed, by considering the additional sparsity assumption (at each stage, at most $s$ decisions incur a nonzero outcome), we derive…

Study on estimating causal effects with limited data and multiple environments.

Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent years, machine learning tools have been developed to aid this process. Intensive care unit (ICU) admissions represent the most data dense and t…

Proposes a method to correct for covariate shift in meta-analysis of randomized trials.

As a popular tool for producing meaningful and interpretable models, large-scale sparse learning works efficiently when the underlying structures are indeed or close to sparse. However, naively applying the existing regularization methods can result in misleading outcomes due to model misspecification. In particular, t…

UncertaintyPlayground simplifies uncertainty estimation in Python.

Paper proposes RRD to learn proxy rewards for sparse delayed rewards in episodic reinforcement learning.

Traditional semantic similarity models often fail to encapsulate the external context in which texts are situated. However, textual datasets generated on mobile platforms can help us build a truer representation of semantic similarity by introducing multimodal data. This is especially important in sparse datasets, maki…

Sparse GFA identifies disease factors in FTD subgroups.

Sparse matrix decomposition identifies key design variables for ICF experiments.

We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function. We focus on a class of parameters that have influence function which depends on two infinite dimensiona…

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We study a consideration for the expl…

Develops a method to infer partial rankings from sparse comparisons.

Improves matrix completion by exploiting biased observation patterns.

Outlier detection aims to identify unusual data instances that deviate from expected patterns. The outlier detection is particularly challenging when outliers are context dependent and when they are defined by unusual combinations of multiple outcome variable values. In this paper, we develop and study a new conditiona…

FIDDLE uses deep learning to estimate ATE from complex data.

Choice models, which capture popular preferences over objects of interest, play a key role in making decisions whose eventual outcome is impacted by human choice behavior. In most scenarios, the choice model, which can effectively be viewed as a distribution over permutations, must be learned from observed data. The ob…

U-aggregation combines multiple models without labels for better risk prediction.