arXiv research

The paper introduces new measures to quantify variability in decision tree models due to observational multiplicity.

Detect hidden confounding in observational data using multiple environments.

RAMEN corrects observational data biases for multiple environments.

New method combines multiple datasets to estimate ATE with valid confidence intervals.

A technique uncovers latent causal relationships in multiple time series data.

New method estimates causal effects of multiple versions of treatment.

A new method combines scores of individual observations to efficiently approximate posterior distributions.

The paper extends multiple instance learning to multiclass and regression problems.

Approach generates multiple correct predictions from single supervision.

The paper tackles robust policy learning from multiple data sources.

Enhanced feedback model improves sample-efficiency in POMDPs.

Algorithm detects influential observations in high-dimensional data.

Meta-learners estimate CATE from multiple environments with partial identification.

A new method detects hidden driving forces in systems with multiple observables.

Paper introduces a Bayesian nonparametric approach for tracking multiple objects with spawning events.

Study learns dynamics of linear systems from multiple short trajectories.

Many learning tasks, such as cross-validation, parameter search, or leave-one-out analysis, involve multiple instances of similar problems, each instance sharing a large part of learning data with the others. We introduce a robust framework for solving multiple square-root LASSO problems, based on a sketch of the learn…

New method for analyzing multiple longitudinal data processes.

This work introduces a method to learn dynamical systems from noisy sensor measurements using multiple shooting.

Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that…

Unified framework for simulation-based inference learns a single model for multiple tasks.

Observable structures of a topological field theory of AKSZ type are analyzed. From a double (or multiple) complex structure of observable algebras, new topological invariants are constructed. Especially, Donaldson polynomial invariants and their generalizations are constructed from a topological field theory of AKSZ t…

Harmoniums model multiple time-to-event variables in survival analysis.

Adapts manifold structure for better clustering performance.

SMTM improves MCMC sampling in high dimensions with multiple proposals and stereographic integration.

This article develops a deep reinforcement learning (Deep-RL) framework for dynamic pricing on managed lanes with multiple access locations and heterogeneity in travelers' value of time, origin, and destination. This framework relaxes assumptions in the literature by considering multiple origins and destinations, multi…

Can humans impute missing data with similar proficiency as machines? This is the question we aim to answer in this paper. We present a novel idea of converting observations with missing data in to a survey questionnaire, which is presented to crowdworkers for completion. We replicate a multiple imputation framework by …

ICIL learns policies invariant to multiple environments, improving generalization.

Estimates treatment effect using ratio of potential outcomes in MS patients.

Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] showe…

Model predicts future values and fills in missing data.

Paper introduces SMM for forecasting multiple time series with missing values.

Unified analysis of generalization curves in large models using gradient flow.

Paper addresses state estimation in sensor networks with intermittent data.

Overfitting occurs when RL agents correlate rewards with spurious observation features.

The paper tackles counterfactual inference with multioutput deep kernels in high-dimensional settings.

PREMA recovers detailed data from aggregated views.

New process explains asset volatility patterns.

This paper improves prediction accuracy for multi-input classification tasks using p-value aggregation.

MDCN improves treatment effect estimation in multicenter observational studies.

Comment refutes the deconfounder method's premise about ignorability.

We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified vari…

Motivated by clinical trials, we study bandits with observable non-compliance. At each step, the learner chooses an arm, after, instead of observing only the reward, it also observes the action that took place. We show that such noncompliance can be helpful or hurtful to the learner in general. Unfortunately, naively i…

Most real life systems have a random component: the multitude of endogenous and exogenous factors influencing them result in stochastic fluctuations of the parameters determining their dynamics. These empirical systems are in many cases subject to noise of multiplicative nature. The special properties of multiplicative…

We propose a probabilistic model for inferring the multivariate function from multiple areal data sets with various granularities. Here, the areal data are observed not at location points but at regions. Existing regression-based models can only utilize the sufficiently fine-grained auxiliary data sets on the same doma…

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we al…

Multiplicative noise, including dropout, is widely used to regularize deep neural networks (DNNs), and is shown to be effective in a wide range of architectures and tasks. From an information perspective, we consider injecting multiplicative noise into a DNN as training the network to solve the task with noisy informat…

We study the classification problem of singularities of function-germs with harmonic leading terms of two variables under the right-equivalence. We observe that the multiple actions of Laplacian appear for the classifications of such class of function-germs.