arXiv research

Unified minimax value interval for off-policy evaluation and optimization.

Study online learning with set-valued feedback, showing differences between deterministic and randomized approaches.

Motivated by the growing popularity of variants of the Wasserstein distance in statistics and machine learning, we study statistical inference for the Sliced Wasserstein distance--an easily computable variant of the Wasserstein distance. Specifically, we construct confidence intervals for the Sliced Wasserstein distanc…

The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.

Improved confidence interval estimation with control variates.

Proposes online debiasing estimators for adaptive linear regression.

Paper introduces a new method for classifying interval-valued time series.

Option contracts are a type of financial derivative that allow investors to hedge risk and speculate on the variation of an asset's future market price. In short, an option has a particular payout that is based on the market price for an asset on a given date in the future. In 1973, Black and Scholes proposed a valuati…

Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity. In this paper, we provide a general framework for understanding the frequentist coverage of point-wise and simultaneous Bayesian credible sets in GP regression. As an intermediate result, we develop…

Study optimality conditions for interval-valued optimization problems on Riemannian manifolds.

New methods for ordinal classification of interval-valued data and functional data.

Paper proposes a 1-bit mean estimation method with near-optimal sample complexity.

Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.

Paper simulates LR fuzzy intervals with interval-valued cores.

Extends Fisher's Discriminant Analysis for interval-valued data.

The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of s…

Paper compares Bayesian and de-biased estimators for low-rank matrix completion.

Proposes adaptive method for classifying interval-valued time series.

New model uses interval-valued CVaR for better risk assessment in finance.

We consider the estimation of two-sample integral functionals, of the type that occur naturally, for example, when the object of interest is a divergence between unknown probability densities. Our first main result is that, in wide generality, a weighted nearest neighbour estimator is efficient, in the sense of achievi…

Paper tackles online learning with interval regret, achieving adaptive bounds.

The paper optimizes risk-sensitive RL with CVaR, achieving near-minimax-optimal results.

Study KKT conditions for multi-objective optimization on Hadamard manifolds.

Method constructs nonparametric prediction intervals with finite-sample guarantees.

Two algorithms for linear contextual bandits with rare updates achieve optimal regret and efficiency.

PIVEN predicts both specific values and prediction intervals.

Pessimistic Minimax Value Iteration finds efficient NE policies from offline data.

Paper tackles linear models with missing values, achieving minimax optimal results.

The paper explores heat flow and constants on graphs, proving properties and proposing new concepts.

New algorithms find optimal policies without knowing MDP span.

New RL method nearly optimally learns policies with generative models.

Study compares Interval Regression models and their performance.

Paper proposes a method to create more reliable confidence intervals for off-policy evaluations.

Study compares imputation methods' effects on IML confidence intervals.

Paper discusses directional differentiability of interval-valued functions on Riemannian manifolds.

Proposes a deep learning framework for interval-censored survival data.

This work uses statistical bootstrapping to provide accurate confidence intervals for policy value in reinforcement learning.

Minimax linkage was first introduced by Ao et al. [3] in 2004, as an alternative to standard linkage methods used in hierarchical clustering. Minimax linkage relies on distances to a prototype for each cluster; this prototype can be thought of as a representative object in the cluster, hence improving the interpretabil…

Proposes a method for obtaining interval bounds in off-policy evaluation.

Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high…

New algorithms solve complex minimax problems without needing derivatives.

It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…

Many problems in financial engineering involve the estimation of unknown conditional expectations across a time interval. Often Least Squares Monte Carlo techniques are used for the estimation. One method that can be combined with Least Squares Monte Carlo is the "Regress-Later" method. Unlike conventional methods wher…

Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are…

Proposes a non-crossing deep neural network quantile regression method.

INNs produce interval-valued uncertainty scores for DNNs.

We give a comprehensive theoretical characterization of a nonparametric estimator for the $L_2^2$ divergence between two continuous distributions. We first bound the rate of convergence of our estimator, showing that it is $\sqrt{n}$-consistent provided the densities are sufficiently smooth. In this smooth regime, we t…

Paper extends Bayes Theorem for interval probability estimates.