arXiv research

AUCRSS detects change points in partially observed multivariate autocorrelated data.

The SPS method constructs confidence regions for true parameters with optimal sample complexity.

An automatic machine learning (AutoML) task is to select the best algorithm and its hyper-parameters simultaneously. Previously, the hyper-parameters of all algorithms are joint as a single search space, which is not only huge but also redundant, because many dimensions of hyper-parameters are irrelevant with the selec…

The paper develops optimal confidence regions for categorical data.

The paper improves confidence ellipsoids for ridge regression with PAC bounds.

Method constructs confidence regions for linear models with arbitrary predictors.

This paper analyzes the sample complexity of SPS method for scalar linear regression.

We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter. The arms are divided into different groups, each of which has a common parameter. Therefore, when the player selects an arm at each time slot, information of other arms in the sam…

Introduces CCR for constructing confidence regions from conformal predictions.

Constructs non-asymptotic confidence regions for unknown functions in RKHS.

Develops an efficient approximation for full conformal prediction regions.

General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions. A way to circumvent potential restrictions is to use conformal methods for constructing non-parametric confidence regions, that offer guarantees regarding validity. In this paper we provide a detailed descripti…

Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by the ambiguity set---the set of plausible transition probabilities---which is usually constructed as a multi-dimensional confidence region. E…

Waldo method constructs valid confidence regions for simulator-based inference.

The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.

A new algorithm, Regular Tree Search, tackles non-convex simulation optimization problems.

The paper develops methods for constructing confidence regions for regression functions in binary classification.

Framework for confidence estimation in deep CT reconstructions.

The paper honors Lai's contributions to multi-armed bandits and establishes new regret bounds.

The paper develops a method to create non-asymptotic confidence ellipsoids for linear regression without strong noise distribution assumptions.

This paper introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytical Lyapunov functions, which are subject to two limitations: the analyti…

A new method, Residual-Permuted Sums, improves confidence region construction for linear regression models.

A novel efficient method for computing the Knowledge-Gradient policy for Continuous Parameters (KGCP) for deterministic optimization is derived. The differences with Expected Improvement (EI), a popular choice for Bayesian optimization of deterministic engineering simulations, are explored. Both policies and the Upper …

This paper improves GP-UCB by using a shifted exponential distribution for confidence parameters.

The estimation of probabilities of default (PDs) for low default portfolios by means of upper confidence bounds is a well established procedure in many financial institutions. However, there are often discussions within the institutions or between institutions and supervisors about which confidence level to use for the…

A new method to measure neural network expressiveness using tighter upper bounds.

Proposes a new Hawkes process bandit model for disaster search and rescue.

Paper improves regret bounds for Gaussian process upper confidence bound in Bayesian optimization.

This note gives a short, self-contained, proof of a sharp connection between Gittins indices and Bayesian upper confidence bound algorithms. I consider a Gaussian multi-armed bandit problem with discount factor $γ$. The Gittins index of an arm is shown to equal the $γ$-quantile of the posterior distribution of the arm'…

Paper develops methods for PCA inference with missing data and heteroskedastic noise.

In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for dealing with non-stationary and large-state spaces multi-armed bandit problems. The proposed method has been targeted to the problem of the …

A new differentiable UCB algorithm for linear bandits learns adaptive confidence bounds.

Region crossing change for a knot or a proper link is an unknotting operation. In this paper, we provide a sharp upper bound on the region unknotting number for a large class of torus knots and proper links. Also, we discuss conditions on torus links to be proper.

Proposes a new framework for deep learning conditional mean estimation with confidence regions.

Improved Bayesian optimisation method using randomised Gaussian process UCB.

PaRCE estimates model confidence for CNNs across various uncertainties.

One-bit feedback suffices for a bandit problem's optimal strategy.

Bayesian methods improve drug discovery experiment design.

In this paper, we study a simple algorithm to construct asymptotically valid confidence regions for model parameters using the batch means method. The main idea is to cancel out the covariance matrix which is hard/costly to estimate. In the process of developing the algorithm, we establish process-level functional cent…

DRF improves confidence and uncertainty assessment for multivariate conditional distributions.

We use PDPs with confidence bands to explain HPO results.

The study estimates the expressiveness of GCNs with bounds on the number of linear regions.

A new method optimizes robustness measures under input uncertainty using randomized Gaussian process upper confidence bound.

New algorithm reduces regret and constraint violation in adversarial CMDP learning.

We propose a new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations. We construct an influence function by projecting the fitted estimating equations to a sparse direction obtained by solving a large-scale…

In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function $f$. Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to $f$ may be obtainable. For example, the exp…

New algorithm learns safe policies in unknown environments.

In this paper we present a new approach for tightening upper bounds on the partition function. Our upper bounds are based on fractional covering bounds on the entropy function, and result in a concave program to compute these bounds and a convex program to tighten them. To solve these programs effectively for general r…