arXiv research

Method constructs confidence regions for linear models with arbitrary predictors.

Study shows NCCP can replace CP for ACI in non-exchangeable data.

A new method for predicting with confidence for complex models.

Develops a new method for uncertainty quantification in high-dimensional learning.

Study explores efficient data division for ICPs.

We provide a pointwise confidence bound for non-linear least-squares with fixed design.

Improves reward bounds for prediction with expert advice using abstention.

Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.

We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given…

We consider using an ensemble of binary classifiers for transductive prediction, when unlabeled test data are known in advance. We derive minimax optimal rules for confidence-rated prediction in this setting. By using PAC-Bayes analysis on these rules, we obtain data-dependent performance guarantees without distributio…

New method optimizes offline linear bandits using different confidence sets.

New truthful calibration errors improve model ranking in multiclass prediction.

PML-LFC improves PML by estimating label confidence from both feature and label spaces.

Paper proposes a method to predict MOBA game winners with calibrated confidence.

New method uses machine learning to improve statistical inference.

Conformal predictors, introduced by Vovk et al. (2005), serve to build prediction intervals by exploiting a notion of conformity of the new data point with previously observed data. In the present paper, we propose a novel method for constructing prediction intervals for the response variable in multivariate linear mod…

A framework uses a mixture of predictors for semi-supervised inference.

We study the model selection problem in conditional average treatment effect (CATE) prediction. Unlike previous works on this topic, we focus on preserving the rank order of the performance of candidate CATE predictors to enable accurate and stable model selection. To this end, we analyze the model performance ranking …

AM-PPI uses multiple predictors to reduce label cost in healthcare AI.

The paper studies calibration in ML models for wireless networks, showing key theoretical and practical insights.

The paper uses conformal prediction to detect railway signals with confidence.

A regularized risk minimization procedure for regression function estimation is introduced that achieves near optimal accuracy and confidence under general conditions, including heavy-tailed predictor and response variables. The procedure is based on median-of-means tournaments, introduced by the authors in [8]. It is …

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expec…

While the use of deep learning in drug discovery is gaining increasing attention, the lack of methods to compute reliable errors in prediction for Neural Networks prevents their application to guide decision making in domains where identifying unreliable predictions is essential, e.g. precision medicine. Here, we prese…

Bayesian sequence prediction is a simple technique for predicting future symbols sampled from an unknown measure on infinite sequences over a countable alphabet. While strong bounds on the expected cumulative error are known, there are only limited results on the distribution of this error. We prove tight high-probabil…

We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, …

LqgOpt learns optimal control in unknown LQG systems with minimal regret.

Link prediction in a graph is the problem of detecting the missing links that would be formed in the near future. Using a graph representation of the data, we can convert the problem of classification to the problem of link prediction which aims at finding the missing links between the unlabeled data (unlabeled nodes) …

This work investigates uncertainty quantification for black-box large language models in natural language generation.

Paper discusses optimal CP for second-order predictions.

ATC predicts target domain accuracy using only labeled and unlabeled data.

Functional PLS improves prediction and inference for scalar responses from functional predictors.

MALT improves adversarial attacks by targeting classes more efficiently.

New algorithm reduces sample complexity for multi-task bandits.

Proposes two-stage robust and sparse distributed inference for large-scale data.

Study combines VaR and ES forecasts using MCS to improve risk predictions.

The paper tackles time series data by applying conformal prediction with nearest neighbors.

Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indi…

Let $(X,Y)$ be a random variable consisting of an observed feature vector $X\in \mathcal{X}$ and an unobserved class label $Y\in \{1,2,...,L\}$ with unknown joint distribution. In addition, let $\mathcal{D}$ be a training data set consisting of $n$ completely observed independent copies of $(X,Y)$. Usual classification…

Proposes a method to quantify uncertainty in DNN models for discrete inputs.

We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets. We formulate the learning approach using a covariance-fitting methodology, and show that the resulting predictor has desirable computational and distribution-free properties: It is implemented onli…

MEC improves efficiency and robustness in semi-supervised inference.

Project aims to improve time series prediction intervals.

New method reduces uncertainty in deep neural networks with minimal computation.

Paper argues for using functional theory of randomness for better understanding of data exchangeability and conformal prediction.

Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertainty in data integration. Mismatched pairs can considerably impact the …

Paper develops an efficient method for conformal prediction in sparse linear models.

Study finds simple model-agreement scores perform well in various error estimation scenarios.