In this paper, we present a Conformal Prediction method that computes prediction sets in a
one-shot Federated Learning (FL) setting. More specifically, we introduce a novel quantile-of …

Bagging is an important technique for stabilizing machine learning models. In this paper, we
derive a finite-sample guarantee on the stability of bagging for any model. Our result places …

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification …

Given a sequence of observable variables $\{(x_1, y_1),\ldots,(x_n, y_n)\} $, the conformal
prediction method estimates a confidence set for $ y_ {n+ 1} $ given $ x_ {n+ 1} $ that is …

Network traffic prediction techniques have attracted much attention since they are valuable
for network congestion control and user experience improvement. While existing prediction …

This paper studies the open problem of conformalized entry prediction in a row/column-
exchangeable matrix. The matrix setting presents novel and unique challenges, but there …

Bagging is an important technique for stabilizing machine learning models. In this paper, we
derive a finite-sample guarantee on the stability of bagging for any model. Our result places …

Despite the recent progress in deep neural networks (DNNs), it remains challenging to
explain the predictions made by DNNs. Existing explanation methods for DNNs mainly focus …

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification …

The task of distinguishing individuals of interest from a vast pool of candidates using
predictive models has garnered significant attention in recent years. This task can be framed …