Bayesian optimization (BO) with Gaussian processes (GPs) surrogate models is widely used
to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we …

In many applications, ranging from logistics to engineering, a designer is faced with a
sequence of optimization tasks for which the objectives are in the form of black-box functions …

Adjustable hyperparameters of machine learning models typically impact various key trade-
offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is to find …

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a
conventional point predictor to provide calibration guarantees. Generalizing conformal …

Optimization-based controller tuning is challenging because it requires formulating
optimization problems explicitly as functions of controller parameters. Safe learning …

Post-hoc calibration of pre-trained models is critical for ensuring reliable inference,
especially in safety-critical domains such as healthcare. Conformal Prediction (CP) offers a …

As machine learning (ML) gains widespread adoption, practitioners are increasingly seeking
means to quantify and control the risk these systems incur. This challenge is especially …

Adaptive Risk Control (ARC) is an online calibration strategy based on set prediction that
offers worst-case deterministic long-term risk control, as well as statistical marginal coverage …

Optimizing process outcomes by tuning parameters through an automated system is
common in industry. Ideally, this optimization is performed as efficiently as possible, using …

As artificial intelligence (AI)/machine learning (ML) gain widespread adoption, practitioners
are increasingly seeking means to quantify and control the risk these systems incur. This …