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Roman Vaculin

3 accepted papers

2026

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring.

ICLR 2026poster

We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additional fine-tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p-value)…

Cited by 0Source ↗code ↗Scholar ↗
2024

Identifying Homogeneous and Interpretable Groups for Conformal Prediction

UAI 2024poster

Conformal prediction methods are a tool for uncertainty quantification of a model’s prediction, providing a model-agnostic and distribution-free statistical wrapper that generates prediction intervals/sets for a given model with finite sample generalization guarantees. However, these guarantees hol…

Cited by 9Source ↗PDF ↗Scholar ↗
2019

Differentially Private Distributed Data Summarization under Covariate Shift

NeurIPS 2019poster

We envision Artificial Intelligence marketplaces to be platforms where consumers, with very less data for a target task, can obtain a relevant model by accessing many private data sources with vast number of data samples. One of the key challenges is to construct a training dataset that matches a t…

Cited by 8Source ↗PDF ↗Scholar ↗