# Learning in the Wild with Incremental Skeptical Gaussian Processes

arXiv: https://arxiv.org/abs/2011.00928 · HTML: https://mlsanity.com/paper/2011.00928/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 13 of 13)

- [Leveraging Explanations in Interactive Machine Learning: An Overview](https://mlsanity.com/paper/2207.14526/) (2022)
- [Interactive Label Cleaning with Example-based Explanations](https://mlsanity.com/paper/2106.03922/) (2021)
- [Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection](https://mlsanity.com/paper/2210.10487/) (2022)
- [Human-in-the-loop Handling of Knowledge Drift](https://mlsanity.com/paper/2103.14874/) (2021)
- [Online Decision Mediation](https://mlsanity.com/paper/2310.18601/) (2023)
- [Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs](https://mlsanity.com/paper/2202.08566/) (2022)
- [Lifelong Personal Context Recognition](https://mlsanity.com/paper/2205.10123/) (2022)
- [Streaming and Learning the Personal Context](https://mlsanity.com/paper/2108.08234/) (2021)
- [Bridging the Gap in Hybrid Decision-Making Systems](https://mlsanity.com/paper/2409.19415/) (2024)
- [A Frank System for Co-Evolutionary Hybrid Decision-Making](https://mlsanity.com/paper/2503.06229/) (2025)
- [Factors Impacting the Quality of User Answers on Smartphones](https://mlsanity.com/paper/2306.00627/) (2023)
- [Help the machine to help you: an evaluation in the wild of egocentric data cleaning via skeptical learning](https://mlsanity.com/paper/2510.23635/) (2025)
- [Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support](https://mlsanity.com/paper/2610.12666/) (2026)

## Abstract (arXiv)

The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI. Two central challenges for deploying interactive learners in the wild are the unreliable nature of the supervision and the varying complexity of the prediction task. We address a simple but representative setting, incremental classification in the wild, where the supervision is noisy and the number of classes grows over time. In order to tackle this task, we propose a redesign of skeptical learning centered around Gaussian Processes (GPs). Skeptical learning is a recent interactive strategy in which, if the machine is sufficiently confident that an example is mislabeled, it asks the annotator to reconsider her feedback. In many cases, this is often enough to obtain clean supervision. Our redesign, dubbed ISGP, leverages the uncertainty estimates supplied by GPs to better allocate labeling and contradiction queries, especially in the presence of noise. Our experiments on synthetic and real-world data show that, as a result, while the original formulation of skeptical learning produces over-confident models that can fail completely in the wild, ISGP works well at varying levels of noise and as new classes are observed.