Towards a theory of out-of-distribution learning

By Ali Geisa, Ronak D. Mehta, Hayden S. Helm, Jayanta Dey, Haoyin Xu, Eric Eaton, Carey E. Priebe, Joshua T. Vogelstein. 2021

Subfields: Statistical Learning Theory

How researchers see this paper

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In Statistical Learning Theory

1. Hayden S. Helm
2. Carey E. Priebe
3. Weiwei Yang
4. Brandon Duderstadt

Citations in the corpus

• Tracking the perspectives of interacting language models (2024, background): “Our work is closely related to simulating groups of computational agents to study sociological and cultural phenomena (Steels, 1990; Wagner et al., 2003) and to continual learning (Vogelstein et al., 2020; Geisa et al., 2021).”
• Mental State Classification Using Multi-Graph Features (2022, background): “This dip in performance is again indicative of the proximity of the distributions between sessions and can be smoothed out by continual or lifelong learning techniques [34, 35, 36].”