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Peter Hayes
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2020 – today
- 2024
- [i7]William Muldrew, Peter Hayes, Mingtian Zhang, David Barber:
Active Preference Learning for Large Language Models. CoRR abs/2402.08114 (2024) - [i6]Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber:
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. CoRR abs/2402.12177 (2024) - 2023
- [j1]Michael Morris
, Peter Hayes, Ingemar J. Cox
, Vasileios Lampos
:
Neural network models for influenza forecasting with associated uncertainty using Web search activity trends. PLoS Comput. Biol. 19(8) (2023) - 2022
- [c2]Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. NeurIPS 2022 - [i5]Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. CoRR abs/2205.11640 (2022) - [i4]Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber:
Integrated Weak Learning. CoRR abs/2206.09496 (2022) - [i3]Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol:
Towards Healing the Blindness of Score Matching. CoRR abs/2209.07396 (2022) - 2021
- [i2]Michael Morris, Peter Hayes, Ingemar J. Cox, Vasileios Lampos:
Estimating the Uncertainty of Neural Network Forecasts for Influenza Prevalence Using Web Search Activity. CoRR abs/2105.12433 (2021) - [i1]Emine Yilmaz, Peter Hayes, Raza Habib, Jordan Burgess, David Barber:
Sample Efficient Model Evaluation. CoRR abs/2109.12043 (2021) - 2020
- [c1]Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber:
Spread Divergence. ICML 2020: 11106-11116
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