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2020 – today
- 2024
[i8]Andrea Schioppa:
Gradient Sketches for Training Data Attribution and Studying the Loss Landscape. CoRR abs/2402.03994 (2024)- [i7]Andrea Schioppa, Emiel Hoogeboom, Jonathan Heek:
Model Integrity when Unlearning with T2I Diffusion Models. CoRR abs/2411.02068 (2024) - 2023
- [j1]Andrea Schioppa, Nal Kalchbrenner:
Stacking Diverse Architectures to Improve Machine Translation. Trans. Mach. Learn. Res. 2023 (2023) - [c3]Andrea Schioppa, Katja Filippova, Ivan Titov, Polina Zablotskaia:
Theoretical and Practical Perspectives on what Influence Functions Do. NeurIPS 2023 - [i6]Andrea Schioppa, Xavier Garcia, Orhan Firat:
Cross-Lingual Supervision improves Large Language Models Pre-training. CoRR abs/2305.11778 (2023) - [i5]Andrea Schioppa, Katja Filippova, Ivan Titov, Polina Zablotskaia:
Theoretical and Practical Perspectives on what Influence Functions Do. CoRR abs/2305.16971 (2023) - 2022
- [c2]Andrea Schioppa, Polina Zablotskaia, David Vilar, Artem Sokolov:
Scaling Up Influence Functions. AAAI 2022: 8179-8186 - 2021
- [c1]Andrea Schioppa, David Vilar, Artem Sokolov, Katja Filippova:
Controlling Machine Translation for Multiple Attributes with Additive Interventions. EMNLP (1) 2021: 6676-6696 - [i4]Andrea Schioppa, Polina Zablotskaia, David Vilar, Artem Sokolov:
Scaling Up Influence Functions. CoRR abs/2112.03052 (2021)
2010 – 2019
- 2019
- [i3]Andrea Schioppa:
Learning to Transport with Neural Networks. CoRR abs/1908.01394 (2019) - [i2]Andrea Schioppa:
Distributed Function Minimization in Apache Spark. CoRR abs/1909.07922 (2019) - 2018
- [i1]Andrea Schioppa:
Optimality of the final model found via Stochastic Gradient Descent. CoRR abs/1810.09418 (2018)
Coauthor Index
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