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2020 – today
- 2024
[j3]Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini:
Personalized Algorithmic Recourse with Preference Elicitation. Trans. Mach. Learn. Res. 2024 (2024)- [c1]Seyedehdelaram Esfahani
, Giovanni De Toni, Bruno Lepri, Andrea Passerini, Katya Tentori, Massimo Zancanaro:
Preference Elicitation in Interactive and User-centered Algorithmic Recourse: an Initial Exploration. UMAP 2024: 249-254 - [i7]Seyedehdelaram Esfahani, Giovanni De Toni, Bruno Lepri, Andrea Passerini, Katya Tentori, Massimo Zancanaro:
Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration. CoRR abs/2404.05270 (2024) - [i6]Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez-Rodriguez:
Towards Human-AI Complementarity with Predictions Sets. CoRR abs/2405.17544 (2024) - [i5]Giovanni De Toni, Stefano Teso, Bruno Lepri, Andrea Passerini:
Time Can Invalidate Algorithmic Recourse. CoRR abs/2410.08007 (2024) - 2023
- [j2]Giovanni De Toni, Bruno Lepri, Andrea Passerini:
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis. Mach. Learn. 112(4): 1389-1409 (2023) - 2022
- [i4]Giovanni De Toni, Bruno Lepri, Andrea Passerini:
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis. CoRR abs/2201.07135 (2022) - [i3]Giovanni De Toni, Paolo Viappiani, Bruno Lepri, Andrea Passerini:
Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences. CoRR abs/2205.13743 (2022) - 2021
- [i2]Giovanni De Toni, Luca Erculiani, Andrea Passerini:
Learning compositional programs with arguments and sampling. CoRR abs/2109.00619 (2021) - 2020
- [j1]Mainak Jas, Titipat Achakulvisut, Aid Idrizovic, Daniel E. Acuna, Matthew Antalek, Vinícius Marques, Tommy Odland, Ravi Garg, Mayank Agrawal, Yu Umegaki, Peter Foley, Hugo Fernandes, Drew Harris, Beibin Li, Olivier Pieters, Scott Otterson, Giovanni De Toni, Chris C. Rodgers, Eva L. Dyer, Matti S. Hämäläinen, Konrad P. Körding, Pavan Ramkumar:
Pyglmnet: Python implementation of elastic-net regularized generalized linear models. J. Open Source Softw. 5(47): 1959 (2020) - [i1]Giovanni De Toni, Cristian Consonni, Alberto Montresor:
A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data. CoRR abs/2010.14903 (2020)
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