| 2026 | COLT | On the Importance of Randomization in Discriminative Feature Feedback. | Valentio Iverson, Tosca Lechner, Sivan Sabato |
| 2025 | AIES | What's Individual About Individual Fairness? | Shai Ben-David, Pascale Gourdeau, Tosca Lechner, Ruth Urner |
| 2025 | ICML | On the Learnability of Distribution Classes with Adaptive Adversaries. | Tosca Lechner, Alex Bie, Gautam Kamath |
| 2024 | COLT | On the Computability of Robust PAC Learning. | Pascale Gourdeau, Tosca Lechner, Ruth Urner |
| 2024 | COLT | Inherent limitations of dimensions for characterizing learnability of distribution classes. | Tosca Lechner, Shai Ben-David |
| 2023 | ICML | Strategic Classification with Unknown User Manipulations. | Tosca Lechner, Ruth Urner, Shai Ben-David |
| 2022 | AAAI | Learning Losses for Strategic Classification. | Tosca Lechner, Ruth Urner |
| 2021 | AAAI | Classification Confidence Scores with Point-wise Guarantees. | Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner |
| 2021 | COLT | Open Problem: Are all VC-classes CPAC learnable? | Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |
| 2021 | UAI | Identifying regions of trusted predictions. | Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |
| 2020 | ALT | On Learnability wih Computable Learners. | Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |