On Learnability wih Computable Learners. - Sushant Agarwal; Nivasini Ananthakrishnan; Shai Ben-David; Tosca Lechner; Ruth Urner (ALT, 2020)

Classification Confidence Scores with Point-wise Guarantees. - Nivasini Ananthakrishnan; Shai Ben-David; Tosca Lechner (SafeAI@AAAI, 2021)

Open Problem: Are all VC-classes CPAC learnable? - Sushant Agarwal; Nivasini Ananthakrishnan; Shai Ben-David; Tosca Lechner; Ruth Urner (COLT, 2021)

Identifying regions of trusted predictions. - Nivasini Ananthakrishnan; Shai Ben-David; Tosca Lechner; Ruth Urner (UAI, 2021)

Impossibility results for fair representations. - Tosca Lechner; Shai Ben-David; Sushant Agarwal; Nivasini Ananthakrishnan (CoRR, 2021)

Learning Losses for Strategic Classification. - Tosca Lechner; Ruth Urner (AAAI, 2022)

Inherent Limitations of Multi-Task Fair Representations. - Tosca Lechner; Shai Ben-David (CoLLAs, 2022)

Strategic Classification with Unknown User Manipulations. - Tosca Lechner; Ruth Urner; Shai Ben-David (ICML, 2023)

Distribution Learnability and Robustness. - Shai Ben-David; Alex Bie; Gautam Kamath 001; Tosca Lechner (NeurIPS, 2023)

Adversarially Robust Learning with Uncertain Perturbation Sets. - Tosca Lechner; Vinayak Pathak; Ruth Urner (NeurIPS, 2023)

Impossibility of Characterizing Distribution Learning - a simple solution to a long-standing problem. - Tosca Lechner; Shai Ben-David (CoRR, 2023)

On the Computability of Robust PAC Learning. - Pascale Gourdeau; Tosca Lechner; Ruth Urner (COLT, 2024)

Inherent limitations of dimensions for characterizing learnability of distribution classes. - Tosca Lechner; Shai Ben-David (COLT, 2024)

Score Design for Multi-Criteria Incentivization. - Anmol Kabra; Mina Karzand; Tosca Lechner; Nati Srebro; Serena Wang 001 (FORC, 2024)

On the Computability of Multiclass PAC Learning. - Pascale Gourdeau; Tosca Lechner; Ruth Urner (CoRR, 2025)