| 2025 | AIES | What's Individual About Individual Fairness? | Shai Ben-David, Pascale Gourdeau, Tosca Lechner, Ruth Urner |
| 2025 | ALT | Refining the Sample Complexity of Comparative Learning. | Sajad Ashkezari, Ruth Urner |
| 2025 | COLT | Simplifying Adversarially Robust PAC Learning With Tolerance. | Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 2024 | COLT | On the Computability of Robust PAC Learning. | Pascale Gourdeau, Tosca Lechner, Ruth Urner |
| 2024 | ECAI | An Axiomatic Perspective on Anomaly Detection. | Chester Wyke, Ruth Urner |
| 2023 | AISTATS | Precision Recall Cover: A Method For Assessing Generative Models. | Fasil Cheema, Ruth Urner |
| 2023 | ALT | Adversarially Robust Learning with Tolerance. | Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 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 | 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 |
| 2020 | ICML | Black-box Certification and Learning under Adversarial Perturbations. | Hassan Ashtiani, Vinayak Pathak, Ruth Urner |
| 2019 | COLT | When can unlabeled data improve the learning rate? | Christina Gpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner |
| 2016 | ALT | On Version Space Compression. | Shai Ben-David, Ruth Urner |
| 2015 | COLT | Efficient Learning of Linear Separators under Bounded Noise. | Pranjal Awasthi, Maria-Florina Balcan, Nika Haghtalab, Ruth Urner |
| 2015 | COLT | Hierarchical Label Queries with Data-Dependent Partitions. | Samory Kpotufe, Ruth Urner, Shai Ben-David |
| 2015 | ICML | Active Nearest Neighbors in Changing Environments. | Christopher Berlind, Ruth Urner |
| 2014 | COLT | The sample complexity of agnostic learning under deterministic labels. | Shai Ben-David, Ruth Urner |
| 2014 | ISAIM | The sample complexity of agnostic learning with deterministic labels. | Shai Ben-David, Ruth Urner |
| 2013 | COLT | PLAL: Cluster-based active learning. | Ruth Urner, Sharon Wulff, Shai Ben-David |
| 2013 | ICML | Monochromatic Bi-Clustering. | Sharon Wulff, Ruth Urner, Shai Ben-David |
| 2013 | UAI | Generative Multiple-Instance Learning Models For Quantitative Electromyography. | Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte |
| 2012 | ALT | On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples. | Shai Ben-David, Ruth Urner |
| 2012 | ISAIM | Domain Adaptation--Can Quantity compensate for Quality?. | Shai Ben-David, Shai Shalev-Shwartz, Ruth Urner |
| 2011 | ICML | Access to Unlabeled Data can Speed up Prediction Time. | Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David |