Learning Structured Models with the AUC Loss and Its Generalizations. - Nir Rosenfeld; Ofer Meshi; Daniel Tarlow; Amir Globerson (AISTATS, 2014)

Optimal Tagging with Markov Chain Optimization. - Nir Rosenfeld; Amir Globerson (NIPS, 2016)

Discriminative Learning of Infection Models. - Nir Rosenfeld; Mor Nitzan; Amir Globerson (WSDM, 2016)

Predicting Counterfactuals from Large Historical Data and Small Randomized Trials. - Nir Rosenfeld; Yishay Mansour; Elad Yom-Tov (WWW (Companion Volume), 2017)

Semi-Supervised Learning with Competitive Infection Models. - Nir Rosenfeld; Amir Globerson (AISTATS, 2018)

Discriminative Learning of Prediction Intervals. - Nir Rosenfeld; Yishay Mansour; Elad Yom-Tov (AISTATS, 2018)

Learning to Optimize Combinatorial Functions. - Nir Rosenfeld; Eric Balkanski; Amir Globerson; Yaron Singer (ICML, 2018)

Machine Learning : Of, By, and For Social Dynamics (כותר נוסף בעברית: מערכות למודות : של, על, ועבור דינמיקה חברתית). - Nir Rosenfeld (2018)

Predicting Choice with Set-Dependent Aggregation. - Nir Rosenfeld; Kojin Oshiba; Yaron Singer (ICML, 2020)

From Predictions to Decisions: Using Lookahead Regularization. - Nir Rosenfeld; Sophie Hilgard; Sai Srivatsa Ravindranath; David C. Parkes (NeurIPS, 2020)

A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone. - Nir Rosenfeld; Aron Szanto; David C. Parkes (WWW, 2020)

Strategic Classification in the Dark. - Ganesh Ghalme; Vineet Nair; Itay Eilat; Inbal Talgam-Cohen; Nir Rosenfeld (ICML, 2021)

Learning Representations by Humans, for Humans. - Sophie Hilgard; Nir Rosenfeld; Mahzarin R. Banaji; Jack Cao; David C. Parkes (ICML, 2021)

Strategic Classification Made Practical. - Sagi Levanon; Nir Rosenfeld (ICML, 2021)

Generalized Strategic Classification and the Case of Aligned Incentives. - Sagi Levanon; Nir Rosenfeld (ICML, 2022)

Strategic Representation. - Vineet Nair; Ganesh Ghalme; Inbal Talgam-Cohen; Nir Rosenfeld (ICML, 2022)

In the Eye of the Beholder: Robust Prediction with Causal User Modeling. - Amir Feder; Guy Horowitz; Yoav Wald; Roi Reichart; Nir Rosenfeld (NeurIPS, 2022)

Learning to Take a Break: Sustainable Optimization of Long-Term User Engagement. - Eden Saig; Nir Rosenfeld (CoRR, 2022)

Strategic Classification with Graph Neural Networks. - Itay Eilat; Ben Finkelshtein; Chaim Baskin; Nir Rosenfeld (ICLR, 2023)

Performative Recommendation: Diversifying Content via Strategic Incentives. - Itay Eilat; Nir Rosenfeld (ICML, 2023)

Causal Strategic Classification: A Tale of Two Shifts. - Guy Horowitz; Nir Rosenfeld (ICML, 2023)

Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement. - Eden Saig; Nir Rosenfeld (ICML, 2023)

Delegated Classification. - Eden Saig; Inbal Talgam-Cohen; Nir Rosenfeld (NeurIPS, 2023)

Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias. - Itay Itzhak; Gabriel Stanovsky; Nir Rosenfeld; Yonatan Belinkov (CoRR, 2023)

Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces. - Omer Nahum; Gali Noti; David C. Parkes; Nir Rosenfeld (ICLR, 2024)

Classification Under Strategic Self-Selection. - Guy Horowitz; Yonatan Sommer; Moran Koren; Nir Rosenfeld (ICML, 2024)

One-Shot Strategic Classification Under Unknown Costs. - Elan Rosenfeld; Nir Rosenfeld (ICML, 2024)

Strategic ML: How to Learn With Data That 'Behaves'. - Nir Rosenfeld (WSDM, 2024)

Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness. - Maayan Ehrenberg; Roy Ganz; Nir Rosenfeld (ICLR, 2025)

Machine Learning Should Maximize Welfare, Not (Only) Accuracy. - Nir Rosenfeld; Haifeng Xu (CoRR, 2025)

A Market for Accuracy: Classification under Competition. - Ohad Einav; Nir Rosenfeld (CoRR, 2025)

Learning Classifiers That Induce Markets. - Yonatan Sommer; Ivri Hikri; Lotan Amit; Nir Rosenfeld (CoRR, 2025)

Evolutionary Prediction Games. - Eden Saig; Nir Rosenfeld (CoRR, 2025)

Strategic Classification with Non-Linear Classifiers. - Benyamin Trachtenberg; Nir Rosenfeld (CoRR, 2025)