| 2026 | AAAI | A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. | Trenton Chang, Tobias Schnabel, Adith Swaminathan, Jenna Wiens |
| 2024 | EMNLP | Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization. | Tobias Schnabel, Jennifer Neville |
| 2024 | UAI | On Overcoming Miscalibrated Conversational Priors in LLM-based ChatBots. | Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan |
| 2023 | WWW | Foreword for Workshop on Decision Making for Information Retrieval and Recommender Systems. | Da Xu, Tobias Schnabel, Xiquan Cui, Sarah Dean, Aniket Deshmukh, Bo Yang, Shipeng Yu |
| 2023 | SIGIR | When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback? | Yushun Dong, Jundong Li, Tobias Schnabel |
| 2022 | CIKM | EvalRS: a rounded evaluation of recommender systems. | Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia |
| 2022 | IUI | HINT: Integration Testing for AI-based features with Humans in the Loop. | Quan Ze Chen, Tobias Schnabel, Besmira Nushi, Saleema Amershi |
| 2021 | ACL | Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text. | Philippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. Hearst |
| 2021 | RecSys | Local Factor Models for Large-Scale Inductive Recommendation. | Longqi Yang, Tobias Schnabel, Paul N. Bennett, Susan T. Dumais |
| 2020 | RecSys | Debiasing Item-to-Item Recommendations With Small Annotated Datasets. | Tobias Schnabel, Paul N. Bennett |
| 2020 | RecSys | "Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for Recommendation. | Tobias Schnabel, Gonzalo A. Ramos, Saleema Amershi |
| 2020 | SIGIR | The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. | Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims |
| 2019 | WSDM | Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. | Tobias Schnabel, Paul N. Bennett, Thorsten Joachims |
| 2018 | IJCAI | Unbiased Learning-to-Rank with Biased Feedback. | Thorsten Joachims, Adith Swaminathan, Tobias Schnabel |
| 2018 | WSDM | Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. | Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims |
| 2017 | KDD | Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. | Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims |
| 2017 | WSDM | Unbiased Learning-to-Rank with Biased Feedback. | Thorsten Joachims, Adith Swaminathan, Tobias Schnabel |
| 2016 | ICML | Recommendations as Treatments: Debiasing Learning and Evaluation. | Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims |
| 2016 | ICTIR | Unbiased Comparative Evaluation of Ranking Functions. | Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims |
| 2016 | WWW | Using Shortlists to Support Decision Making and Improve Recommender System Performance. | Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims |
| 2015 | EMNLP | Evaluation methods for unsupervised word embeddings. | Tobias Schnabel, Igor Labutov, David M. Mimno, Thorsten Joachims |
| 2015 | EMNLP | Online Updating of Word Representations for Part-of-Speech Tagging. | Wenpeng Yin, Tobias Schnabel, Hinrich Schtze |
| 2015 | WWW | Unbiased Ranking Evaluation on a Budget. | Tobias Schnabel, Adith Swaminathan, Thorsten Joachims |
| 2013 | ICML | Stable Coactive Learning via Perturbation. | Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel |
| 2013 | IJCNLP | Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging. | Tobias Schnabel, Hinrich Schtze |