Stable Coactive Learning via Perturbation. - Karthik Raman 001; Thorsten Joachims; Pannaga Shivaswamy; Tobias Schnabel (ICML (3), 2013)

Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging. - Tobias Schnabel; Hinrich Schütze (IJCNLP, 2013)

FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging. - Tobias Schnabel; Hinrich Schütze (Trans. Assoc. Comput. Linguistics, 2014)

Evaluation methods for unsupervised word embeddings. - Tobias Schnabel; Igor Labutov; David M. Mimno; Thorsten Joachims (EMNLP, 2015)

Online Updating of Word Representations for Part-of-Speech Tagging. - Wenpeng Yin 001; Tobias Schnabel; Hinrich Schütze (EMNLP, 2015)

Unbiased Ranking Evaluation on a Budget. - Tobias Schnabel; Adith Swaminathan; Thorsten Joachims (WWW (Companion Volume), 2015)

Towards a Better Understanding of Predict and Count Models. - S. Sathiya Keerthi; Tobias Schnabel; Rajiv Khanna (CoRR, 2015)

Recommendations as Treatments: Debiasing Learning and Evaluation. - Tobias Schnabel; Adith Swaminathan; Ashudeep Singh; Navin Chandak; Thorsten Joachims (ICML, 2016)

Unbiased Comparative Evaluation of Ranking Functions. - Tobias Schnabel; Adith Swaminathan; Peter I. Frazier; Thorsten Joachims (ICTIR, 2016)

Using Shortlists to Support Decision Making and Improve Recommender System Performance. - Tobias Schnabel; Paul N. Bennett; Susan T. Dumais; Thorsten Joachims (WWW, 2016)

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers. - Aman Agarwal; Soumya Basu 003; Tobias Schnabel; Thorsten Joachims (KDD, 2017)

A preference elicitation interface for collecting dense recommender datasets with rich user information. - Pantelis P. Analytis; Tobias Schnabel; Stefan M. Herzog; Daniel Barkoczi; Thorsten Joachims (CoRR, 2017)

Unbiased Learning-to-Rank with Biased Feedback. - Thorsten Joachims; Adith Swaminathan; Tobias Schnabel (IJCAI, 2018)

Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration. - Tobias Schnabel; Paul N. Bennett; Susan T. Dumais; Thorsten Joachims (WSDM, 2018)

Improving Recommender Systems Beyond the Algorithm. - Tobias Schnabel; Paul N. Bennett; Thorsten Joachims (CoRR, 2018)

Improving Machine Learning Beyond the Algorithm. - Tobias Schnabel (2018)

Deep Generalized Method of Moments for Instrumental Variable Analysis. - Andrew Bennett; Nathan Kallus; Tobias Schnabel (NeurIPS, 2019)

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory. - Tobias Schnabel; Paul N. Bennett; Thorsten Joachims (WSDM, 2019)

Debiasing Item-to-Item Recommendations With Small Annotated Datasets. - Tobias Schnabel; Paul N. Bennett (RecSys, 2020)

"Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for Recommendation. - Tobias Schnabel; Gonzalo A. Ramos; Saleema Amershi (RecSys, 2020)

The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. - Tobias Schnabel; Saleema Amershi; Paul N. Bennett; Peter Bailey; Thorsten Joachims (SIGIR, 2020)

Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text. - Philippe Laban; Tobias Schnabel; Paul N. Bennett; Marti A. Hearst (ACL/IJCNLP (1), 2021)

Local Factor Models for Large-Scale Inductive Recommendation. - Longqi Yang; Tobias Schnabel; Paul N. Bennett; Susan T. Dumais (RecSys, 2021)

EvalRS: a rounded evaluation of recommender systems. - Jacopo Tagliabue; Federico Bianchi 001; Tobias Schnabel; Giuseppe Attanasio; Ciro Greco; Gabriel de Souza P. Moreira; Patrick John Chia (CIKM Workshops, 2022)

HINT: Integration Testing for AI-based features with Humans in the Loop. - Quan Ze Chen; Tobias Schnabel; Besmira Nushi; Saleema Amershi (IUI, 2022)

Lightweight Compositional Embeddings for Incremental Streaming Recommendation. - Mengyue Hang; Tobias Schnabel; Longqi Yang; Jennifer Neville (CoRR, 2022)

Where Do We Go From Here? Guidelines For Offline Recommender Evaluation. - Tobias Schnabel (CoRR, 2022)

Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates. - Tobias Schnabel; Mengting Wan; Longqi Yang (CoRR, 2022)

SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization. - Philippe Laban; Tobias Schnabel; Paul N. Bennett; Marti A. Hearst (Trans. Assoc. Comput. Linguistics, 2022)

When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback? - Yushun Dong; Jundong Li; Tobias Schnabel (SIGIR, 2023)

Foreword for Workshop on Decision Making for Information Retrieval and Recommender Systems. - Da Xu; Tobias Schnabel; Xiquan Cui; Sarah Dean; Aniket Deshmukh 001; Bo Yang; Shipeng Yu (WWW (Companion Volume), 2023)

A challenge for rounded evaluation of recommender systems. - Jacopo Tagliabue; Federico Bianchi 001; Tobias Schnabel; Giuseppe Attanasio; Ciro Greco; Gabriel de Souza P. Moreira; Patrick John Chia (Nat. Mac. Intell., 2023)

Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization. - Tobias Schnabel; Jennifer Neville (EMNLP (Findings), 2024)

On Overcoming Miscalibrated Conversational Priors in LLM-based ChatBots. - Christine Herlihy; Jennifer Neville; Tobias Schnabel; Adith Swaminathan (UAI, 2024)

Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization. - Tobias Schnabel; Jennifer Neville (CoRR, 2024)

Author Correction: A challenge for rounded evaluation of recommender systems. - Jacopo Tagliabue; Federico Bianchi 001; Tobias Schnabel; Giuseppe Attanasio; Ciro Greco; Gabriel de Souza P. Moreira; Patrick John Chia (Nat. Mac. Intell., 2024)

Introduction to the Special Issue on Causal Inference for Recommender Systems. - Yongfeng Zhang; Xu Chen 017; Da Xu; Tobias Schnabel (Trans. Recomm. Syst., 2024)

Lost in Transmission: When and Why LLMs Fail to Reason Globally. - Tobias Schnabel; Kiran Tomlinson; Adith Swaminathan; Jennifer Neville (CoRR, 2025)

A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMs. - Trenton Chang; Tobias Schnabel; Adith Swaminathan; Jenna Wiens (CoRR, 2025)