Tobias Schnabel

According to our database, Tobias Schnabel authored at least 36 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Author Correction: A challenge for rounded evaluation of recommender systems.

Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia

Nat. Mac. Intell., 2024

On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots.

Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan

CoRR, 2024

Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization.

Tobias Schnabel, Jennifer Neville

CoRR, 2024

Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization.

Tobias Schnabel, Jennifer Neville

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2023

A challenge for rounded evaluation of recommender systems.

Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia

Nat. Mac. Intell., February, 2023

Foreword for Workshop on Decision Making for Information Retrieval and Recommender Systems.

Da Xu, Tobias Schnabel, Xiquan Cui, Sarah Dean, Aniket Anand Deshmukh, Bo Yang, Shipeng Yu

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?

Yushun Dong, Jundong Li, Tobias Schnabel

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

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

Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates.

Tobias Schnabel, Mengting Wan, Longqi Yang

CoRR, 2022

Where Do We Go From Here? Guidelines For Offline Recommender Evaluation.

Tobias Schnabel

CoRR, 2022

Lightweight Compositional Embeddings for Incremental Streaming Recommendation.

Mengyue Hang, Tobias Schnabel, Longqi Yang, Jennifer Neville

CoRR, 2022

HINT: Integration Testing for AI-based features with Humans in the Loop.

Quan Ze Chen, Tobias Schnabel, Besmira Nushi, Saleema Amershi

Proceedings of the IUI 2022: 27th International Conference on Intelligent User Interfaces, Helsinki, Finland, March 22, 2022

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

Proceedings of the CIKM 2022 Workshops co-located with 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), 2022

2021

Local Factor Models for Large-Scale Inductive Recommendation.

Longqi Yang, Tobias Schnabel, Paul N. Bennett, Susan T. Dumais

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text.

Philippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. Hearst

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation.

Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

"Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for Recommendation.

Tobias Schnabel, Gonzalo A. Ramos, Saleema Amershi

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Debiasing Item-to-Item Recommendations With Small Annotated Datasets.

Tobias Schnabel, Paul N. Bennett

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

2019

Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory.

Tobias Schnabel, Paul N. Bennett, Thorsten Joachims

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Deep Generalized Method of Moments for Instrumental Variable Analysis.

Andrew Bennett, Nathan Kallus, Tobias Schnabel

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Improving Machine Learning Beyond the Algorithm.

Tobias Schnabel

PhD thesis, 2018

Improving Recommender Systems Beyond the Algorithm.

Tobias Schnabel, Paul N. Bennett, Thorsten Joachims

CoRR, 2018

Short-Term Satisfaction and Long-Term Coverage: Understanding How Users Tolerate Algorithmic Exploration.

Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018

Unbiased Learning-to-Rank with Biased Feedback.

Thorsten Joachims, Adith Swaminathan, Tobias Schnabel

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

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

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers.

Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

2016

Using Shortlists to Support Decision Making and Improve Recommender System Performance.

Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, Thorsten Joachims

Proceedings of the 25th International Conference on World Wide Web, 2016

Unbiased Comparative Evaluation of Ranking Functions.

Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims

Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval, 2016

Recommendations as Treatments: Debiasing Learning and Evaluation.

Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Towards a Better Understanding of Predict and Count Models.

S. Sathiya Keerthi, Tobias Schnabel, Rajiv Khanna

CoRR, 2015

Unbiased Ranking Evaluation on a Budget.

Tobias Schnabel, Adith Swaminathan, Thorsten Joachims

Proceedings of the 24th International Conference on World Wide Web Companion, 2015

Online Updating of Word Representations for Part-of-Speech Tagging.

Wenpeng Yin, Tobias Schnabel, Hinrich Schütze

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015

Evaluation methods for unsupervised word embeddings.

Tobias Schnabel, Igor Labutov, David M. Mimno, Thorsten Joachims

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015

2014

FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging.

Tobias Schnabel, Hinrich Schütze

Trans. Assoc. Comput. Linguistics, 2014

2013

Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging.

Tobias Schnabel, Hinrich Schütze

Proceedings of the Sixth International Joint Conference on Natural Language Processing, 2013

Stable Coactive Learning via Perturbation.

Karthik Raman, Thorsten Joachims, Pannaga Shivaswamy, Tobias Schnabel

Proceedings of the 30th International Conference on Machine Learning, 2013