3 citations · 7 across the 5 of their papers we have counts for
9 papers
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
Tobias Schnabel, Mengting Wan, Longqi Yang
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving…
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation
Tobias Schnabel
Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. H…
Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Mengyue Hang, Tobias Schnabel, Longqi Yang +1
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i.e., users and items) is available upfront at training time.…
Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban, Tobias Schnabel, Paul Bennett +1
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplici…
Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett, Nathan Kallus, Tobias Schnabel
Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard metho…
Improving Recommender Systems Beyond the Algorithm
Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that t…