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Poster
Preference Completion from Partial Rankings
Suriya Gunasekar · Sanmi Koyejo · Joydeep Ghosh

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #32
in Wednesday Posters »
We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over a shared set of items, based on a limited number of observed affinity values. Our approach exploits the observation that while preferences are often recorded as numerical scores, the predictive quantity of interest is the underlying rankings. Thus, attempts to closely match the recorded scores may lead to overfitting and impair generalization performance. Instead, we propose an estimator that directly fits the underlying preference order, combined with nuclear norm constraints to encourage low--rank parameters. Besides (approximate) correctness of the ranking order, the proposed estimator makes no generative assumption on the numerical scores of the observations. One consequence is that the proposed estimator can fit any consistent partial ranking over a subset of the items represented as a directed acyclic graph (DAG), generalizing standard techniques that can only fit preference scores. Despite this generality, for supervision representing total or blockwise total orders, the computational complexity of our algorithm is within a $\log$ factor of the standard algorithms for nuclear norm regularization based estimates for matrix completion. We further show promising empirical results for a novel and challenging application of collaboratively ranking of the associations between brain--regions and cognitive neuroscience terms.
Keywords: (Other) Optimization · (Application) Collaborative Filtering and Recommender Systems · Ranking and Preference Learning · (Other) Neuroscience · Regularization and Large Margin Methods · Structured Prediction
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Author Information

Suriya Gunasekar (UT Austin)
Sanmi Koyejo (UIUC)
Sanmi Koyejo

Sanmi Koyejo an Assistant Professor in the Department of Computer Science at Stanford University. Koyejo also spends time at Google as a part of the Brain team. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning. Additionally, Koyejo focuses on applications to neuroscience and healthcare. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence (UAI), a Skip Ellis Early Career Award, and a Sloan Fellowship. Koyejo serves as the president of the Black in AI organization.

Joydeep Ghosh (UT Austin)

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