Search Results for author: Mihaela Curmei

Found 7 papers, 3 papers with code

Initializing Services in Interactive ML Systems for Diverse Users

no code implementations19 Dec 2023 Avinandan Bose, Mihaela Curmei, Daniel L. Jiang, Jamie Morgenstern, Sarah Dean, Lillian J. Ratliff, Maryam Fazel

(ii) Suboptimal Local Solutions: The total loss (sum of loss functions across all users and all services) landscape is not convex even if the individual losses on a single service are convex, making it likely for the learning dynamics to get stuck in local minima.

Private Matrix Factorization with Public Item Features

no code implementations17 Sep 2023 Mihaela Curmei, Walid Krichene, Li Zhang, Mukund Sundararajan

It can be applied to different types of public item data, including: (1) categorical item features; (2) item-item similarities learned from public sources; and (3) publicly available user feedback.

Collaborative Filtering

Delayed and Indirect Impacts of Link Recommendations

no code implementations17 Mar 2023 Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei

Moreover, we show that the effects of recommendations can persist in networks, in part due to their indirect impacts on natural dynamics even after recommendations are turned off.

counterfactual

Towards Psychologically-Grounded Dynamic Preference Models

no code implementations1 Aug 2022 Mihaela Curmei, Andreas Haupt, Dylan Hadfield-Menell, Benjamin Recht

Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design.

Recommendation Systems

Emergent segmentation from participation dynamics and multi-learner retraining

1 code implementation6 Jun 2022 Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel

We study the participation and retraining dynamics that arise when both the learners and sub-populations of users are \emph{risk-reducing}, which cover a broad class of updates including gradient descent, multiplicative weights, etc.

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

1 code implementation30 Jun 2021 Mihaela Curmei, Sarah Dean, Benjamin Recht

In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery.

Recommendation Systems

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