14 citations · 32 across the 10 of their papers we have counts for
10 papers
Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking
Haruka Kiyohara, Mihaela Curmei, Ariel Evnine +7
Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker (ESR) generates a candidate se…
Initializing Services in Interactive ML Systems for Diverse Users
Avinandan Bose, Mihaela Curmei, Daniel L. Jiang +4
This paper investigates ML systems serving a group of users, with multiple models/services, each aimed at specializing to a sub-group of users. We consider settings where upon depl…
Private Matrix Factorization with Public Item Features
Mihaela Curmei, Walid Krichene, Li Zhang +1
We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at…
Delayed and Indirect Impacts of Link Recommendations
Han Zhang, Shangen Lu, Yixin Wang +1
The impacts of link recommendations on social networks are challenging to evaluate, and so far they have been studied in limited settings. Observational studies are restricted in t…
Towards Psychologically-Grounded Dynamic Preference Models
Mihaela Curmei, Andreas Haupt, Dylan Hadfield-Menell +1
Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior an…
Emergent specialization from participation dynamics and multi-learner retraining
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff +2
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in t…
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
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. We propose an ev…
Do Offline Metrics Predict Online Performance in Recommender Systems?
Karl Krauth, Sarah Dean, Alex Zhao +4
Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences ch…
Finding Equilibrium in Multi-Agent Games with Payoff Uncertainty
Wenshuo Guo, Mihaela Curmei, Serena Wang +2
We study the problem of finding equilibrium strategies in multi-agent games with incomplete payoff information, where the payoff matrices are only known to the players up to some b…
Shape-Constrained Regression using Sum of Squares Polynomials
Mihaela Curmei, Georgina Hall
We present a hierarchy of semidefinite programs (SDPs) for the problem of fitting a shape-constrained (multivariate) polynomial to noisy evaluations of an unknown shape-constrained…