7 papers
Lever: Inference-Time Policy Reuse under Support Constraints
Ihor Vitenko, Noha Ibrahim, Sihem Amer-Yahia
Reinforcement learning (RL) policies are typically trained for fixed objectives, making reuse difficult when task requirements change. We study inference-time policy reuse: given a…
Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition
Ricardo Pedro Querido Andrade Silva, Nassim Bouarour, Dina Fettache +3
Quiz design is a tedious process that teachers undertake to evaluate the acquisition of knowledge by students. Our goal in this paper is to automate quiz composition from a set of…
On Efficient Approximate Aggregate Nearest Neighbor Queries over Learned Representations
Carrie Wang, Sihem Amer-Yahia, Laks V. S. Lakshmanan +1
We study Aggregation Queries over Nearest Neighbors (AQNN), which compute aggregates over the learned representations of the neighborhood of a designated query object. For example,…
Producer-Fairness in Sequential Bundle Recommendation
Alexandre Rio, Marta Soare, Sihem Amer-Yahia
We address fairness in the context of sequential bundle recommendation, where users are served in turn with sets of relevant and compatible items. Motivated by real-world scenarios…
The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi +43
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challe…
A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs
Yun Wang, Chrysanthi Kosyfaki, Sihem Amer-Yahia +1
Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representation…