71 citations · 174 across the 21 of their papers we have counts for
23 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…
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…
Personalized Top-k Set Queries Over Predicted Scores
Sohrab Namazi Nia, Subhodeep Ghosh, Senjuti Basu Roy +1
This work studies the applicability of expensive external oracles such as large language models in answering top-k queries over predicted scores. Such scores are incurred by user-d…
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,…
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…
From Large Language Models to Databases and Back: A discussion on research and education
Sihem Amer-Yahia, Angela Bonifati, Lei Chen +4
This discussion was conducted at a recent panel at the 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023), held April 17-20, 2023 in Tianjin,…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
On Efficient Approximate Queries over Machine Learning Models
Dujian Ding, Sihem Amer-Yahia, Laks VS Lakshmanan
The question of answering queries over ML predictions has been gaining attention in the database community. This question is challenging because the cost of finding high quality an…
Guided Exploration of Data Summaries
Brit Youngmann, Sihem Amer-Yahia, Aurélien Personnaz
Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose o…
INODE: Building an End-to-End Data Exploration System in Practice [Extended Vision]
Sihem Amer-Yahia, Georgia Koutrika, Frederic Bastian +25
A full-fledged data exploration system must combine different access modalities with a powerful concept of guiding the user in the exploration process, by being reactive and antici…
DropLeaf: a precision farming smartphone application for measuring pesticide spraying methods
Bruno Brandoli, Gabriel Spadon, Travis Esau +4
Pesticide application has been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades. However, their appropriate use…
Recommending Deployment Strategies for Collaborative Tasks
Dong Wei, Senjuti Basu Roy, Sihem Amer-Yahia
Our work contributes to aiding requesters in deploying collaborative tasks in crowdsourcing. We initiate the study of recommending deployment strategies for collaborative tasks to…
Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls
Jose F Rodrigues-Jr, Gabriel Spadon, Bruno Brandoli +1
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to…
Eliciting Worker Preference for Task Completion
Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia
Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completin…
Exploration of User Groups in VEXUS
Sihem Amer-Yahia, Behrooz Omidvar-Tehrani, Joao Comba +2
We introduce VEXUS, an interactive visualization framework for exploring user data to fulfill tasks such as finding a set of experts, forming discussion groups and analyzing collec…
Querying Temporal Drifts at Multiple Granularities (Technical Report)
Sofia Kleisarchaki, Sihem Amer-Yahia, Ahlame Douzal-Chouakria +1
There exists a large body of work on online drift detection with the goal of dynamically finding and maintaining changes in data streams. In this paper, we adopt a query-based appr…
Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns
Martin Kirchgessner, Vincent Leroy, Sihem Amer-Yahia +1
Understanding customer buying patterns is of great interest to the retail industry and has shown to benefit a wide variety of goals ranging from managing stocks to implementing loy…
"The Whole Is Greater Than the Sum of Its Parts": Optimization in Collaborative Crowdsourcing
Habibur Rahman, Senjuti Basu Roy, Saravanan Thirumuruganathan +2
In this work, we initiate the investigation of optimization opportunities in collaborative crowdsourcing. Many popular applications, such as collaborative document editing, sentenc…
Optimization in Knowledge-Intensive Crowdsourcing
Senjuti Basu Roy, Ioanna Lykourentzou, Saravanan Thirumuruganathan +2
We present SmartCrowd, a framework for optimizing collaborative knowledge-intensive crowdsourcing. SmartCrowd distinguishes itself by accounting for human factors in the process of…
Who Tags What? An Analysis Framework
Mahashweta Das, Saravanan Thirumuruganathan, Sihem Amer-Yahia +2
The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three compone…
SocialScope: Enabling Information Discovery on Social Content Sites
Sihem Amer-Yahia, Laks Lakshmanan, Cong Yu
Recently, many content sites have started encouraging their users to engage in social activities such as adding buddies on Yahoo! Travel and sharing articles with their friends on…