Pedram Daee
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View article: EntityBot: Actionable Entity Recommendations for Everyday Digital Task
EntityBot: Actionable Entity Recommendations for Everyday Digital Task Open
Our everyday digital tasks require access to information from a wide range of applications and systems. Although traditional search systems can help find information, they usually operate within one application (e.g., email client or web b…
View article: EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations
EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations Open
Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, …
View article: Entity Recommendation for Everyday Digital Tasks
Entity Recommendation for Everyday Digital Tasks Open
Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific re…
View article: Interactive AI with a Theory of Mind
Interactive AI with a Theory of Mind Open
Understanding each other is the key to success in collaboration. For humans, attributing mental states to others, the theory of mind, provides the crucial advantage. We argue for formulating human--AI interaction as a multi-agent problem, …
View article: Integrating neurophysiologic relevance feedback in intent modeling for information retrieval
Integrating neurophysiologic relevance feedback in intent modeling for information retrieval Open
The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signa…
View article: Machine Teaching of Active Sequential Learners
Machine Teaching of Active Sequential Learners Open
Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data di…
View article: Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge
Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge Open
Motivation Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the …
View article: Probabilistic Expert Knowledge Elicitation of Feature Relevances in Sparse Linear Regression
Probabilistic Expert Knowledge Elicitation of Feature Relevances in Sparse Linear Regression Open
Peer reviewed