Events

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

Dynamic sampling (DS) is applied to create a sampled set of relevance judgments in our participation of TREC Common Core Track 2018. One goal was to test the effectiveness and efficiency of this technique with a set of non-expert, secondary relevance assessors.  We consider NIST assessors to be the experts and the primary assessors. Another goal was to make available to other researchers a sampled set of relevance judgments (prels) and thus allow the estimation of retrieval metrics that have the potential to be more robust than the standard NIST provided relevance judgments (qrels). In addition to creating the prels, we also submitted several runs based on our manual judging and the models produced by our HiCAL system. 

Chang Ge, PhD candidate
David R. Cheriton School of Computer Science

Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for differentially private query answering place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quantity they care about, namely accuracy of query answers.

Alexey Karyakin, PhD candidate
David R. Cheriton School of Computer Science

Energy consumed by the main memory in existing database systems does not effectively scale down with lower system utilization, both in terms of actual memory usage and load conditions. At the same time, main memory represents a sizable portion of the total server energy footprint, which makes it an outlier as the rest of the system moves towards energy proportionality. 

We introduce DimmStore, a prototype main-memory database system that addresses the problem of memory energy consumption.

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

Haotian Zhang, PhD candidate
David R. Cheriton School of Computer Science

James She, Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology

Michael Farag, MMath candidate
David R. Cheriton School of Computer Science

Knowledge graphs are considered an important representation that lies between free text on one hand and fully-structured relational data on the other. Knowledge graphs are a backbone of many applications on the Web. With the rise of many large-scale open-domain knowledge graphs like Freebase, DBpedia, and Yago, various applications including document retrieval, question answering, and data integration have been relying on them.

Besat Kassaie, PhD candidate
David R. Cheriton School of Computer Science

Alireza Heidari, PhD candidate
David R. Cheriton School of Computer Science

We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement.

PLEASE NOTE: THIS TALK IS CANCELLED

Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science