Publications (8)
Scalable Deep Generative Modeling for Sparse Graphs
Hanjun Dai, Azade Nazi, Yujia Li +2
Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However curren…
Chip Placement with Deep Reinforcement Learning
Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan +19
In this work, we present a learning-based approach to chip placement, one of the most complex and time-consuming stages of the chip design process. Unlike prior methods, our approa…
GAP: Generalizable Approximate Graph Partitioning Framework
Azade Nazi, Will Hang, Anna Goldie +2
Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions. Due to its combinatorial nature, ma…
Assisting Service Providers In Peer-to-peer Marketplaces: Maximizing Gain Over Flexible Attributes
Abolfazl Asudeh, Azade Nazi, Nick Koudas +1
Peer to peer marketplaces such as AirBnB enable transactional exchange of services directly between people. In such platforms, those providing a service (hosts in AirBnB) are faced…
Web Item Reviewing Made Easy By Leveraging Available User Feedback
Azade Nazi, Mahashweta Das, Gautam Das
The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Thou…
RRR: Rank-Regret Representative
Abolfazl Asudeh, Azade Nazi, Nan Zhang +2
Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different…
Walk, Not Wait: Faster Sampling Over Online Social Networks
Azade Nazi, Zhuojie Zhou, Saravanan Thirumuruganathan +2
In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walk which wait…
Generalized Clustering by Learning to Optimize Expected Normalized Cuts
Azade Nazi, Will Hang, Anna Goldie +2
We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples. Our clustering objective is based on optimizing normalized cuts, a criterion wh…