Link prediction and multi-label learning on graphs are two important but challenging machine learning problems that have broad applications in diverse fields. Not only are the tw...
We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our fram...
Image tweets are becoming a prevalent form of social media, but little is known about their content – textual and visual – and the relationship between the two mediums. Our an...
Tao Chen, Hany M. SalahEldeen, Xiangnan He, Min-Ye...
The modern web critically depends on aggregation of information from self-interested agents, for example opinion polls, product ratings, or crowdsourcing. We consider a setting wh...
Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, producer). This observation motivates us to consider a feat...
Chenyi Zhang, Ke Wang, Ee-Peng Lim, Qinneng Xu, Ji...
We propose two local consistencies that extend bounds consistency (BC) by simultaneously considering combinations of constraints as opposed to single constraints. We prove that th...
Christian Bessiere, Anastasia Paparrizou, Kostas S...
Knowledge compilation is a powerful reasoning paradigm with many applications across AI and computer science more broadly. We consider the problem of bottom-up compilation of know...
Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors. A key feature of layer...
Civil unrest (protests, strikes, and “occupy” events) is a common occurrence in both democracies and authoritarian regimes. The study of civil unrest is a key topic for politi...