no code implementations • 13 Sep 2016 • Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song
DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time.
no code implementations • NeurIPS 2016 • Yichen Wang, Nan Du, Rakshit Trivedi, Le Song
Matching users to the right items at the right time is a fundamental task in recommendation systems.
no code implementations • ICML 2017 • Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.
2 code implementations • ICML 2017 • Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song
The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.
no code implementations • ICLR 2018 • Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
no code implementations • 11 Mar 2018 • Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
How can we effectively encode evolving information over dynamic graphs into low-dimensional representations?
no code implementations • ACL 2018 • Rakshit Trivedi, Bunyamin Sisman, Jun Ma, Christos Faloutsos, Hongyuan Zha, Xin Luna Dong
Knowledge graphs have emerged as an important model for studying complex multi-relational data.
2 code implementations • ICLR 2019 • Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes).
no code implementations • ICML 2020 • Rakshit Trivedi, Jiachen Yang, Hongyuan Zha
Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging.
no code implementations • NeurIPS 2020 • Rakshit Trivedi, Hongyuan Zha
Real-world networks, especially the ones that emerge due to actions of animate agents (e. g. humans, animals), are the result of underlying strategic mechanisms aimed at maximizing individual or collective benefits.
no code implementations • ICLR 2022 • Matthias Gerstgrasser, Rakshit Trivedi, David C. Parkes
Human demonstrations of video game play can serve as vital surrogate representations of real-world behaviors, access to which would facilitate rapid progress in several complex learning settings (e. g. behavior classification, imitation learning, offline RL etc.).