113 citations · 373 across the 9 of their papers we have counts for
9 papers
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
Jiachen Yang, Ethan Wang, Rakshit Trivedi +2
Critical sectors of human society are progressing toward the adoption of powerful artificial intelligence (AI) agents, which are trained individually on behalf of self-interested p…
GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi, Jiachen Yang, Hongyuan Zha
Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging. In real-world domains, one often has access only to the f…
LinkNBed: Multi-Graph Representation Learning with Entity Linkage
Rakshit Trivedi, Bunyamin Sisman, Jun Ma +3
Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowl…
Representation Learning over Dynamic Graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal +1
How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learn…
Learning Deep Mean Field Games for Modeling Large Population Behavior
Jiachen Yang, Xiaojing Ye, Rakshit Trivedi +2
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete t…
Tweeting about journal articles: Engagement, marketing or just gibberish?
Nicolas Robinson-Garcia, Rakshit Trivedi, Rodrigo Costas +3
This paper presents preliminary results on the analysis of tweets to journal articles in the field of Dentistry. We present two case studies in which we critically examine the cont…
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang +1
The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. Reasoning over…
Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye +6
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. Th…
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation
Hanjun Dai, Yichen Wang, Rakshit Trivedi +1
Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item…