Search Results for author: Rakshit Trivedi

Found 12 papers, 2 papers with code

CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games

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.).

Atari Games Decision Making +3

Learning Strategic Network Emergence Games

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.

GraphOpt: Learning Optimization Models of Graph Formation

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.

Decision Making Link Prediction

DyRep: Learning Representations over Dynamic Graphs

1 code implementation 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).

Dynamic Link Prediction Representation Learning

Representation Learning over Dynamic Graphs

no code implementations11 Mar 2018 Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha

How can we effectively encode evolving information over dynamic graphs into low-dimensional representations?

Dynamic Link Prediction Representation Learning

Learning Deep Mean Field Games for Modeling Large Population Behavior

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.

reinforcement-learning

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

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.

Entity Embeddings Knowledge Graphs +1

Fake News Mitigation via Point Process Based Intervention

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.

reinforcement-learning

Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

no code implementations13 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.

Activity Prediction Network Embedding +2

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