Learning Graphical Models
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang, Guosheng Yin, Francesca Dominici
Neural Information Processing Systems http://nips.cc/
GroundedAnswersforMulti-agentDecision-making ProblemthroughGenerativeWorldModel
Theempirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark.
Scalable Deep Generative Relational Model with High-Order Node Dependence
Xuhui Fan, Bin Li, Caoyuan Li, Scott SIsson, Ling Chen
Neural Information Processing Systems http://nips.cc/
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
Chaitanya Ryali, Gautam Reddy, Angela J. Yu
Neural Information Processing Systems http://nips.cc/
Learning and Testing Causal Models with Interventions
Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy
Neural Information Processing Systems http://nips.cc/
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos
Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski
Neural Information Processing Systems http://nips.cc/
Reconciling λ-Returns with Experience Replay
Brett Daley, Christopher Amato
A unique benefit to this approach is that each transition's TD error can be
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Neural Information Processing Systems http://nips.cc/