Industry
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Federico Monti, Michael Bronstein, Xavier Bresson
Neural Information Processing Systems http://nips.cc/
Affinity Clustering: Hierarchical Clustering at Scale
Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni
The main focus of this paper is on minimum spanning tree (MST) based clusterings.
Countering Feedback Delays in Multi-Agent Learning
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter W. Glynn, Claire Tomlin
Neural Information Processing Systems http://nips.cc/
Unsupervised Learning of Disentangled Representations from Video
Emily L. Denton, vighnesh Birodkar
In this paper, we introduce a form of predictive auto-encoder which uses a novel adversarial loss to factor the latent representation for each video frame into two components, one that is roughly time-independent (i.e.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
Yunzhu Li, Jiaming Song, Stefano Ermon
Neural Information Processing Systems http://nips.cc/
Decoding with Value Networks for Neural Machine Translation
Di He, Hanqing Lu, Yingce Xia, Tao Qin, Liwei Wang, Tie-Yan Liu
Neural Information Processing Systems http://nips.cc/
A multi-agent reinforcement learning model of common-pool resource appropriation
Humanity faces numerous problems of common-pool resource appropriation.
Reliable Decision Support using Counterfactual Models
One use of such an estimate is to evaluate risk; e.g. is this patient likely to die if I do not
Permutation-based Causal Inference Algorithms with Interventions
Yuhao Wang, Liam Solus, Karren Yang, Caroline Uhler
In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption.
2650d6089a6d640c5e85b2b88265dc2b-Paper.pdf
There are two major types of uncertainty one can model.