Learning Graphical Models
Causal Inference via Kernel Deviance Measures
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
In many areas of science, we strive to answer questions that are fundamentally causal in nature.
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
In our output perturbation method, the parties combine local models within a secure computation and then add the required differential privacy noise before revealing the model.
Multi-domain Causal Structure Learning in Linear Systems
AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang
Our approach unifies the idea in those works and generalizes to the case that there is no such invariance across the domains. Our proposed methods are generally capable of identifying causal direction from fewer than ten domains.
A Bayes-Sard Cubature Method
Toni Karvonen, Chris J. Oates, Simo Sarkka
Neural Information Processing Systems http://nips.cc/
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Agastya Kalra, Abdullah Rashwan, Wei-Shou Hsu, Pascal Poupart, Prashant Doshi, Georgios Trimponias
Neural Information Processing Systems http://nips.cc/
Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs
Yanlin Han, Piotr Gmytrasiewicz
It extends POMDPs to multi-agent settings by including models of other agents in the state space and forming a hierarchical belief structure. In order to predict other agents' actions using I-POMDPs, we
Improving Explorability in Variational Inference with Annealed Variational Objectives Chin-Wei Huang,?, 1 Shawn Tan
In our experiments, we demonstrate our method's
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
Nima Anari, Constantinos Daskalakis, Wolfgang Maass, Christos Papadimitriou, Amin Saberi, Santosh Vempala
We analyze linear independence of rank one tensors produced by tensor powers of randomly perturbed vectors. This enables efficient decomposition of sums of high-order tensors. Our analysis builds upon Bhaskara et al.
Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
Nishant Desai, Andrew Critch, Stuart J. Russell
Harsanyi's theorem shows that when the principals have a