Bayesian Inference
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Raanan Y. Rohekar, Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik
We introduce a principled approach for unsupervised structure learning of deep neural networks.
A Bayesian Approach to Generative Adversarial Imitation Learning
Wonseok Jeon, Seokin Seo, Kee-Eung Kim
Y et, one of the fundamental challenges lies in the fact that obtaining trajectory samples from the environment is often very costly, e.g., physical robots situated in real-world.
Transfer of Value Functions via Variational Methods Andrea Tirinzoni Politecnico di Milano nullnullnullnullnullnullnullnullnullnullnullnullnullnullnull
We consider the problem of transferring value functions in reinforcement learning.
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily Fox, Christopher Nemeth
When applied to distributions defined on a constrained space the time-discretization error can dominate when we are near the boundary of the space.
Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn, Kelvin Xu, Sergey Levine
In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution.
Rectangular Bounding Process
Xuhui Fan, Bin Li, Scott SIsson
By tailoring a multi-dimensional space (or multi-dimensional array) into a number of rectangular regions, the partition model can fit data using these "blocks" such that the data within each block
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
JING LI, Rafal Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet
Neural Information Processing Systems http://nips.cc/
Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes Andrea Tirinzoni Politecnico di Milano andrea.tirinzoni@polimi.it Xiangli Chen Amazon Robotics
What policy should be employed in a Markov decision process with uncertain parameters? Robust optimization's answer to this question is to use rectangular
Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments
Daniel Johnson, Daniel Gorelik, Ross E. Mawhorter, Kyle Suver, Weiqing Gu, Steven Xing, Cody Gabriel, Peter Sankhagowit
We present an approach for simultaneously separating and localizing multiple sound sources using recorded microphone data.
Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
Fei Jiang, Guosheng Yin, Francesca Dominici
Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes. The BMS method can effectively suppress the non-boundary spike points with large instantaneous changes.