Uncertainty
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
Neural Information Processing Systems http://nips.cc/
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Neural Information Processing Systems http://nips.cc/
Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony, Mohammad Ali Bashiri, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Multi-view Matrix Factorization for Linear Dynamical System Estimation
Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari
Maximum likelihood for LDS estimation, on the other hand, has several advantages.
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng
This game continues until the agent reaches a final state and receives a terminal reward .
Structured Embedding Models for Grouped Data
Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei
We study how the word usage of U.S. Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons.
Perturbative Black Box Variational Inference
Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt
Drawing on variational perturbation theory of statistical physics, we use these insights to construct a family of new variational bounds.
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
There is rapidly growing interest in using Bayesian optimization to tune model and inference hyperparameters for machine learning algorithms that take a long time to run.
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Nicolò Colombo, Ricardo Silva, Soong Moon Kang
Based on the reconstruction of the users' travel time distribution, the model is