Directed Networks
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Potential-based reward shaping incorporates prior domain knowledge in the form of additional rewards provided during training to speed up convergence of reinforcement learning algorithms, without changing the optimal policies (Ng et al. [1999]).
Experimental Design for Cost-Aware Learning of Causal Graphs
We first show that this problem is NP-hard.
Infinite-Horizon Gaussian Processes
Arno Solin, James Hensman, Richard E. Turner
This work was undertaken whilst AS was a Visiting Research Fellow with University of Cambridge.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
Neural Information Processing Systems http://nips.cc/
Nonparametric learning from Bayesian models with randomized objective functions
Simon Lyddon, Stephen Walker, Chris C. Holmes
This assumption is problematic, particularly in complex data environments.
Generalizing Tree Probability Estimation via Bayesian Networks
Cheng Zhang, Frederick A Matsen IV
Neural Information Processing Systems http://nips.cc/
Bayesian Nonparametric Spectral Estimation
Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time
Stochastic Expectation Maximization with Variance Reduction
However, sEM has a slow asymptotic convergence rate due to the high variance of each update.
Evidential Deep Learning to Quantify Classification Uncertainty
Murat Sensoy, Lance Kaplan, Melih Kandemir
Neural Information Processing Systems http://nips.cc/