Learning Graphical Models
Supplementary Material: Lossy Compression for Lossless Prediction T able of Contents
We will often use variational optimization. When the variational family is not made explicit it means that the optimization is over all functions with the correct domain and codomain, e.g.
Testing Determinantal Point Processes
Determinantal point processes (DPPs) are popular probabilistic models of diversity.
Hardness in Markov Decision Processes: Theory and Practice
Finally, we benchmark five tabular agents in our newly proposed benchmark.
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In medical imaging, for example, experts will often disagree about the exact location of object boundaries.
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Work done while at Harvard University.
Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization.
Contrastive Active Inference
In contrast, reinforcement learning requires human-designed rewards to accomplish any desired outcome.
Supplementary material: High-recall causal discovery for autocorrelated time series with latent confounders
It allows for both latent confounders and selection variables, although in this paper we assume the absence of selection variables.
