Uncertainty
Estimating Convergence of Markov chains with L-Lag Couplings
Niloy Biswas, Pierre E. Jacob, Paul Vanetti
Neural Information Processing Systems http://nips.cc/
Multi-objective Bayesian optimisation with preferences over objectives
Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
These preferences are defined based on the stability of the obtained solutions with respect to preferred objective functions.
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
A graphical model is a structured representation of the data generating process. The traditional method to reason over random variables is to perform inference in this graphical model. However, in many cases the generating process is only a poor approximation of the much more complex true data generating process, leading to suboptimal estimations.
Tensor Monte Carlo: Particle Methods for the GPU era
Neural Information Processing Systems http://nips.cc/
A Spectral Approach to Item Response Theory
The core of our algorithm is the computation of the stationary distribution of a Markov chain defined on an item-item graph.
Learning Fractional White Noises in Neural Stochastic Differential Equations
Differential equations play important roles in modeling complex physical systems.
Unsupervised Causal Generative Understanding of Images
We present a novel framework for unsupervised object-centric 3D scene understanding that generalizes robustly to out-of-distribution images.
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Meta-learning is based on the idea of learning-how-to-learn by improving the algorithm itself (Schmidhuber, 1987; Hospedales et al., 2020).