4 papers
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference
Paul E. Chang, Nasrulloh Loka, Daolang Huang +3
Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are l…
Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang, Manuel Haussmann, Ulpu Remes +5
Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification.…
Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence
Jukka Corander, Ulpu Remes, Timo Koski
Choice of appropriate structure and parametric dimension of a model in the light of data has a rich history in statistical research, where the first seminal approaches were develop…
Nonparametric likelihood-free inference with Jensen-Shannon divergence for simulator-based models with categorical output
Jukka Corander, Ulpu Remes, Ida Holopainen +1
Likelihood-free inference for simulator-based statistical models has recently attracted a surge of interest, both in the machine learning and statistics communities. The primary fo…