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Found 8 papers, 4 papers with code
Probabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning
Without exact knowledge of the true system dynamics, optimal control of non-linear continuous-time systems requires careful treatment of epistemic uncertainty.
Model-based Reinforcement Learning
reinforcement-learning
+1
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo
Several disciplines, such as econometrics, neuroscience, and computational psychology, study the dynamic interactions between variables over time.
Automatic structured variational inference
However, the performance of the variational approach depends on the choice of an appropriate variational family.
The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.
Bayesian nonparametric discontinuity design
Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions.
Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.
Forward Amortized Inference for Likelihood-Free Variational Marginalization
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss.
GP CaKe: Effective brain connectivity with causal kernels
Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity.
