A Bayesian foundation for individual learning under uncertainty

A Bayesian Foundation for Individual Learning Under Uncertainty

Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic h...

Bayesian Optimization for Learning Gaits under Uncertainty An experimental comparison on a dynamic bipedal walker

Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parameterization, finding nearoptimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and t...

Bayesian Deep Learning | Uncertainty in Deep Learning

Bayesian Switching Interaction Analysis Under Uncertainty

We introduce a Bayesian discrete-time framework for switching-interaction analysis under uncertainty, in which latent interactions, switching pattern and signal states and dynamics are inferred from noisy (and possibly missing) observations of these signals. We propose reasoning over full posterior distribution of these latent variables as a means of combating and characterizing uncertainty. Th...

Multi-Attribute Bayesian Optimization under Utility Uncertainty

We consider multi-attribute Bayesian optimization, where each design in an optimization problem’s feasible space is associated with a vector of attributes that can be evaluated via a time-consuming computer code, and each vector of attributes is assigned a utility according to a decision-maker’s utility function. A standard Bayesian optimization approach could be applied if the utility function...