Bayesian Neural Networks (BNNs) offer uncertainty quantification but are computationally expensive, limiting their practical deployment. This paper introduces a neuron-level pruning framework that reduces BNN complexity while preserving predictive performance. Unlike existing weight-level pruning techniques, our approach removes entire neurons, enabling significant memory savings and inference speedups without requiring specialized hardware. We propose a pruning loss based on the Wasserstein distance, balancing model sparsity and predictive accuracy. Our method is fully automatic, eliminating the need for manual hyperparameter tuning. Experimental results on UCI regression and Fashion MNIST datasets demonstrate that our framework can prune over 80% of neurons while maintaining predictive distribution integrity. Additionally, we validate the Lottery Ticket Hypothesis in the Bayesian setting, showing that pruned subnetworks retain performance and learn faster when retrained. This work represents a step toward making BNNs more scalable for real-world applications.

This work investigates the impact of action sampling strategies on the performance of Sampled MuZero, a reinforcement learning algorithm designed for continuous control settings like robotics. In contrast to discrete domains, continuous action spaces require sampling from a proposal distribution beta during Monte Carlo Tree Search (MCTS), a process that is underexplored despite being central to the algorithm's effectiveness. We systematically study how performance is influenced by (1) the choice of beta distribution and (2) the use of progressive widening, an MCTS augmentation that samples additional actions for frequently visited search tree nodes. Our JAX-based implementation of Sampled MuZero is evaluated on the Brax HalfCheetah environment, testing beta as either a uniform distribution or the agent's policy distribution. Additionally, we examine how different progressive widening parameters affect planning depth and computational efficiency. Results show that while temperature modulation provides marginal benefits under specific conditions, progressive widening with properly calibrated parameters can improve planning depth and episode returns.