Many industrial and scientific applications require optimization of one or more objectives by tuning dozens or hundreds of input parameters. While Bayesian optimization has been a popular approach for the efficient optimization of blackbox functions, its performance decreases drastically as the dimensionality of the search space increases (i.e., above twenty). Recent advancements in high-dimensional Bayesian optimization (HDBO) seek to mitigate this issue through techniques such as adaptive local search with trust regions or dimensionality reduction using random embeddings. In this paper, we provide a close examination of these advancements and show that sampling strategy plays a prominent role and is key to tackling the curse-of-dimensionality. We then propose cylindrical Thompson sampling (CTS), a novel strategy that can be integrated into single- and multi-objective HDBO algorithms. We demonstrate this by integrating CTS as a modular component in state-of-the-art HDBO algorithms. We verify the effectiveness of CTS on both synthetic and real-world high-dimensional problems, and show that CTS largely enhances existing HDBO methods.

Identifying an appropriate task space can simplify solving robotic manipulation problems. One solution is deploying control algorithms in a learned low-dimensional action space. Linear and nonlinear action mapping methods have trade-offs between simplicity and the ability to express motor commands outside of a single low-dimensional subspace. We propose that learning local linear action representations can achieve both of these benefits. Our state-conditioned linear maps ensure that for any given state, the high-dimensional robotic actuation is linear in the low-dimensional actions. As the robot state evolves, so do the action mappings, so that necessary motions can be performed during a task. These local linear representations guarantee desirable theoretical properties by design. We validate these findings empirically through two user studies. Results suggest state-conditioned linear maps outperform conditional autoencoder and PCA baselines on a pick-and-place task and perform comparably to mode switching in a more complex pouring task.

A significant research problem of recent interest is the localization of targets like vessels, surgical needles, and tumors in photoacoustic (PA) images.To achieve accurate localization, a high photoacoustic signal-to-noise ratio (SNR) is required. However, this is not guaranteed for deep targets, as optical scattering causes an exponential decay in optical fluence with respect to tissue depth. To address this, we develop a novel deep learning method designed to explicitly exhibit robustness to noise present in photoacoustic radio-frequency (RF) data. More precisely, we describe and evaluate a deep neural network architecture consisting of a shared encoder and two parallel decoders. One decoder extracts the target coordinates from the input RF data while the other boosts the SNR and estimates clean RF data. The joint optimization of the shared encoder and dual decoders lends significant noise robustness to the features extracted by the encoder, which in turn enables the network to contain detailed information about deep targets that may be obscured by noise. Additional custom layers and newly proposed regularizers in the training loss function (designed based on observed RF data signal and noise behavior) serve to increase the SNR in the cleaned RF output and improve model performance. To account for depth-dependent strong optical scattering, our network was trained with simulated photoacoustic datasets of targets embedded at different depths inside tissue media of different scattering levels. The network trained on this novel dataset accurately locates targets in experimental PA data that is clinically relevant with respect to the localization of vessels, needles, or brachytherapy seeds. We verify the merits of the proposed architecture by outperforming the state of the art on both simulated and experimental datasets.