Instead ofsampling andscoring allpossible structures individually,we assume the generator of the CTBN to be composed as a mixture of generators stemming from different structures.

Table 5 shows the positioning of FedL2P against existing literature. This personalized policy can either 1) be fixed, e.g. FedEx which randomly samples per-client hyperparameters from learned categorical distributions. Scenarios where it's expensive to train from scratch for a new group of clients, e.g. This is illustrated in Section 4.4 where we adapt a publicly available pretrained Scenarios where it's important to also maintain a global model with high initial accuracy - Note that our approach also does not critically depend on the global model's performance.

Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest. A key property of this workflow is the efficacy of performing sparse or parameter-efficient fine-tuning, meaning that by updating only a tiny fraction of the whole FM parameters on a downstream task can lead to surprisingly good performance, often even superior to a full model update. However, it is not clear what is the optimal and principled way to select which parameters to update. Although a growing number of sparse fine-tuning ideas have been proposed, they are mostly not satisfactory, relying on hand-crafted heuristics or heavy approximation. In this paper we propose a novel Bayesian sparse fine-tuning algorithm: we place a (sparse) Laplace prior for each parameter of the FM, with the mean equal to the initial value and the scale parameter having a hyper-prior that encourages small scale.

The Newton-Raphson (NR) method is widely used for solving power flow (PF) equations due to its quadratic convergence. However, its performance deteriorates under poor initialization or extreme operating scenarios, e.g., high levels of renewable energy penetration. Traditional NR initialization strategies often fail to address these challenges, resulting in slow convergence or even divergence. We propose the use of reinforcement learning (RL) to optimize the initialization of NR, and introduce a novel quantum-enhanced RL environment update mechanism to mitigate the significant computational cost of evaluating power system states over a combinatorially large action space at each RL timestep by formulating the voltage adjustment task as a quadratic unconstrained binary optimization problem. Specifically, quantum/digital annealers are integrated into the RL environment update to evaluate state transitions using a problem Hamiltonian designed for PF. Results demonstrate significant improvements in convergence speed, a reduction in NR iteration counts, and enhanced robustness under different operating conditions.

Neural Information Processing Systems http://nips.cc/

Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physics based models are rooted in rich understanding of the physical processes, a significant performance gap still remains which can be potentially addressed by leveraging the recent advances in machine learning. The goal of this work is to incorporate our understanding of hydrological processes and constraints into machine learning algorithms to improve the predictive performance. Traditional ML models for this problem predict streamflow using weather drivers as input. However there are multiple intermediate processes that interact to generate streamflow from weather drivers. The key idea of the approach is to explicitly model these intermediate processes that connect weather drivers to streamflow using a multi-task learning framework. While our proposed approach requires data about intermediate processes during training, only weather drivers will be needed to predict the streamflow during testing phase. We assess the efficacy of the approach on a simulation dataset generated by the SWAT model for a catchment located in the South Branch of the Root River Watershed in southeast Minnesota. While the focus of this paper is on improving the performance given data from a single catchment, methodology presented here is applicable to ML-based approaches that use data from multiple catchments to improve performance of each individual catchment.

Acoustic sensors, particularly Doppler V elocity Logs (DVLs), have become indispensable for underwater navigation and environmental sensing. To enable robust fusion of DVL measurements with data from other sensors, precise calibration of extrinsic parameters and temporal synchronization is critical, especially in challenging underwater operating conditions [1]-[5]. Prior work by Xu et al. [6] and Westman and Kaes [7] framed the DVL-camera calibration as an odometry alignment problem, matching the trajectory from a DVL-IMU system against the visual one from a camera. A critical limitation of these approaches is their implicit assumption of known and static DVL-IMU extrinsics, which is frequently violated in underwater environments due to their dynamic nature. While studies in [8]-[11] address the calibration of IMU-free DVLs, their applicability is strictly limited to co-sensors that provide direct linear and angular velocity measurements, such as SINS/GPS systems. Crucially, a significant gap persists across all these works: none address the calibration of translational extrinsic nor account for temporal synchronization across heterogeneous sensors.

Table 5 shows the positioning of FedL2P against existing literature. This personalized policy can either 1) be fixed, e.g. FedEx which randomly samples per-client hyperparameters from learned categorical distributions. Scenarios where it's expensive to train from scratch for a new group of clients, e.g. This is illustrated in Section 4.4 where we adapt a publicly available pretrained Scenarios where it's important to also maintain a global model with high initial accuracy - Note that our approach also does not critically depend on the global model's performance.