Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the i.i.d. rule. A common strategy is to harmonize the site bias while retaining important biological information. The ComBat is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, ComBat lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel Cluster ComBat harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of ComBat harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization.

Hyperparameter tuning, particularly the selection of an appropriate learning rate in adaptive gradient training methods, remains a challenge. To tackle this challenge, in this paper, we propose a novel parameter-free optimizer, \textsc{AdamG} (Adam with the golden step size), designed to automatically adapt to diverse optimization problems without manual tuning. The core technique underlying \textsc{AdamG} is our golden step size derived for the AdaGrad-Norm algorithm, which is expected to help AdaGrad-Norm preserve the tuning-free convergence and approximate the optimal step size in expectation w.r.t. various optimization scenarios. To better evaluate tuning-free performance, we propose a novel evaluation criterion, \textit{reliability}, to comprehensively assess the efficacy of parameter-free optimizers in addition to classical performance criteria. Empirical results demonstrate that compared with other parameter-free baselines, \textsc{AdamG} achieves superior performance, which is consistently on par with Adam using a manually tuned learning rate across various optimization tasks.

Large foundation models, such as large language models, have performed exceptionally well in various application scenarios. Building or fully fine-tuning such large models is usually prohibitive due to either hardware budget or lack of access to backpropagation. The zeroth-order methods offer a promising direction for tackling this challenge, where only forward passes are needed to update the model. This paper introduces an efficient Stochastic Two-Point (S2P) approach within the gradient-free regime. We present the theoretical convergence properties of S2P under the general and relaxed smoothness assumptions. The theoretical properties also shed light on a faster and more stable S2P variant, Accelerated S2P (AS2P), through exploiting our new convergence properties that better represent the dynamics of deep models in training. Our comprehensive empirical results show that AS2P is highly effective in optimizing objectives for large deep models, including language models, and outperforms standard methods across various model types and scales, with 2 $\times$ speed-up in training over most conducted tasks.

Mixed Cooperation and competition are the actual scenarios of deploying multi-robot systems, such as the multi-UAV/UGV teaming for tracking criminal vehicles and protecting important individuals. Types and the total number of robot are all important factors that influence mixed cooperation quality. In various real-world environments, such as open space, forest, and urban building clusters, robot deployments have been influenced largely, as different robots have different configurations to support different environments. For example, UGVs are good at moving on the urban roads and reach the forest area while UAVs are good at flying in open space and around the high building clusters. However, it is challenging to design the collective behaviors for robot cooperation according to the dynamic changes in robot capabilities, working status, and environmental constraints. To solve this question, we proposed a novel proficiency-aware mixed environment multi-agent deep reinforcement learning (Mix-DRL). In Mix-DRL, robot capability and environment factors are formalized into the model to update the policy to model the nonlinear relations between heterogeneous team deployment strategies and the real-world environmental conditions. Mix-DRL can largely exploit robot capability while staying aware of the environment limitations. With the validation of a heterogeneous team with 2 UAVs and 2 UGVs in tasks, such as social security for criminal vehicle tracking, the Mix-DRL's effectiveness has been evaluated with $14.20\%$ of cooperation improvement. Given the general setting of Mix-DRL, it can be used to guide the general cooperation of UAVs and UGVs for multi-target tracking.