Yijiang Pang

Yijiang Pang, Boyang Liu, Jiayu Zhou

Recently, adversarial training has been incorporated in self-supervised contrastive pre-training to augment label efficiency with exciting adversarial robustness. However, the robustness came at a cost of expensive adversarial training. In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing. It boosts both standard accuracy and robust accuracy, and significantly reduces training costs as compared with adversarial training. We use extensive empirical studies to show that the proposed RUSH outperforms robust classifiers from adversarial training, by a significant margin on common benchmarks (CIFAR-10, CIFAR-100, and STL-10) under first-order attacks. In particular, under $\ell_{\infty}$-norm perturbations of size 8/255 PGD attack on CIFAR-10, our model using ResNet-18 as backbone reached 77.8% robust accuracy and 87.9% standard accuracy. Our work has an improvement of over 15% in robust accuracy and a slight improvement in standard accuracy, compared to the state-of-the-arts.

Bao Hoang, Yijiang Pang, Siqi Liang et al.

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.

Yijiang Pang, Jiayu Zhou

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, and the derived results help understand and inherently connect the two popular types of zeroth-order methods, basic random search and stochastic three-point method. The theoretical properties also shed light on a Variant of S2P (VS2P), through exploiting our new convergence properties that better represent the dynamics of deep models in training. Our comprehensive empirical results show that VS2P is highly effective in optimizing objectives for deep models. It outperforms or achieves competitive performance compared to standard methods across various model types and scales.

Yijiang Pang, Shuyang Yu, Bao Hoang et al.

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.

Zhengbang Yang, Junyuan Hong, Yijiang Pang et al.

Cognitive health in older adults presents a growing challenge. Although conversational interventions show feasibility in improving cognitive wellness, human caregiver resources remain overloaded. AI-based chatbots have shown promise, yet existing work is often limited to implicit strategies or heavily depends on training and label resources. In response, we propose a strategy-guided AI chatbot named ChatWise that follows a dual-level conversation reasoning framework. It integrates macro-level strategy planning and micro-level utterance generation to enable engaging, multi-turn dialogue tailored to older adults. Empirical results show that ChatWise closely aligns with professional human caregiver behaviors in offline evaluation using real clinic data, and achieves positive user cognitive and emotional responses in interactive simulations with digital twins, which significantly outperforms AI baselines that follow implicit conversation generation.

Yijiang Pang, Bao Hoang, Jiayu Zhou

Sub-population shift is a specific type of domain shift that highlights changes in data distribution within specific sub-groups or populations between training and testing. Sub-population shift accounts for a significant source of algorithmic bias and calls for distributional robustness. Recent studies found inherent distributional robustness in multi-modality foundation models, such as the vision-language model CLIP, yet this robustness is vulnerable through parameter fine-tuning. In this paper, we propose leveraging the connection of robustness among different modalities and reshaping the distributional robustness of one modality with another. Specifically, in the context of the distributional robustness of CLIP, we propose to leverage natural language inputs to debias the image feature representations, to improve worst-case performance on sub-populations. Our extensive empirical studies show that image representations debiased by natural language can achieve significant performance improvement and reduction of performance instability under sub-population shifts.

Yijiang Pang, Chao Huang, Rui Liu

Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team's powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, a novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explores two aspects of human trust (trust level and trust preference), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.

Yijiang Pang, Rui Liu

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a heterogeneous team, is widely used for emergent scenarios such as criminal tracking in social security and victim assistance in a natural disaster. These emergent scenarios require a cooperative team to quickly terminate the current task and transit the system to a new task, bringing difficulty in motion planning. Moreover, due to the immediate task transitions, uncertainty from both physical systems and prior tasks is accumulated to decrease swarm performance, causing robot failures and influencing the cooperation effectiveness between the human and the robot swarm. Therefore, given the quick-transition requirements and the introduced uncertainty, it is challenging for a human-swarm system to respond to emergent tasks, compared with executing normal tasks where a gradual transition between tasks is allowed. Human trust reveals the behavior expectations of others and is used to adjust unsatisfactory behaviors for better cooperation. Inspired by human trust, in this paper, a trust-aware reflective control (Trust-R) is developed to dynamically calibrate human-swarm cooperation. Trust-R, based on a weighted mean subsequence reduced algorithm (WMSR) and human trust modeling, helps a swarm to self-reflect its performance from the perspective of human trust; then proactively correct its faulty behaviors in an early stage before a human intervenes. One typical task scenario {emergency response} was designed in the real-gravity simulation environment, and a human user study with 145 volunteers was conducted. Trust-R's effectiveness in correcting faulty behaviors in emergency response was validated by the improved swarm performance and increased trust scores.

Yijiang Pang, Rui Liu

Emergency happens in human-UAV cooperation, such as criminal activity tracking and urgent needs for ground assistance. Emergency response usually has high requirements on the motion control of the multi-UAV system, by maintaining both the team performance and team behaviors. However When a UAV swarm executes tasks in a real-world environment, because of real-world factors, such as system reliability and environmental disturbances, some robots in the swarm will behave abnormally, such as slow flocking speed, wrong heading direction, or poor spatial relations. In the meanwhile, incorrect trust between human and UAV swarm could map the abnormal behavior of faulty robot to the whole swarm and request a time-consuming intervention from human supervisor, damage the UAV swarm response for a dynamic task, even evolve to a failure of task because of accumulated error. To correct reflect the trust between humans and UAV swarm and rebuild the trust to improve the performance caused by incorrect trust. We propose a dynamic trust repair model. The dynamic trust model focus on human-supervisory UAV system which can help UAV swarm to reduce the negative influence from faulty UAV on the performance of the UAV swarm, get a flexible reaction and stable human-supervisory UAV task performance. Results show that trust model could improve the performance of the swarm for dynamic task response and regain human trust.

Qifei Yu, Zhexin Shen, Yijiang Pang et al.

A mixed aerial and ground robot team, which includes both unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is widely used for disaster rescue, social security, precision agriculture, and military missions. However, team capability and corresponding configuration vary since robots have different motion speeds, perceiving ranges, reaching areas, and resilient capabilities to the dynamic environment. Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations and maximum utilization of robot capability. To address this challenge for effective mixed ground and aerial teaming, this paper developed a novel teaming method, proficiency aware multi-agent deep reinforcement learning (Mix-RL), to guide ground and aerial cooperation by considering the best alignments between robot capabilities, task requirements, and environment conditions. Mix-RL largely exploits robot capabilities while being aware of the adaption of robot capabilities to task requirements and environment conditions. Mix-RL's effectiveness in guiding mixed teaming was validated with the task "social security for criminal vehicle tracking".