11 accepted papers
Decentralized online convex optimization (D-OCO), where multiple agents within a network collaboratively learn optimal decisions in real-time, arises naturally in applications such as federated learning, sensor networks, and multi-agent control. In this paper, we study D-OCO under unknown, time- and
In this paper, we study dynamic regret in unconstrained online convex optimization (OCO) with movement costs. Specifically, we generalize the standard setting by allowing the movement cost coefficients $\lambda_t$ to vary arbitrarily over time. Our main contribution is a novel algorithm that establi…
We study the distributed multi-agent multi-armed bandit problem with heterogeneous rewards over random communication graphs. Uniquely, at each time step $t$ agents communicate over a time-varying random graph $\mathcal{G}\_t$ generated by applying the Erdős–Rényi model to a fixed connected base grap…
We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (MLLMs). EgoBlind comprises 1,392 first-person videos from the daily lives of blind and visually impaired individuals. It…
In this work, we study the online convex optimization problem with curved losses and delayed feedback. When losses are strongly convex, existing approaches obtain regret bounds of order $d_{\max} \ln T$, where $d_{\max}$ is the maximum delay and $T$ is the time horizon. However, in many cases, this…
In this work, we study the federated multi-armed bandit (FMAB) problem, where a set of distributed agents collaboratively aim to minimize cumulative regret while interacting with a shared set of arms. Unlike traditional centralized bandit models, agents in FMAB settings are connected via a communica…
In this paper, we focus on the research of federated multi-armed bandit (FMAB) problems where agents can only communicate with their neighbors. All agents aim to solve a common multi-armed bandit (MAB) problem to minimize individual regrets, while group regret can also be minimized. In a federated b
Ego-centric Traffic Anomaly Detection (TAD) aims to identify abnormal events in videos captured by dashboardmounted cameras in vehicles. Compared to anomaly detection in roadside surveillance videos, ego-centric TAD poses greater challenges due to the dynamic backgrounds caused by vehicle motion. Pr
We study a $K$-armed bandit with delayed feedback and intermediate observations. We consider a model, where intermediate observations have a form of a finite state, which is observed immediately after taking an action, whereas the loss is observed after an adversarially chosen delay. We show that th…
We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influences the accuracy of their prediction through some unknown Lipschitz ``productivity'' function. In each round, the lear…
We present the first demonstration of a battery-free untethered wirelessly powered sub-gram jumping robot on an insect-scale. In order to operate the insect-sized robot autonomously, the limitation in battery use emphasizes the need for a wireless power transmission system as an onboard power soluti…