28 accepted papers
Large Language Models (LLMs) exhibit remarkable capabilities in the hierarchical decomposition of complex tasks through semantic reasoning. However, their application in embodied systems faces challenges in ensuring reliable execution of subtask sequences and achieving one-shot success in long-term
We consider the problem of differentially private stochastic convex optimization (DP-SCO) in a distributed setting with $M$ clients, where each of them has a local dataset of $N$ i.i.d. data samples from an underlying data distribution. The objective is to design an algorithm to minimize a convex po…
Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits traditional detection methods, making anomaly generation essential for expanding the data repository. However, recent gener
To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the detector. However, the functional mismatch between restoration and detection networks can introduce instability and hinder…
We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space (RKHS). We study this problem under the additional constraint of joint differential privacy, where the agents needs to ensure that t…
Cognitive distortion is a critical issue in psychology, with most existing studies based on Burns’ cognitive distortion theory. However, differences in annotation standards lead to variations in building analysis tools, resulting in inconsistent analyses and limiting the generalizability of findings…
With the rise of mental health challenges, social media has become a key platform for emotional expression. Deep learning offers a promising solution for analyzing mental health but lacks flexibility and interpretability. Large language models (LLMs) introduce greater adaptability and can explain th
We consider distributed kernel bandits where $N$ agents aim to collaboratively maximize an unknown reward function that lies in a reproducing kernel Hilbert space. Each agent sequentially queries the function to obtain noisy observations at the query points. Agents can share information through a ce…
"Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue. Nonetheless, p…
In the current environment, psychological issues are prevalent and widespread, with social media serving as a key outlet for individuals to share their feelings. This results in the generation of vast quantities of data daily, where negative emotions have the potential to precipitate crisis situatio…
In industrial quality control, detecting defects is essential. However, manual checks and machine vision encounter challenges in complex conditions, as defects vary among products made of different materials and shapes. We create FD-UAD, Unsupervised Anomaly Detection Platform Based on Defect Autono…
We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. We show that this random exploration approach achieves the optimal error rates. Our…
Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy consideratio…
We consider distributed linear bandits where $M$ agents learn collaboratively to minimize the overall cumulative regret incurred by all agents. Information exchange is facilitated by a central server, and both the uplink and downlink communications are carried over channels with fixed capacity, whic…
In this letter, we synthesize a data-driven method to predict the optimal topology of an ad-hoc robot network. This problem is technically a multi-task classification problem. However, we divide it into a class of multi-class classification problems that can be more efficiently solved. For this purp
We consider sequential optimization of an unknown function in a reproducing kernel Hilbert space. We propose a Gaussian process-based algorithm and establish its order-optimal regret performance (up to a poly-logarithmic factor). This is the first GP-based algorithm with an order-optimal regret guar…
We consider the problem of noisy group testing where the test results are corrupted by noise with an unknown distribution. We propose an adaptive test plan consisting of a hierarchy of biased random walks guided by a local sequential test which together lend adaptivity and agnosticism to the unknown…
Most SLAM (Simultaneous Localization and Mapping) algorithms are based on the assumption that the scene is static. However, in practice, most real scenes usually contain moving objects. In this letter, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D
A framework based on iterative coordinate minimization (CM) is developed for stochastic convex optimization. Given that exact coordinate minimization is impossible due to the unknown stochastic nature of the objective function, the crux of the proposed optimization algorithm is an optimal control of…
The problem of anomaly detection among multiple processes is considered within the framework of sequential design of experiments. The objective is an active inference strategy consisting of a selection rule governing which process to probe at each time, a stopping rule on when to terminate the detec…
An active inference problem of detecting an anomalous process among M heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to ter…
We consider the problem of detecting heavy hitters and hierarchical heavy hitters among a large number of traffic flows modeled as random processes with unknown and potentially heavy-tailed distributions. The objective is an active inference strategy that determines, sequentially, which aggregated f…
We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his T-period payoff, the bidder determines the optimal allocation of his budget among his bids for $K$ goods at each period. As a bidding strategy, we propose a polynomial-time algorit…
We consider an oligopoly dynamic pricing problem where the demand model is unknown and the sellers have different marginal costs. We formulate the problem as a repeated game with incomplete information. We develop a dynamic pricing strategy that leads to a Pareto-efficient and subgame-perfect equili…
The problem of online learning and optimization of unknown Markov jump linear models is considered. A new online learning algorithm, referred to as Markovian simultaneous perturbations stochastic approximation (MSPSA), is proposed. It is shown that ν/ MSPSA achieves the minimax regret order of Θ(√T)…
We consider the problem of sampling a node-weighted graph. The objective is to infer the values of all nodes from that of a minimum subset of nodes by exploiting correlations in node values. We first introduce the concept of information dominating set (IDS). A subset of nodes in a given graph is an…
The quickest detection of short-term voltage instability in a smart grid is considered. The problem is formulated as a binary sequential composite hypothesis testing where the null hypothesis is a non-stationary process with an unknown exponentially decaying mean and the alternative is a nonstationa…
We study risk-averse multi-armed bandit problems under mean-variance measures. We consider two risk mitigation models. In the first model, the variations in the reward values obtained at different times are considered as risk and the objective is to minimize the mean-variance of the observed rewards…