14 accepted papers
Vertical Federated Learning (VFL) is a distributed machine learning paradigm in which participants train models with vertically partitioned data. Many previous studies have identified backdoor vulnerabilities in VFL systems. However, limited effort has been devoted to developing defenses against suc
Portfolio optimization encounters persistent challenges in adapting to dynamic markets due to static assumptions and high-dimensional decision spaces. Although reinforcement learning (RL) has emerged as a potential solution, conventional reward engineering often fails to capture complex market dynam
Despite their remarkable image generation capabilities, text-to-image diffusion models inadvertently learn inappropriate concepts from vast and unfiltered training data, which leads to various ethical and business risks. Specifically, model-generated images may exhibit not safe for work (NSFW) conte…
We consider the problem of diversity maximization from the perspective of individual fairness: given a set $P$ of $n$ points in a metric space, we aim to extract a subset $S$ of size $k$ from $P$ so that (1) the diversity of $S$ is maximized and (2) $S$ is \emph{individually fair} in the sense that…
Data Shapley values provide a principled approach for quantifying the contribution of individual training examples to machine learning models. However, computing these values often requires computational complexity that is exponential in the data size, and this has led researchers to pursue efficien…
Kernel Density Estimation (KDE) is a fundamental problem with broad machine learning applications. In this paper, we investigate the KDE problem under Local Differential Privacy (LDP), a setting in which users privatize data on their own devices before sending them to an untrusted server for analyti…
Recent studies have shown that Pre-trained Language Models (PLMs) are vulnerable to adversarial examples, crafted by introducing human-imperceptible perturbations to clean examples to deceive the models. This vulnerability stems from the divergence in the data distributions of clean and adversarial…
As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differential privacy. However, the state-of-the-art text sanitization mechanisms based on a relaxed notion of metric local dif…
Submodular maximization has attracted extensive attention due to its numerous applications in machine learning and artificial intelligence. Many real-world problems require maximizing multiple submodular objective functions at the same time. In such cases, a common approach is to select a representa…
Recommender systems typically suggest to users content similar to what they consumed in the past. A user, if happening to be exposed to strongly polarized content, might be steered towards more and more radicalized content by subsequent recommendations, eventually being trapped in what we call a "ra…
This paper investigates a new yet challenging problem called Reverse k-Maximum Inner Product Search (RkMIPS). Given a query (item) vector, a set of item vectors, and a set of user vectors, the problem of RkMIPS aims to find a set of user vectors whose inner products with the query vector are one of…
Traffic classification is a critical task in network security and management. Recent research has demonstrated the effectiveness of the deep learning-based traffic classification method. However, the following limitations remain: (1) the traffic representation is simply generated from raw packet byt…
Text-based captchas are the most widely used security mechanism currently. Due to the limitations and specificity of the segmentation algorithm, the early segmentation-based attack method has been unable to deal with the current captchas with newly introduced security features (e.g., occluding lines…
Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however, unrealistic. To bridge the gap between theoretical graph attacks and real-worl…