2 citations · 8 across the 20 of their papers we have counts for
22 papers
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
Wenkai Guo, Xuefeng Liu, Haolin Wang +3
Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteri…
The Power of Adaptation: Boosting In-Context Learning through Adaptive Prompting
Shuzhang Cai, Twumasi Mensah-Boateng, Xander Kuksov +2
Large Language Models (LLMs) have demonstrated exceptional abilities across a broad range of language-related tasks, including generating solutions to complex reasoning problems. A…
Learning Submodular Sequencing from Samples
Jing Yuan, Shaojie Tang
This paper addresses the problem of sequential submodular maximization: selecting and ranking items in a sequence to optimize some composite submodular function. In contrast to mos…
The Power of Second Chance: Personalized Submodular Maximization with Two Candidates
Jing Yuan, Shaojie Tang
Most of existing studies on submodular maximization focus on selecting a subset of items that maximizes a \emph{single} submodular function. However, in many real-world scenarios,…
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
Xinghao Wu, Jianwei Niu, Xuefeng Liu +4
Federated Learning (FL) faces challenges due to data heterogeneity, which limits the global model's performance across diverse client distributions. Personalized Federated Learning…
Submodular Participatory Budgeting
Jing Yuan, Shaojie Tang
Participatory budgeting refers to the practice of allocating public resources by collecting and aggregating individual preferences. Most existing studies in this field often assume…
Assortment Planning with Sponsored Products
Shaojie Tang, Shuzhang Cai, Jing Yuan +1
In the rapidly evolving landscape of retail, assortment planning plays a crucial role in determining the success of a business. With the rise of sponsored products and their increa…
Non-monotone Sequential Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we study a fundamental problem in submodular optimization, which is called sequential submodular maximization. Specifically, we aim to select and rank a group of …
Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints
Shaojie Tang, Jing Yuan
Machine learning algorithms play an important role in a variety of important decision-making processes, including targeted advertisement displays, home loan approvals, and criminal…
Achieving Long-term Fairness in Submodular Maximization through Randomization
Shaojie Tang, Jing Yuan, Twumasi Mensah-Boateng
Submodular function optimization has numerous applications in machine learning and data analysis, including data summarization which aims to identify a concise and diverse set of d…
Group Fairness in Non-monotone Submodular Maximization
Jing Yuan, Shaojie Tang
Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small se…
Worst-Case Adaptive Submodular Cover
Jing Yuan, Shaojie Tang
In this paper, we study the adaptive submodular cover problem under the worst-case setting. This problem generalizes many previously studied problems, namely, the pool-based active…
Streaming Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setti…
Partial-Monotone Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
Many sequential decision making problems, including pool-based active learning and adaptive viral marketing, can be formulated as an adaptive submodular maximization problem. Most…
Group Equality in Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings. It has been shown that the…
Partial-Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
The goal of a typical adaptive sequential decision making problem is to design an interactive policy that selects a group of items sequentially, based on some partial observations,…
Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising
Shaojie Tang, Jing Yuan
The idea of social advertising (or social promotion) is to select a group of influential individuals (a.k.a \emph{seeds}) to help promote some products or ideas through an online s…
Optimal Sampling Gaps for Adaptive Submodular Maximization
Shaojie Tang, Jing Yuan
Running machine learning algorithms on large and rapidly growing volumes of data is often computationally expensive, one common trick to reduce the size of a data set, and thus red…
Adaptive Regularized Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we study the problem of maximizing the difference between an adaptive submodular (revenue) function and an non-negative modular (cost) function under the adaptive se…
Adaptive Submodular Meta-Learning
Shaojie Tang, Jing Yuan
Meta-Learning has gained increasing attention in the machine learning and artificial intelligence communities. In this paper, we introduce and study an adaptive submodular meta-lea…
Adaptive Cascade Submodular Maximization
Shaojie Tang, Jing Yuan
In this paper, we propose and study the cascade submodular maximization problem under the adaptive setting. The input of our problem is a set of items, each item is in a particular…
Adaptive Robust Optimization with Nearly Submodular Structure
Shaojie Tang, Jing Yuan
Constrained submodular maximization has been extensively studied in the recent years. In this paper, we study adaptive robust optimization with nearly submodular structure (ARONSS)…