2 citations · 4 across the 18 of their papers we have counts for
9 papers · 1 filter
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3
Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize expe…
Distributionally Robust Federated Learning with Outlier Resilience
Zifan Wang, Xinlei Yi, Xenia Konti +2
Federated learning (FL) enables collaborative model training without direct data sharing, but its performance can degrade significantly in the presence of data distribution perturb…
Federated Flow Matching
Zifan Wang, Anqi Dong, Mahmoud Selim +2
Data today is decentralized, generated and stored across devices and institutions where privacy, ownership, and regulation prevent centralization. This motivates the need to train…
Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti, Yi Shen, Zifan Wang +4
The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data gene…
Source-Guided Flow Matching
Zifan Wang, Alice Harting, Matthieu Barreau +2
Guidance of generative models is typically achieved by modifying the probability flow vector field through the addition of a guidance field. In this paper, we instead propose the S…
Risk-Averse Certification of Bayesian Neural Networks
Xiyue Zhang, Zifan Wang, Yulong Gao +3
In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In t…
Risk-averse learning with delayed feedback
Siyi Wang, Zifan Wang, Karl Henrik Johansson +1
In real-world scenarios, risk-averse learning is valuable for mitigating potential adverse outcomes. However, the delayed feedback makes it challenging to assess and manage risk ef…
A Zeroth-Order Momentum Method for Risk-Averse Online Convex Games
Zifan Wang, Yi Shen, Zachary I. Bell +3
We consider risk-averse learning in repeated unknown games where the goal of the agents is to minimize their individual risk of incurring significantly high cost. Specifically, the…
Risk-Averse No-Regret Learning in Online Convex Games
Zifan Wang, Yi Shen, Michael M. Zavlanos
We consider an online stochastic game with risk-averse agents whose goal is to learn optimal decisions that minimize the risk of incurring significantly high costs. Specifically, w…