2 citations · 4 across the 18 of their papers we have counts for
21 papers
ExactVariance of Random Return in Distributional LQR and Its Application to Mean-Variance Optimal Control
Ruyi Teng, Zifan Wang, Yulong Gao
The classical linear quadratic regulator (LQR) minimizes the expected cumulative return but fails to account for performance variability, rendering it inadequate for risk-aware app…
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…
SparScene: Efficient Traffic Scene Representation via Sparse Graph Learning for Large-Scale Trajectory Generation
Xiaoyu Mo, Jintian Ge, Zifan Wang +2
Multi-agent trajectory generation is a core problem for autonomous driving and intelligent transportation systems. However, efficiently modeling the dynamic interactions between nu…
Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
Zifan Wang, Georgios Pantazis, Sergio Grammatico +2
We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. I…
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…
First- and Zeroth-Order Learning in Asynchronous Games
Zifan Wang, Xinlei Yi, Michael M. Zavlanos +1
This paper investigates the discrete-time asynchronous games in which noncooperative agents seek to minimize their individual cost functions. Building on the assumption of partial…
Projected Gradient Descent for Constrained Decision-Dependent Optimization
Zifan Wang, Changxin Liu, Thomas Parisini +2
This paper considers the decision-dependent optimization problem, where the data distributions react in response to decisions affecting both the objective function and linear const…
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…
Risk-averse Learning with Non-Stationary Distributions
Siyi Wang, Zifan Wang, Xinlei Yi +3
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is fa…
Learning of Nash Equilibria in Risk-Averse Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper considers risk-averse learning in convex games involving multiple agents that aim to minimize their individual risk of incurring significantly high costs. Specifically,…
Policy Evaluation in Distributional LQR (Extended Version)
Zifan Wang, Yulong Gao, Siyi Wang +3
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random retu…
Constrained Optimization with Decision-Dependent Distributions
Zifan Wang, Changxin Liu, Thomas Parisini +2
In this paper we deal with stochastic optimization problems where the data distributions change in response to the decision variables. Traditionally, the study of optimization prob…
Convergence Analysis of the Best Response Algorithm for Time-Varying Games
Zifan Wang, Yi Shen, Michael M. Zavlanos +1
This paper studies a class of strongly monotone games involving non-cooperative agents that optimize their own time-varying cost functions. We assume that the agents can observe ot…
Asymmetric Feedback Learning in Online Convex Games
Zifan Wang, Xinlei Yi, Yi Shen +2
This paper considers convex games involving multiple agents that aim to minimize their own cost functions using locally available information. A common assumption in the study of s…
Policy Evaluation in Distributional LQR
Zifan Wang, Yulong Gao, Siyi Wang +3
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random retu…
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…