76 citations · 308 across the 72 of their papers we have counts for
23 papers · 1 filter
Conformal Risk Minimization for Semi-Supervised Domain Adaptation via Optimal Transport
Manos Giannopoulos, Yi Shen, Michael M. Zavlanos
In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, creating a distribution shift th…
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…
FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport
Pengxi Liu, Yi Shen, Matthew M. Engelhard +4
Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and…
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration
Andreas Kontogiannis, Konstantinos Papathanasiou, Yi Shen +3
Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MAR…
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping
Guangyi Liu, Suzan Iloglu, Michael Caldara +2
In Amazon robotic warehouses, the destination-to-chute mapping problem is crucial for efficient package sorting. Often, however, this problem is complicated by uncertain and dynami…
Transfer Reinforcement Learning in Heterogeneous Action Spaces using Subgoal Mapping
Kavinayan P. Sivakumar, Yan Zhang, Zachary Bell +2
In this paper, we consider a transfer reinforcement learning problem involving agents with different action spaces. Specifically, for any new unseen task, the goal is to use a succ…
Inverse Reinforcement Learning from Non-Stationary Learning Agents
Kavinayan P. Sivakumar, Yi Shen, Zachary Bell +3
In this paper, we study an inverse reinforcement learning problem that involves learning the reward function of a learning agent using trajectory data collected while this agent is…
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos +4
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clust…
Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
Hans Riess, Manolis Veveakis, Michael M. Zavlanos
The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths. On the other ha…
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
Yi Shen, Pan Xu, Michael M. Zavlanos
Off-policy evaluation and learning are concerned with assessing a given policy and learning an optimal policy from offline data without direct interaction with the environment. Oft…
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health
Yi Shen, Jessilyn Dunn, Michael M. Zavlanos
In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizi…
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…
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
Chenyu Liu, Yan Zhang, Yi Shen +1
In this paper, we consider a transfer Reinforcement Learning (RL) problem in continuous state and action spaces, under unobserved contextual information. For example, the context c…
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback
Yan Zhang, Yi Zhou, Kaiyi Ji +1
Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the unknown gradient of the objective function. Nevertheless, two-point feedback can not be used f…
Cooperative Multi-Agent Reinforcement Learning with Partial Observations
Yan Zhang, Michael M. Zavlanos
In this paper, we propose a distributed zeroth-order policy optimization method for Multi-Agent Reinforcement Learning (MARL). Existing MARL algorithms often assume that every agen…
Transfer Reinforcement Learning under Unobserved Contextual Information
Yan Zhang, Michael M. Zavlanos
In this paper, we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context. Specifically, we consider a dem…
VarNet: Variational Neural Networks for the Solution of Partial Differential Equations
Reza Khodayi-Mehr, Michael M. Zavlanos
In this paper we propose a new model-based unsupervised learning method, called VarNet, for the solution of partial differential equations (PDEs) using deep neural networks (NNs).…
Distributed off-Policy Actor-Critic Reinforcement Learning with Policy Consensus
Yan Zhang, Michael M. Zavlanos
In this paper, we propose a distributed off-policy actor critic method to solve multi-agent reinforcement learning problems. Specifically, we assume that all agents keep local esti…