7 citations · 15 across the 10 of their papers we have counts for
11 papers
Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
Sang Bin Moon, Nicole Cho, Daniel Borrajo +2
Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal…
Variance Reduction for Stochastic Switching in Functionally Constrained Federated Optimization
Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi +1
Federated learning (FL) is increasingly used in safety-critical and fairness-sensitive applications, where learning objectives must be optimized subject to functional constraints.…
Optimization via First-Order Switching Methods: Skew-Symmetric Dynamics and Optimistic Discretization
Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi
Large-scale constrained optimization problems are at the core of many tasks in control, signal processing, and machine learning. Notably, problems with functional constraints arise…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Equitable Federated Learning with Activation Clustering
Antesh Upadhyay, Abolfazl Hashemi
Federated learning is a prominent distributed learning paradigm that incorporates collaboration among diverse clients, promotes data locality, and thus ensures privacy. These clien…
Submodular Maximization Approaches for Equitable Client Selection in Federated Learning
Andrés Catalino Castillo Jiménez, Ege C. Kaya, Lintao Ye +1
In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random…
Beyond Convexity: Proximal-Perturbed Lagrangian Methods for Efficient Functional Constrained Optimization
Sang Bin Moon, Jong Gwang Kim, Ashish Chandra +2
Non-convex functional constrained optimization problems have gained substantial attention in machine learning and data science, addressing broad requirements that typically go beyo…
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi +2
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a globa…
Localized Distributional Robustness in Submodular Multi-Task Subset Selection
Ege C. Kaya, Abolfazl Hashemi
In this work, we treat the problem of multi-task submodular optimization from the perspective of local distributional robustness within the neighborhood of a reference distribution…
Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations
Ege C. Kaya, Michael Hibbard, Takashi Tanaka +2
We study a pair of budget- and performance-constrained weak-submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which…
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi +1
A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication…