7 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…
Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3
Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although influential methods such as…
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1
This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints. We propose a novel first-order Softmax-Weighted Switching Gradient m…
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…
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…
Optimistic Regret Bounds for Online Learning in Adversarial Markov Decision Processes
Sang Bin Moon, Abolfazl Hashemi
The Adversarial Markov Decision Process (AMDP) is a learning framework that deals with unknown and varying tasks in decision-making applications like robotics and recommendation sy…