44 citations · 50 across the 12 of their papers we have counts for
13 papers
PerMix-RLVR: Preserving Persona Expressivity under Verifiable-Reward Alignment
Jihwan Oh, Soowon Oh, Murad Aghazada +3
Persona prompting has been widely adopted to steer large language models (LLMs) behavior and improve their instruction performance by assigning specific characters. However, identi…
Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
Minchan Jeong, J. Jon Ryu, Se-Young Yun +1
The Koopman operator provides a principled framework for analyzing nonlinear dynamical systems through linear operator theory. Recent advances in dynamic mode decomposition (DMD) h…
From Belief Entrenchment to Robust Reasoning in LLM Agents
Jihwan Oh, Minchan Jeong, Jongwoo Ko +1
Multi-Agent Debate (MAD) has emerged as a promising inference scaling method for Large Language Model (LLM) reasoning. However, it frequently suffers from belief entrenchment, wher…
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
Yunseon Choi, Sangmin Bae, Seonghyun Ban +6
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning reg…
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Gihun Lee, Minchan Jeong, Yujin Kim +4
While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents furthe…
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
Seongyoon Kim, Minchan Jeong, Sungnyun Kim +3
Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with h…
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning
Haeju Lee, Minchan Jeong, Se-Young Yun +1
Prompt tuning, in which prompts are optimized to adapt large-scale pre-trained language models to downstream tasks instead of fine-tuning the full model parameters, has been shown…
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Gihun Lee, Minchan Jeong, Sangmook Kim +2
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers…
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning
Jihwan Oh, Joonkee Kim, Minchan Jeong +1
The multi-agent setting is intricate and unpredictable since the behaviors of multiple agents influence one another. To address this environmental uncertainty, distributional reinf…
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective
Jongwoo Ko, Seungjoon Park, Minchan Jeong +4
Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs). Among various KD approaches, Intermediate…
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Gihun Lee, Minchan Jeong, Yongjin Shin +2
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models. Although this precludes the need to access clients' data dir…
Robust Streaming PCA
Daniel Bienstock, Minchan Jeong, Apurv Shukla +1
We consider streaming principal component analysis when the stochastic data generating model is subject to perturbations. While existing models assume a fixed covariance, we adopt…
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
Jung-hun Kim, Se-Young Yun, Minchan Jeong +3
We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. To address the challenges posed by this noise, we analy…