4 citations · 10 across the 18 of their papers we have counts for
9 papers · 1 filter
Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
Seo Hyun Kim, Sunwoo Hong, Younwoo Choi +3
Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a distinct credit-assignment chall…
Eliciting Weak-to-Strong Generalization with On-Policy Reverse Distillation
Youngrok Park, Sangmin Bae, Hojung Jung +6
Weak-to-strong generalization asks whether stronger models can learn from weaker supervisors and surpass them. This is particularly important for successive model generations and m…
PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization
Boryeong Cho, Sumyeong Ahn, Se-Young Yun
Direct Preference Optimization (DPO) simplifies alignment through pairwise comparisons but assumes all observed preferences are reliable. Real data often violates this assumption,…
Bastion: Budget-Aware Speculative Decoding with Tree-structured Block Diffusion Drafting
Soowon Oh, Nam Cao, Yujin Kim +4
Block-diffusion drafters have recently emerged as a powerful alternative for speculative decoding by predicting multiple future-token distributions in a single parallel step. Howev…
Temporal Alignment Guidance: On-Manifold Sampling in Diffusion Models
Youngrok Park, Hojung Jung, Sangmin Bae +1
Diffusion models have achieved remarkable success as generative models. However, even a well-trained model can accumulate errors throughout the generation process. These errors bec…
Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models
Yongjin Yang, Sihyeon Kim, Hojung Jung +4
Fine-tuning text-to-image diffusion models with human feedback is an effective method for aligning model behavior with human intentions. However, this alignment process often suffe…
Fine-tuned In-Context Learning Transformers are Excellent Tabular Data Classifiers
Felix den Breejen, Sangmin Bae, Stephen Cha +1
The recently introduced TabPFN pretrains an In-Context Learning (ICL) transformer on synthetic data to perform tabular data classification. In this work, we extend TabPFN to the fi…
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning
Felix den Breejen, Sangmin Bae, Stephen Cha +3
While interests in tabular deep learning has significantly grown, conventional tree-based models still outperform deep learning methods. To narrow this performance gap, we explore…
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Sumyeong Ahn, Sihyeon Kim, Jongwoo Ko +1
The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. To tackle this issue, researchers have explored methods for…