5 papers
PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction
Yoshitaka Inoue, Minoh Jeong, Alfred Hero +2
Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug rep…
Probabilistic Variational Contrastive Learning
Minoh Jeong, Seonho Kim, Alfred Hero
Deterministic embeddings learned by contrastive learning (CL) methods such as SimCLR and SupCon achieve state-of-the-art performance but lack a principled mechanism for uncertainty…
Generalizing Supervised Contrastive learning: A Projection Perspective
Minoh Jeong, Alfred Hero
Self-supervised contrastive learning (SSCL) has emerged as a powerful paradigm for representation learning and has been studied from multiple perspectives, including mutual informa…
Anchors Aweigh! Sail for Optimal Unified Multi-Modal Representations
Minoh Jeong, Zae Myung Kim, Min Namgung +3
A unified representation space in multi-modal learning is essential for effectively integrating diverse data sources, such as text, images, and audio, to enhance efficiency and per…
Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis
Haonan Zhu, Andre R. Goncalves, Camilo Valdes +10
This paper proposes a hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a s…