DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain. - Dianbo Liu; Fengjiao Peng; Ognjen (Oggi) Rudovic; Rosalind W. Picard (AffComp@IJCAI, 2017)

Balance of thrones: a network study on 'Game of Thrones'. - Dianbo Liu; Luca Albergante (CoRR, 2017)

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain. - Dianbo Liu; Fengjiao Peng; Andrew Shea; Ognjen Rudovic; Rosalind W. Picard (CoRR, 2017)

Genie: A Secure, Transparent Sharing and Services Platform for Genetic and Health Data. - Shifa Zhang; Anne Kim; Dianbo Liu; Sandeep C. Nuckchadyy; Lauren Huang; Aditya Masurkar; Jingwei Zhang; Lawrence Tseng; Pratheek Karnati; Laura Martínez; Thomas Hardjono; Manolis Kellis; et al. (CoRR, 2018)

FADL: Federated-Autonomous Deep Learning for Distributed Electronic Health Record. - Dianbo Liu; Timothy Miller 002; Raheel Sayeed; Kenneth D. Mandl (CoRR, 2018)

LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data. - Li Huang; Yifeng Yin; Zeng Fu; Shifa Zhang; Hao Deng; Dianbo Liu (CoRR, 2018)

Movies Network as the Indicator of Globalization. - Zilu Yu; Chen Chen; Dianbo Liu (CoRR, 2018)

Border Effect of Complex Network: An analysis on the cooperation network of movie stars across different regions. - Wen Zhou; Dong Zhang; Chen Chen; Dianbo Liu (CoRR, 2018)

Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden. - Dianbo Liu; Néstor Sepúlveda; Ming Zheng (CoRR, 2018)

High Performance Computing on Flat FHIR Files Created with the New SMART/HL7 Bulk Data Access Standard. - Dianbo Liu; Ricky Sahu; Vlad Ignatov; Daniel Gottlieb 001; Kenneth D. Mandl (AMIA, 2019)

Two-stage Federated Phenotyping and Patient Representation Learning. - Dianbo Liu; Dmitriy Dligach; Timothy Miller 002 (BioNLP@ACL, 2019)

Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records. - Li Huang; Dianbo Liu (CoRR, 2019)

Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence. - Dianbo Liu; Tim Miller 002; Kenneth D. Mandl (CoRR, 2019)

Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning. - Rulin Shao; Hui Liu; Dianbo Liu (CoRR, 2019)

Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving. - Rulin Shao; Hongyu He; Hui Liu; Dianbo Liu (CoRR, 2019)

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. - Li Huang; Andrew L. Shea; Huining Qian; Aditya Masurkar; Hao Deng; Dianbo Liu (J. Biomed. Informatics, 2019)

Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records. - Jianfei Cui; Dianbo Liu (CoRR, 2020)

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos. - Dianbo Liu; Tim Miller 002 (CoRR, 2020)

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models. - Dianbo Liu; Leonardo Clemente; Canelle Poirier; Xiyu Ding; Matteo Chinazzi; Jessica T. Davis; Alessandro Vespignani; Mauricio Santillana (CoRR, 2020)

FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network. - Dianbo Liu; He Zhu (CoRR, 2020)

Patient similarity: methods and applications. - Leyu Dai; He Zhu; Dianbo Liu (CoRR, 2020)

FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters. - Yuwei Cheng; Jiannan Zhu; Mengxin Jiang; Jie Fu 001; Changsong Pang; Peidong Wang; Kris Sankaran; Olawale Onabola; Yimin Liu; Dianbo Liu; Yoshua Bengio (ICCV, 2021)

hBERT + BiasCorp - Fighting Racism on the Web. - Olawale Onabola; Zhuang Ma; Yang Xie; Benjamin Akera; Abdulrahman Ibraheem; Jia Xue; Dianbo Liu; Yoshua Bengio (LT-EDI@EACL, 2021)

Discrete-Valued Neural Communication. - Dianbo Liu; Alex Lamb; Kenji Kawaguchi; Anirudh Goyal; Chen Sun 007; Michael C. Mozer; Yoshua Bengio (NeurIPS, 2021)

FakeSafe: Human Level Steganography Techniques by Disinformation Mapping Using Cycle-Consistent Adversarial Network. - He Zhu; Dianbo Liu (IEEE Access, 2021)

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records. - Tianyi Zhang; Shirui Zhang; Ziwei Chen; Dianbo Liu (CoRR, 2021)

FeARH: Federated machine learning with anonymous random hybridization on electronic medical records. - Jianfei Cui; He Zhu; Hao Deng; Ziwei Chen; Dianbo Liu (J. Biomed. Informatics, 2021)

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records. - Tianyi Zhang; Shirui Zhang; Ziwei Chen; Yoshua Bengio; Dianbo Liu (IEEE Big Data, 2022)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization. - Dianbo Liu; Alex Lamb; Xu Ji; Pascal Notsawo; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi (CoRR, 2022)

FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data. - Mike He Zhu; Léna Néhale Ezzine; Dianbo Liu; Yoshua Bengio (CoRR, 2022)

Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel. - Dianbo Liu; Vedant Shah; Oussama Boussif; Cristian Meo; Anirudh Goyal; Tianmin Shu; Michael Mozer; Nicolas Heess; Yoshua Bengio (CoRR, 2022)

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection. - Bonaventure F. P. Dossou; Dianbo Liu; Xu Ji; Moksh Jain; Almer M. van der Sloot; Roger Palou; Michael Tyers; Yoshua Bengio (CoRR, 2022)

Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data. - Dianbo Liu; Karmel W. Choi; Paulo Lizano; William Yuan; Kun-Hsing Yu; Jordan Smoller; Isaac S. Kohane (CoRR, 2022)

Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence. - Dianbo Liu; Kathe P. Fox; Griffin M. Weber; Tim Miller 002 (J. Biomed. Informatics, 2022)

Machine learning approaches to predicting no-shows in pediatric medical appointment. - Dianbo Liu; Won-Yong Shin; Eli Sprecher; Kathleen Conroy; Omar Santiago; Gal Wachtel; Mauricio Santillana (npj Digit. Medicine, 2022)

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization for Heterogeneous Representational Coarseness. - Dianbo Liu; Alex Lamb; Xu Ji; Pascal Tikeng Notsawo Jr.; Michael Mozer; Yoshua Bengio; Kenji Kawaguchi (AAAI, 2023)

Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. - Dianbo Liu; Vedant Shah; Oussama Boussif; Cristian Meo; Anirudh Goyal; Tianmin Shu; Michael Curtis Mozer; Nicolas Heess; Yoshua Bengio (ICLR, 2023)

GFlowOut: Dropout with Generative Flow Networks. - Dianbo Liu; Moksh Jain; Bonaventure F. P. Dossou; Qianli Shen; Salem Lahlou; Anirudh Goyal; Nikolay Malkin; Chris Chinenye Emezue; Dinghuai Zhang; Nadhir Hassen; Xu Ji; Kenji Kawaguchi; et al. (ICML, 2023)

Reusable Slotwise Mechanisms. - Bailey Trang Nguyen; Amin Mansouri; Kanika Madan; Khuong Nguyen; Kartik Ahuja; Dianbo Liu; Yoshua Bengio (NeurIPS, 2023)

Attention Schema in Neural Agents. - Dianbo Liu; Samuele Bolotta; He Zhu; Yoshua Bengio; Guillaume Dumas (CoRR, 2023)

Enhancing Human Capabilities through Symbiotic Artificial Intelligence with Shared Sensory Experiences. - Rui Hao; Dianbo Liu; Linmei Hu (CoRR, 2023)

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems. - Bailey Trang Nguyen; Alexander Tong 001; Kanika Madan; Yoshua Bengio; Dianbo Liu (CoRR, 2023)

Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries. - Zarif Ikram; Ling Pan; Dianbo Liu (CoRR, 2023)

Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents. - Hang Chen; Yuchuan Jang; Weijie Zhou; Cristian Meo; Ziwei Chen; Dianbo Liu (CoRR, 2023)

Unsupervised Concept Discovery Mitigates Spurious Correlations. - Md Rifat Arefin; Yan Zhang; Aristide Baratin; Francesco Locatello; Irina Rish; Dianbo Liu; Kenji Kawaguchi (ICML, 2024)

Improve Robustness of Eye Disease Detection by including Learnable Probabilistic Discrete Latent Variables into Machine Learning Models. - Anirudh Prabhakaran; YeKun Xiao; Ching-Yu Cheng; Dianbo Liu (CoRR, 2024)

Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos. - Tianyi Zhang; Yu Cao 001; Dianbo Liu (CoRR, 2024)

VQSynery: Robust Drug Synergy Prediction With Vector Quantization Mechanism. - Jiawei Wu; Mingyuan Yan; Dianbo Liu (CoRR, 2024)

Bifurcated Generative Flow Networks. - Chunhui Li; Cheng-Hao Liu; Dianbo Liu; Qingpeng Cai 001; Ling Pan (CoRR, 2024)

Verbalized Probabilistic Graphical Modeling with Large Language Models. - Hengguan Huang; Xing Shen 001; Songtao Wang; Dianbo Liu; Hao Wang 014 (CoRR, 2024)

Common and Rare Fundus Diseases Identification Using Vision-Language Foundation Model with Knowledge of Over 400 Diseases. - Meng Wang 038; Tian Lin; Aidi Lin; Kai Yu 009; Yuanyuan Peng; Lianyu Wang; Cheng Chen 013; Ke Zou; Huiyu Liang; Man Chen; Xue Yao; Meiqin Zhang; et al. (CoRR, 2024)

Balance of Number of Embedding and their Dimensions in Vector Quantization. - Hang Chen; Sankepally Sainath Reddy; Ziwei Chen; Dianbo Liu (CoRR, 2024)

Safety challenges of AI in medicine. - Xiaoye Wang; Nicole Xi Zhang; Hongyu He; Trang Nguyen; Kun-Hsing Yu; Hao Deng; Cynthia Brandt; Danielle S. Bitterman; Ling Pan; Ching-Yu Cheng; James Zou; Dianbo Liu (CoRR, 2024)

Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model. - Aidan Gilson; Xuguang Ai; Qianqian Xie; Sahana Srinivasan; Krithi Pushpanathan; Maxwell B. Singer; Jimin Huang; Hyunjae Kim; Erping Long; Peixing Wan; Luciano V. Del Priore; Lucila Ohno-Machado; et al. (CoRR, 2024)

Brain-inspired continual pre-trained learner via silent synaptic consolidation. - Xuming Ran; Juntao Yao; Yusong Wang; Mingkun Xu; Dianbo Liu (CoRR, 2024)

Masked Generative Priors Improve World Models Sequence Modelling Capabilities. - Cristian Meo; Mircea Lica; Zarif Ikram; Akihiro Nakano; Vedant Shah; Aniket Rajiv Didolkar; Dianbo Liu; Anirudh Goyal; Justin Dauwels (CoRR, 2024)

Gaussian Mixture Vector Quantization with Aggregated Categorical Posterior. - Mingyuan Yan; Jiawei Wu; Rushi Shah; Dianbo Liu (CoRR, 2024)

CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept. - YuXuan Wu; Bonaventure F. P. Dossou; Dianbo Liu (CoRR, 2024)

Improving Discrete Optimisation Via Decoupled Straight-Through Gumbel-Softmax. - Rushi Shah; Mingyuan Yan; Michael Curtis Mozer; Dianbo Liu (CoRR, 2024)

Representation Collapsing Problems in Vector Quantization. - Wenhao Zhao; Qiran Zou; Rushi Shah; Dianbo Liu (CoRR, 2024)

Physical Reasoning and Object Planning for Household Embodied Agents. - Ayush Agrawal; Raghav Prabhakar; Anirudh Goyal; Dianbo Liu (Trans. Mach. Learn. Res., 2024)

Gradient-guided discrete walk-jump sampling for biological sequence generation. - Zarif Ikram; Dianbo Liu; M. Saifur Rahman (Trans. Mach. Learn. Res., 2024)

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning. - Haoyue Sheng; Linrui Ma; Jean-Francois Samson; Dianbo Liu (BMC Medical Informatics Decis. Mak., 2024)

Flow Factorization for Efficient Generative Flow Networks. - Jiashun Liu; Chunhui Li; Cheng-Hao Liu; Dianbo Liu; Qingpeng Cai 001; Ling Pan (AAAI, 2025)

FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting. - He Zhu; Jun Bai; Na Li; Xiaoxiao Li; Dianbo Liu; David L. Buckeridge; Yue Li (npj Digit. Medicine, 2025)

Evolution guided generative flow networks. - Zarif Ikram; Ling Pan; Dianbo Liu (Trans. Mach. Learn. Res., 2025)

Can OpenAI o1 Reason Well in Ophthalmology? A 6,990-Question Head-to-Head Evaluation Study. - Sahana Srinivasan; Xuguang Ai; Minjie Zou; Ke Zou; Hyunjae Kim; Thaddaeus Wai Soon Lo; Krithi Pushpanathan; Yiming Kong; Anran Li; Maxwell B. Singer; Kai Jin; Fares Antaki; et al. (CoRR, 2025)

Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming. - Arash Lagzian; Srinivas Anumasa; Dianbo Liu (CoRR, 2025)

Auto-Bench: An Automated Benchmark for Scientific Discovery in LLMs. - Tingting Chen; Srinivas Anumasa; Beibei Lin; Vedant Shah; Anirudh Goyal; Dianbo Liu (CoRR, 2025)

Benchmarking Next-Generation Reasoning-Focused Large Language Models in Ophthalmology: A Head-to-Head Evaluation on 5,888 Items. - Minjie Zou; Sahana Srinivasan; Thaddaeus Wai Soon Lo; Ke Zou; Gabriel Dawei Yang; Xuguang Ai; Hyunjae Kim; Maxwell B. Singer; Fares Antaki; Kelvin Li; Robert Chang; Marcus Tan; et al. (CoRR, 2025)

A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers. - Meng Wang 038; Tian Lin; Qingshan Hou; Aidi Lin; Jingcheng Wang; Qingsheng Peng; Truong X. Nguyen; Danqi Fang; Ke Zou; Ting Xu; Cancan Xue; Ten Cheer Quek; et al. (CoRR, 2025)

JEDI: Latent End-to-end Diffusion Mitigates Agent-Human Performance Asymmetry in Model-Based Reinforcement Learning. - Jing Yu Lim; Zarif Ikram; Samson Yu 001; Haozhe Ma; Tze-Yun Leong; Dianbo Liu (CoRR, 2025)

BEnchmarking LLMs for Ophthalmology (BELO) for Ophthalmological Knowledge and Reasoning. - Sahana Srinivasan; Xuguang Ai; Thaddaeus Wai Soon Lo; Aidan Gilson; Minjie Zou; Ke Zou; Hyunjae Kim; Mingjia Yang; Krithi Pushpanathan; Samantha Min Er Yew; Wan Ting Loke; Jocelyn Hui Lin Goh; et al. (CoRR, 2025)