Tianyu Cui

InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference

Tianyu Cui, Song-Jun Xu, Artem Moskalev, Shuwei Li, Tommaso Mansi, Mangal Prakash*, Rui Liao*

ICLR: AI4NA, Oral, 2025; ICLR: MLGenX, Spotlight, 2025

We introduce InfoSEM, an unsupervised deep generative model for GRN inference that integrates gene embeddings from pretrained foundation models and known interactions as informative priors. We show that existing supervised methods exploit dataset biases rather than true biological mechanisms. We propose a biologically motivated benchmarking that better reflects real-world applications, where InfoSEM achieves state-of-the-art performance.

Generalist World Model Pre-Training for Efficient Reinforcement Learning

Yi Zhao, Aidan Scannell, Yuxin Hou, Tianyu Cui, Le Chen, Dieter Büchler, Arno Solin, Juho Kannala, Joni Pajarinen

ICLR: World Models, 2025

We propose a generalist world model pretraining (WPT) approach for robot learning using reward-free, non-expert multi-embodiment offline data. Combined with retrieval-based experience rehearsal and execution guidance, WPT enables efficient reinforcement learning and rapid task adaptation, outperforming learning-from-scratch baselines by over 35% across 72 visuomotor tasks.

Molecular Property Prediction using Pretrained-BERT and Bayesian Active Learning: A Data-Efficient Approach to Drug Design

Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui

Journal of Cheminformatics, 2025; ICLR: GEM, 2025

We propose a semi-supervised Bayesian active learning framework for molecular property prediction, leveraging representations from a pretrained MolBERT model on 1.26 million compounds instead of relying solely on limited labeled data. This semi-supervised approach improves prediction accuracy and acquisition efficiency compared to classical supervised active learning.

Incorporating Functional Summary Information in Bayesian Neural Networks Using a Dirichlet Process Likelihood

Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen

AISTATS, 2023

We introduce DP-BNN, a novel strategy that integrates prior knowledge of task difficulty and class imbalance into deep learning with a Dirichlet process on the predicted probabilities. DP-BNN improves in accuracy, uncertainty calibration, and robustness against corruptions on both balanced and imbalanced image and text datasets with negligible computational overhead.

Deconfounded Representation Similarity for Comparison of Neural Networks

Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski

NeurIPS, Oral, 2022

Representation similarity between neural networks can be confounded by population structure, leading to misleading conclusions, such as spuriously high similarity in random neural networks. We propose covariate adjustment regression to remove this confounder, enhancing functional similarity detection while preserving invariance properties.

Informative Bayesian Neural Network Priors for Weak Signals

Tianyu Cui, Aki Havulinna, Pekka Marttinen, Samuel Kaski

Bayesian Analysis, Oral at Joint Statistical Meetings, 2022

We introduce a novel framework that incorporates domain knowledge on feature sparsity and data signal-to-noise ratio into the Gaussian scale mixture priors of neural network weights. This informative prior enhances prediction accuracy in datasets with weak and sparse signals, such as those in genetics, even surpassing computationally intensive cross-validation for hyperparameter tuning.

Gene-gene Interaction Detection with Deep Learning

Tianyu Cui, Khaoula El Mekkaoui, Jaakko Reinvall, Aki S. Havulinna, Pekka Marttinen, Samuel Kaski

Nature Communications Biology, 2022

We propose a biologically motivated neural network architecture to model and detect gene interactions from GWAS datasets, along with a novel permutation procedure to assess the uncertainty of these interactions. The proposed framework identified nine interactions in a cholesterol study using the UK Biobank, which were successfully replicated in an independent FINRISK dataset.

Learning Global Pairwise Interactions with Bayesian Neural Networks

Tianyu Cui, Pekka Marttinen*, Samuel Kaski*

ECAI, Oral, 2020; NeurIPS: Bayesian Deep Learning, 2019

In this paper, we propose using Bayesian neural networks to capture global interactions effects with well-calibrated uncertainty between features or latent representations to enhance interpretability.