BAPS 2: enhanced possibilities for the analysis of genetic population structure. - Jukka Corander; Patrik Waldmann; Pekka Marttinen; Mikko J. Sillanpää (Bioinform., 2004)

Bayesian search of functionally divergent protein subgroups and their function specific residues. - Pekka Marttinen; Jukka Corander; Petri Törönen; Liisa Holm (Bioinform., 2006)

Bayesian Model Learning Based on Predictive Entropy. - Jukka Corander; Pekka Marttinen (J. Log. Lang. Inf., 2006)

Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. - Jukka Corander; Pekka Marttinen; Jukka Sirén; Jing Tang 002 (BMC Bioinform., 2008)

Bayesian modeling of recombination events in bacterial populations. - Pekka Marttinen; Adam Baldwin; William P. Hanage; Chris Dowson; Eshwar Mahenthiralingam; Jukka Corander (BMC Bioinform., 2008)

Bayesian clustering and feature selection for cancer tissue samples. - Pekka Marttinen; Samuel Myllykangas; Jukka Corander (BMC Bioinform., 2009)

Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function. - Petri Törönen; Pauli J. Ojala; Pekka Marttinen; Liisa Holm (BMC Bioinform., 2009)

Bayesian learning of graphical vector autoregressions with unequal lag-lengths. - Pekka Marttinen; Jukka Corander (Mach. Learn., 2009)

Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach. - Pekka Marttinen; Jing Tang 002; Bernard De Baets; Peter Dawyndt; Jukka Corander (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Efficient Bayesian approach for multilocus association mapping including gene-gene interactions. - Pekka Marttinen; Jukka Corander (BMC Bioinform., 2010)

Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects. - Jussi Gillberg; Pekka Marttinen; Matti Pirinen; Antti J. Kangas; Pasi Soininen; Marjo-Riitta Järvelin; Mika Ala-Korpela; Samuel Kaski (CoRR, 2013)

Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. - Pekka Marttinen; Matti Pirinen; Antti-Pekka Sarin; Jussi Gillberg; Johannes Kettunen; Ida Surakka; Antti J. Kangas; Pasi Soininen; Paul F. O'Reilly; Marika Kaakinen; Mika Kähönen; Terho Lehtimäki; et al. (Bioinform., 2014)

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. - Anna Cichonska; Juho Rousu; Pekka Marttinen; Antti J. Kangas; Pasi Soininen; Terho Lehtimäki; Olli T. Raitakari; Marjo-Riitta Järvelin; Veikko Salomaa; Mika Ala-Korpela; Samuli Ripatti; Matti Pirinen (Bioinform., 2016)

Multiple Output Regression with Latent Noise. - Jussi Gillberg; Pekka Marttinen; Matti Pirinen; Antti J. Kangas; Pasi Soininen; Mehreen Ali; Aki S. Havulinna; Marjo-Riitta Järvelin; Mika Ala-Korpela; Samuel Kaski (J. Mach. Learn. Res., 2016)

Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets. - Luana Micallef; Iiris Sundin; Pekka Marttinen; Muhammad Ammad-ud-din; Tomi Peltola; Marta Soare; Giulio Jacucci; Samuel Kaski (IUI, 2017)

biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements. - Matti Pirinen; Christian Benner; Pekka Marttinen; Marjo-Riitta Järvelin; Manuel A. Rivas; Samuli Ripatti (Bioinform., 2017)

Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation. - Iiris Sundin; Tomi Peltola; Muntasir Mamun Majumder; Pedram Daee; Marta Soare; Homayun Afrabandpey; Caroline Heckman; Samuel Kaski; Pekka Marttinen (CoRR, 2017)

Speciation trajectories in recombining bacterial species. - Pekka Marttinen; William P. Hanage (PLoS Comput. Biol., 2017)

Bacmeta: simulator for genomic evolution in bacterial metapopulations. - Aleksi Sipola; Pekka Marttinen; Jukka Corander (Bioinform., 2018)

Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. - Iiris Sundin; Tomi Peltola; Luana Micallef; Homayun Afrabandpey; Marta Soare; Muntasir Mamun Majumder; Pedram Daee; Chen He 003; Baris Serim; Aki S. Havulinna; Caroline Heckman; Giulio Jacucci; et al. (Bioinform., 2018)

A Bayesian model of acquisition and clearance of bacterial colonization. - Marko Järvenpää; Mohamad R. Abdul Sater; Georgia K. Lagoudas; Paul C. Blainey; Loren G. Miller; James A. McKinnell; Susan S. Huang; Yonatan H. Grad; Pekka Marttinen (CoRR, 2018)

ELFI: Engine for Likelihood-Free Inference. - Jarno Lintusaari; Henri Vuollekoski; Antti Kangasrääsiö; Kusti Skytén; Marko Järvenpää; Pekka Marttinen; Michael U. Gutmann; Aki Vehtari; Jukka Corander; Samuel Kaski (J. Mach. Learn. Res., 2018)

Predicting utilization of healthcare services from individual disease trajectories using RNNs with multi-headed attention. - Yogesh Kumar; Henri Salo; Tuomo Nieminen; Kristian Vepsalainen; Sangita Kulathinal; Pekka Marttinen (ML4H@NeurIPS, 2019)

Modelling G×E with historical weather information improves genomic prediction in new environments. - Jussi Gillberg; Pekka Marttinen; Hiroshi Mamitsuka; Samuel Kaski (Bioinform., 2019)

Recovering Pairwise Interactions Using Neural Networks. - Tianyu Cui; Pekka Marttinen; Samuel Kaski (CoRR, 2019)

Parallel Gaussian process surrogate method to accelerate likelihood-free inference. - Marko Järvenpää; Michael U. Gutmann; Aki Vehtari; Pekka Marttinen (CoRR, 2019)

Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. - Marko Järvenpää; Aki Vehtari; Pekka Marttinen (CoRR, 2019)

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation. - Marko Järvenpää; Mohamad R. Abdul Sater; Georgia K. Lagoudas; Paul C. Blainey; Loren G. Miller; James A. McKinnell; Susan S. Huang; Yonatan H. Grad; Pekka Marttinen (PLoS Comput. Biol., 2019)

Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text. - Shaoxiong Ji; Erik Cambria; Pekka Marttinen (ClinicalNLP@EMNLP, 2020)

Learning Global Pairwise Interactions with Bayesian Neural Networks. - Tianyu Cui; Pekka Marttinen; Samuel Kaski (ECAI, 2020)

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. - Marko Järvenpää; Aki Vehtari; Pekka Marttinen (UAI, 2020)

Informative Gaussian Scale Mixture Priors for Bayesian Neural Networks. - Tianyu Cui; Aki S. Havulinna; Pekka Marttinen; Samuel Kaski (CoRR, 2020)

Medical Code Assignment with Gated Convolution and Note-Code Interaction. - Shaoxiong Ji; Shirui Pan; Pekka Marttinen (ACL/IJCNLP (Findings), 2021)

A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. - Severi Rissanen; Pekka Marttinen (NeurIPS, 2021)

Multitask Recalibrated Aggregation Network for Medical Code Prediction. - Wei Sun 046; Shaoxiong Ji; Erik Cambria; Pekka Marttinen (ECML/PKDD (4), 2021)

Does the magic of BERT apply to medical code assignment? A quantitative study. - Shaoxiong Ji; Matti Hölttä; Pekka Marttinen (Comput. Biol. Medicine, 2021)

A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models. - Severi Rissanen; Pekka Marttinen (CoRR, 2021)

Medical SANSformers: Training self-supervised transformers without attention for Electronic Medical Records. - Yogesh Kumar; Alexander Ilin; Henri Salo; Sangita Kulathinal; Maarit K. Leinonen; Pekka Marttinen (CoRR, 2021)

Multi-task Balanced and Recalibrated Network for Medical Code Prediction. - Wei Sun 046; Shaoxiong Ji; Erik Cambria; Pekka Marttinen (CoRR, 2021)

Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories. - Guangyi Zhang 001; Reza A. Ashrafi; Anne Juuti; Kirsi Pietiläinen; Pekka Marttinen (IEEE J. Biomed. Health Informatics, 2021)

Deconfounded Representation Similarity for Comparison of Neural Networks. - Tianyu Cui; Yogesh Kumar; Pekka Marttinen; Samuel Kaski (NeurIPS, 2022)

Contextualized Graph Embeddings for Adverse Drug Event Detection. - Ya Gao 005; Shaoxiong Ji; Tongxuan Zhang; Prayag Tiwari; Pekka Marttinen (ECML/PKDD (2), 2022)

A Unified Review of Deep Learning for Automated Medical Coding. - Shaoxiong Ji; Wei Sun 046; Hang Dong 002; Honghan Wu; Pekka Marttinen (CoRR, 2022)

Look beyond labels: Incorporating functional summary information in Bayesian neural networks. - Vishnu Raj; Tianyu Cui; Markus Heinonen; Pekka Marttinen (CoRR, 2022)

Deformation equivariant cross-modality image synthesis with paired non-aligned training data. - Joel Honkamaa; Umair Khan; Sonja Koivukoski; Leena Latonen; Pekka Ruusuvuori; Pekka Marttinen (CoRR, 2022)

Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions. - Çaglar Hizli; S. T. John; Anne Juuti; Tuure Saarinen; Kirsi Pietiläinen; Pekka Marttinen (CoRR, 2022)

Deep learning for depression recognition with audiovisual cues: A review. - Lang He; Mingyue Niu; Prayag Tiwari; Pekka Marttinen; Rui Su; Jiewei Jiang; Chenguang Guo; Hongyu Wang 007; Songtao Ding; Zhongmin Wang 001; Xiaoying Pan; Wei Dang (Inf. Fusion, 2022)

COVIDNet: An Automatic Architecture for COVID-19 Detection With Deep Learning From Chest X-Ray Images. - Lang He; Prayag Tiwari; Rui Su; Xiuying Shi; Pekka Marttinen; Neeraj Kumar 001 (IEEE Internet Things J., 2022)

A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. - Shaoxiong Ji; Shirui Pan; Erik Cambria; Pekka Marttinen; Philip S. Yu (IEEE Trans. Neural Networks Learn. Syst., 2022)

Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. - Vishnu Raj; Tianyu Cui; Markus Heinonen; Pekka Marttinen (AISTATS, 2023)

Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning. - Shaoxiong Ji; Pekka Marttinen (EACL, 2023)

Reader: Model-based language-instructed reinforcement learning. - Nicola Dainese; Pekka Marttinen; Alexander Ilin (EMNLP, 2023)

Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. - Caglar Hizli; S. T. John; Anne Tuulikki Juuti; Tuure Tapani Saarinen; Kirsi Hannele Pietiläinen; Pekka Marttinen (ICML, 2023)

Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. - Arina Odnoblyudova; Caglar Hizli; St John; Andrea Cognolato; Anne Juuti; Simo Särkkä; Kirsi Pietiläinen; Pekka Marttinen (ML4H@NeurIPS, 2023)

Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series. - Onur Poyraz; Pekka Marttinen (ML4H@NeurIPS, 2023)

Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions. - Çaglar Hizli; St John; Anne Juuti; Tuure Saarinen; Kirsi Pietiläinen; Pekka Marttinen (NeurIPS, 2023)

Weak Supervision and Clustering-Based Sample Selection for Clinical Named Entity Recognition. - Wei Sun 046; Shaoxiong Ji; Tuulia Denti; Hans Moen; Oleg Kerro; Antti Rannikko; Pekka Marttinen; Miika Koskinen (ECML/PKDD (6), 2023)

ASymReg: Robust symmetric image registration using anti-symmetric formulation and deformation inversion layers. - Joel Honkamaa; Pekka Marttinen (CoRR, 2023)

A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease. - Manish Bhatia; Balram Meena; Vipin Kumar Rathi; Prayag Tiwari; Amit Kumar Jaiswal; Shagaf M. Ansari; Ajay Kumar; Pekka Marttinen (CoRR, 2023)

Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions. - Çaglar Hizli; S. T. John; Anne Juuti; Tuure Saarinen; Kirsi Pietiläinen; Pekka Marttinen (CoRR, 2023)

Content Reduction, Surprisal and Information Density Estimation for Long Documents. - Shaoxiong Ji; Wei Sun 046; Pekka Marttinen (CoRR, 2023)

Supporting Management of Gestational Diabetes with Comprehensive Self-Tracking: Mixed-Method Study of Wearable Sensors. - Mikko Kytö; Saila Koivusalo; Heli Tuomonen; Lisbeth Strömberg; Antti Ruonala; Pekka Marttinen; Seppo Heinonen; Giulio Jacucci (CoRR, 2023)

EEG Based Emotion Recognition: A Tutorial and Review. - Xiang Li 064; Yazhou Zhang; Prayag Tiwari; Dawei Song 001; Bin Hu 001; Meihong Yang; Zhigang Zhao; Neeraj Kumar 001; Pekka Marttinen (ACM Comput. Surv., 2023)

Multitask Balanced and Recalibrated Network for Medical Code Prediction. - Wei Sun 046; Shaoxiong Ji; Erik Cambria; Pekka Marttinen (ACM Trans. Intell. Syst. Technol., 2023)

Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality. - Tommi Gröhn; Sammeli Liikkanen; Teppo Huttunen; Mika Mäkinen; Pasi Liljeberg; Pekka Marttinen (ACM Trans. Comput. Heal., 2023)

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes. - Sophie Wharrie; Zhiyu Yang; Vishnu Raj; Remo Monti; Rahul Gupta; Ying Wang 074; Alicia Martin; Luke J. O'Connor; Samuel Kaski; Pekka Marttinen; Pier Francesco Palamara; Christoph Lippert; et al. (Bioinform., 2023)

Unsupervised feature selection based on variance-covariance subspace distance. - Saeed Karami; Farid Saberi-Movahed; Prayag Tiwari; Pekka Marttinen; Sahar Vahdati (Neural Networks, 2023)

Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization. - Fanni Ojala; Mohamad R. Abdul Sater; Loren G. Miller; James A. McKinnell; Mary K. Hayden; Susan S. Huang; Yonatan H. Grad; Pekka Marttinen (PLoS Comput. Biol., 2023)

Deformation equivariant cross-modality image synthesis with paired non-aligned training data. - Joel Honkamaa; Umair Khan; Sonja Koivukoski; Mira Valkonen; Leena Latonen; Pekka Ruusuvuori; Pekka Marttinen (Medical Image Anal., 2023)

In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery. - Matteo Merler; Katsiaryna Haitsiukevich; Nicola Dainese; Pekka Marttinen (ACL (Student Research Workshop), 2024)

Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection. - Ya Gao 005; Shaoxiong Ji; Pekka Marttinen (LREC/COLING, 2024)

Can docstring reformulation with an LLM improve code generation? - Nicola Dainese; Alexander Ilin; Pekka Marttinen (EACL (Student Research Workshop), 2024)

Improving Medical Multi-modal Contrastive Learning with Expert Annotations. - Yogesh Kumar; Pekka Marttinen (ECCV (20), 2024)

Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning. - Sam Spilsbury; Pekka Marttinen; Alexander Ilin (EMNLP, 2024)

VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. - Chen He 003; Vishnu Raj; Hans Moen; Tommi Gröhn; Chen Wang 145; Laura-Maria Peltonen; Saila Koivusalo; Pekka Marttinen; Giulio Jacucci (IUI, 2024)

Query-Guided Self-Supervised Summarization of Nursing Notes. - Ya Gao 005; Hans Moen; Saila Koivusalo; Miika Koskinen; Pekka Marttinen (ML4H@NeurIPS, 2024)

Diffusion Models as Probabilistic Neural Operators for Recovering Unobserved States of Dynamical Systems. - Katsiaryna Haitsiukevich; Onur Poyraz; Pekka Marttinen; Alexander Ilin (MLSP, 2024)

Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search. - Nicola Dainese; Matteo Merler; Minttu Alakuijala; Pekka Marttinen (NeurIPS, 2024)

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. - Alexander Nikitin; Jannik Kossen; Yarin Gal; Pekka Marttinen (NeurIPS, 2024)

Identifying latent state transition in non-linear dynamical systems. - Çaglar Hizli; Çagatay Yildiz; Matthias Bethge; St John; Pekka Marttinen (CoRR, 2024)

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model. - Hans Moen; Vishnu Raj; Andrius Vabalas; Markus Perola; Samuel Kaski; Andrea Ganna; Pekka Marttinen (CoRR, 2024)

Identifiable Causal Inference with Noisy Treatment and No Side Information. - Antti Pöllänen; Pekka Marttinen (Trans. Mach. Learn. Res., 2024)

Self-Supervised Forecasting in Electronic Health Records With Attention-Free Models. - Yogesh Kumar; Alexander Ilin; Henri Salo; Sangita Kulathinal; Maarit K. Leinonen; Pekka Marttinen (IEEE Trans. Artif. Intell., 2024)

A Unified Review of Deep Learning for Automated Medical Coding. - Shaoxiong Ji; Xiaobo Li 007; Wei Sun 046; Hang Dong 002; Ara Taalas; Yijia Zhang 001; Honghan Wu; Esa Pitkänen; Pekka Marttinen (ACM Comput. Surv., 2024)

Identifying latent state transitions in non-linear dynamical systems. - Çaglar Hizli; Çagatay Yildiz; Matthias Bethge; S. T. John; Pekka Marttinen (ICLR, 2025)

Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics. - Minttu Alakuijala; Reginald McLean; Isaac Woungang; Nariman Farsad; Samuel Kaski; Pekka Marttinen; Kai Yuan (Trans. Mach. Learn. Res., 2025)

Efficient Knowledge Injection in LLMs via Self-Distillation. - Kalle Kujanpää; Pekka Marttinen; Harri Valpola; Alexander Ilin (Trans. Mach. Learn. Res., 2025)

Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization. - Minttu Alakuijala; Ya Gao 005; Georgy Ananov; Samuel Kaski; Pekka Marttinen; Alexander Ilin; Harri Valpola (CoRR, 2025)

New multimodal similarity measure for image registration via modeling local functional dependence with linear combination of learned basis functions. - Joel Honkamaa; Pekka Marttinen (CoRR, 2025)

Recursive Decomposition with Dependencies for Generic Divide-and-Conquer Reasoning. - Sergio Hernández-Gutiérrez; Minttu Alakuijala; Alexander V. Nikitin; Pekka Marttinen (CoRR, 2025)

ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models. - Matteo Merler; Nicola Dainese; Minttu Alakuijala; Giovanni Bonetta; Pietro Ferrazzi; Yu Tian; Bernardo Magnini; Pekka Marttinen (CoRR, 2025)

Beyond the LUMIR challenge: The pathway to foundational registration models. - Junyu Chen 002; Shuwen Wei; Joel Honkamaa; Pekka Marttinen; Hang Zhang; Min Liu; Yichao Zhou; Zuopeng Tan; Zhuoyuan Wang; Yi Wang; Hongchao Zhou; Shunbo Hu; et al. (CoRR, 2025)

Object-level Self-Distillation for Vision Pretraining. - Çaglar Hizli; Çagatay Yildiz; Pekka Marttinen (CoRR, 2025)