Software Framework for Data Fault Injection to Test Machine Learning Systems. - Jukka K. Nurminen; Tuomas Halvari; Juha Harviainen; Juha Mylläri; Antti Röyskö; Juuso Silvennoinen; Tommi Mikkonen (ISSRE Workshops, 2019)

Approximating the Permanent with Deep Rejection Sampling. - Juha Harviainen; Antti Röyskö; Mikko Koivisto (NeurIPS, 2021)

Trustworthy Monte Carlo. - Juha Harviainen; Mikko Koivisto; Petteri Kaski (NeurIPS, 2022)

A Faster Practical Approximation Scheme for the Permanent. - Juha Harviainen; Mikko Koivisto (AAAI, 2023)

Revisiting Bayesian network learning with small vertex cover. - Juha Harviainen; Mikko Koivisto (UAI, 2023)

On inference and learning with probabilistic generating circuits. - Juha Harviainen; Vaidyanathan Peruvemba Ramaswamy; Mikko Koivisto (UAI, 2023)

Estimating the Permanent by Nesting Importance Sampling. - Juha Harviainen; Mikko Koivisto (ICML, 2024)

Faster Perfect Sampling of Bayesian Network Structures. - Juha Harviainen; Mikko Koivisto (UAI, 2024)

Advances in Sampling and Counting Bipartite Matchings and Directed Acyclic Graphs. - Juha Harviainen (2024)

On Tractability of Learning Bayesian Networks with Ancestral Constraints. - Juha Harviainen; Pekka Parviainen (AISTATS, 2025)

Quantum Speedups for Bayesian Network Structure Learning. - Juha Harviainen; Kseniya Rychkova; Mikko Koivisto (UAI, 2025)

Optimal Decision Tree Pruning Revisited: Algorithms and Complexity. - Juha Harviainen; Frank Sommer; Manuel Sorge; Stefan Szeider (CoRR, 2025)