NeurIPS

Ten research papers from FCAI accepted to the prestigious NeurIPS conference

Photo Matti Ahlgren / Aalto University

Photo Matti Ahlgren / Aalto University

Research conducted at the Finnish Centre for Artificial Intelligence (FCAI) is well presented at this year’s NeurIPS conference. In total, the prestigious conference has accepted ten submissions from either Aalto University or the University of Helsinki.

“Especially in European terms, we did well this year and the number of publications is within the top 10 of European academic institutions,” says Antti Honkela, Associate Professor of Computer Science at the University of Helsinki.

NeurIPS is the largest and most prestigious conference on machine learning, and it has become increasingly popular in the past years. In 2018, the main conference was sold out in under 12 minutes and therefore this year’s registration was based on a lottery.

“When I visited NeurIPS for the first time 15 years ago, there were 207 publications and less than a thousand participants. This year, the conference lasts for about the same time but there will be more than 1,400 presentations and possibly more than 12,000 participants,” says Honkela.

Over the past 32 years, the NeurIPS conference has been held at various locations around the world. This year’s conference will be held in Canada, at the Vancouver Convention Center.

Honkela and his research group wrote one of the accepted papers, which Honkela sees as a valuable acknowledgement for his group’s hard work. Researchers studied privacy in machine learning, and due to this work, they can assure that no one’s privacy will be violated by using a learning algorithm utilizing, for example, health data. Honkela and his colleagues developed a new version of Markov chain Monte Carlo, one of the most widely used Bayesian algorithms.

Their version of the algorithm assures privacy for a larger variety of models than any previously designed algorithm. “Therefore, this algorithm opens new, important possibilities for statistical inference that aims to secure privacy,” explains Honkela.

Accepted papers from FCAI (Aalto University and the University of Helsinki):

Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney (Aalto University) · Norman Di Palo (Italian Institute of Technology) · Mathias Berglund (Curious AI) · Alexander Ilin (Aalto University) · Juho Kannala (Aalto University) · Antti Rasmus (Curious AI) · Harri Valpola (Curious AI)

Improved Precision and Recall Metric for Assessing Generative Models
Tuomas Kynkäänniemi (Aalto University; NVIDIA) · Tero Karras (NVIDIA) · Samuli Laine (NVIDIA) · Jaakko Lehtinen (Aalto University; NVIDIA) · Timo Aila (NVIDIA)

Differentially Private Markov Chain Monte Carlo
Mikko Heikkilä (University of Helsinki) · Joonas Jälkö (Aalto University) · Onur Dikmen (Halmstad University) · Antti Honkela (University of Helsinki)

On Adversarial Mixup Resynthesis
Christopher Beckham (Mila, Polytechnique Montréal) · Sina Honari (Mila, Polytechnique Montréal) · Alex Lamb (Mila, University of Montreal) · Vikas Verma (Aalto University) · Farnoosh Ghadiri (Mila, Polytechnique Montréal) · R Devon Hjelm (Mila, University of Montreal; Microsoft Research) · Yoshua Bengio (Mila, University of Montreal) · Chris Pal (Mila, Element AI, Polytechnique Montréal)

High-Quality Self-Supervised Deep Image Denoising
Samuli Laine (NVIDIA) · Tero Karras (NVIDIA) · Jaakko Lehtinen (Aalto University; NVIDIA) · Timo Aila (NVIDIA)

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Wenzheng Chen (University of Toronto) · Huan Ling (University of Toronto; NVIDIA) · Jun Gao (University of Toronto) · Edward Smith (McGill University) · Jaakko Lehtinen (Aalto University; NVIDIA) · Alec Jacobson (University of Toronto) · Sanja Fidler (University of Toronto)

Machine Teaching of Active Sequential Learners
Tomi Peltola (Aalto University) · Mustafa Mert Çelikok (Aalto University) · Pedram Daee (Aalto University) · Samuel Kaski (Aalto University)

ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
Cagatay Yildiz (Aalto University) · Markus Heinonen (Aalto University) · Harri Lähdesmäki (Aalto University)

Identifying Causal Effects via Context-specific Independence Relations
Santtu Tikka (University of Jyväskylä) · Antti Hyttinen (University of Helsinki) · Juha Karvanen (University of Jyväskylä)

Variational Bayesian Decision-making for Continuous Utilities
Tomasz Kuśmierczyk (University of Helsinki) · Joseph Sakaya (University of Helsinki) · Arto Klami (University of Helsinki)

The full list of accepted papers is available on the NeurIPS website.