ΑΙhub.org
 

ICML 2020 Test of Time award


by
13 July 2020



share this:

ICML
The International Conference on Machine Learning (ICML) Test of Time award is given to a paper from ICML ten years ago that has had significant impact. This year the award goes to Niranjan Srinivas, Andreas Krause, Sham Kakade and Matthias Seeger for their work “Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design“.

The award was announced by the conference chairs on 1 July:

On their award page the ICML Test of Time Award committee members explain the significance of the paper:

This paper brought together the fields of Bayesian optimization, bandits and experimental design by analyzing Gaussian process bandit optimization, giving a novel approach to derive finite-sample regret bounds in terms of a mutual information gain quantity. This paper has had profound impact over the past ten years, including the method itself, the proof techniques used, and the practical results. These have all enriched our community by sparking creativity in myriad subsequent works, ranging from theory to practice.

The authors react to the good news:

In a special award session the authors gave a plenary talk describing their work. To summarise their presentation they took a brief look back over the ten years following publication of their paper and noted that the community have been working on a number of exciting related topics during that time. These areas include:
Theory of Bayesian optimisation and kernalized bandits – exploring other acquisition functions and high dimensions, developing fast algorithms, and establishing lower bounds.
Variants of Bayesian optimisation and kernalized bandits – a lot of this research is motivated by various practical applications. For example, in experimental design settings you might want to schedule experiments to happen in parallel or in batches, you might want to trade-off multiple objectives or take constraints into account (motivated by safety and robustness considerations).
More general models – analysis tools similar to the one reported in this paper have found exciting applications. These include neural bandits and neural tangent kernel, Thompson sampling and reinforcement learning.

In addition to these theoretical developments, there has been a lot of exciting work on applications of GP-UCB and Bayesian optimisation more broadly. Bayesian optimisation is now used extensively in industry for problems related to automatic machine learning, robotics, recommender systems, environmental monitoring, protein design, and much more.

Read the winning paper

The abstract on arXiv.
The full paper as pdf.



tags: ,


Lucy Smith is Senior Managing Editor for AIhub.
Lucy Smith is Senior Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



Forthcoming machine learning and AI seminars: April 2025 edition

  01 Apr 2025
A list of free-to-attend AI-related seminars that are scheduled to take place between 1 April and 31 May 2025.

AI can be a powerful tool for scientists. But it can also fuel research misconduct

  31 Mar 2025
While AI is allowing scientists to make technological breakthroughs, there’s also a darker side to the use of AI in science: scientific misconduct is on the rise.
monthly digest

AIhub monthly digest: March 2025 – human-allied AI, differential privacy, and social media microtargeting

  28 Mar 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

AI ring tracks spelled words in American Sign Language

  27 Mar 2025
In its current form, SpellRing could be used to enter text into computers or smartphones via fingerspelling.

How AI images are ‘flattening’ Indigenous cultures – creating a new form of tech colonialism

  26 Mar 2025
AI-generated stock images that claim to depict “Indigenous Australians”, don’t resemble Aboriginal and Torres Strait Islander peoples.

Interview with Lea Demelius: Researching differential privacy

  25 Mar 2025
We hear from doctoral consortium participant Lea Demelius who is investigating the trade-offs and synergies that arise between various requirements for trustworthy AI.

The Machine Ethics podcast: Careful technology with Rachel Coldicutt

This episode, Ben chats to Rachel Coldicutt about AI taxonomy, innovating for everyone not just the few, responsibilities of researchers, and more.

Interview with AAAI Fellow Roberto Navigli: multilingual natural language processing

  21 Mar 2025
Roberto tells us about his career path, some big research projects he’s led, and why it’s important to follow your passion.




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association