Google AI Experts to Discuss Randomization in Learning Algorithms at ICLR 2025

Google AI Researchers Lead Thought-Provoking Session on Randomization in AI

Today at 3:00 PM, Seijin Kobayashi and Johannes von Oswald, prominent researchers in artificial intelligence, will address a thought-provoking question: “Can learning algorithms discover when randomization is beneficial?”

The discussion will take place at the Google booth during the International Conference on Learning Representations (ICLR) 2025.

Google AI announced the event via a tweet, sparking interest among attendees and AI enthusiasts worldwide. The session promises to delve into the role of randomization in enhancing the efficiency and adaptability of learning algorithms, a topic that has gained considerable traction in recent years.

At 3PM today, Seijin Kobayashi & Johannes von Oswald will be at the #ICLR2025 Google booth to address the question, "Can learning algorithms discover when randomization is beneficial?" https://t.co/FLDQl8MVWo pic.twitter.com/SC2FNNg0zq

— Google AI (@GoogleAI) April 26, 2025

Exploring the Role of Randomization in AI

Randomization has long been a cornerstone of machine learning, enabling algorithms to generalize better and avoid overfitting.

Kobayashi and von Oswald are expected to shed light on how algorithms can autonomously determine when and how to apply randomization for optimal results. According to a Google AI spokesperson, “This discussion aims to push the boundaries of what we know about adaptive learning systems.”

The Significance of the ICLR Platform

ICLR, one of the most prestigious conferences in the field of AI, serves as a platform for groundbreaking research and innovative ideas.

The participation of experts like Kobayashi and von Oswald underscores the importance of this year’s theme, which focuses on advancing the theoretical and practical aspects of machine learning.

Anticipated Insights from the Session

The session is expected to cover various aspects of randomization, including its applications in neural networks, reinforcement learning, and meta-learning.

Attendees are eager to gain insights into how these concepts can be applied to real-world problems, from autonomous systems to predictive analytics.