When applied to a new data distribution, machine learning algorithms have been shown to deteriorate. Distribution shifts are caused by spurious correlations that hold at training time but not at test time, changes to the domain, as well as under- and over-representation with respect to future data. ...

Agnieszka Słowik is a Postdoctoral Researcher working on human-centric, inclusive and responsible AI at Microsoft Research Cambridge. Prior to joining Microsoft, she did her PhD in out-of-distribution generalisation in machine learning at University of Cambridge, with particular focus on learning ...

Analysing the customer journey in an e-commerce website is essential for understanding the user's experience and identifying areas for improvement. The customer journey refers to the path a user takes from initial engagement with a website or product to final purchase; placing an ad, personalised ...

Konrad Banachewicz is a Staff Data Scientist at Adevinta and a former Data Scientist at eBay. During his 20 years of industry experience he has worked with a variety of institutions on a wide range of data analysis problems. He holds a PhD in statistics from Vrije Universiteit Amsterdam where he ...

The notion of uncertainty has recently drawn increasing attention in machine learning research due to the field's burgeoning relevance for practical applications, many of which have safety requirements. This talk will address questions regarding the representation and adequate handling of ...

Eyke Hüllermeier is a full professor at the Institute of Informatics at LMU Munich, Germany, where he heads the Chair of Artificial Intelligence and Machine Learning. He studied mathematics and business computing, received his PhD in computer science from Paderborn University in 1997, and a ...

Synthetic data in machine learning is attracting increased interest, as it frequently offers a faster and cheaper alternative to traditional data collection and annotation in applications ranging from autonomous vehicles to healthcare. It is typically used to supplement real data and often requires ...

Sebastian is an ELLIS/IMPRS-IS PhD student at the University of Tübingen. Prior to returning to academia, he conducted research in industry, where he worked as a scientist in Microsoft's Mixed Reality and AI Lab, and an AI Resident at Microsoft Research Cambridge. In a previous life, he spent ...

Machine learning algorithms work by extracting patterns from large amounts of training data and projecting correlations forward. This mechanism by which ML works becomes a problem when training data is scarce: In the low data regime, performance usually drops dramatically. Data augmentation (DA), ...

Pola got her PhD in 2022 from the Technical University of Denmark doing research on probabilistic machine learning, invariance learning and AI ethics. She has recently joined AWS in Berlin as an Applied Scientist where she works on responsible AI.

What do we learn by fooling GPT?

"GPT can be extremely impressive, but we turn that impression around with a few carefully crafted prompts. The wrong answers are given with an almost human-like confidence. We will discuss the various mistakes of GPT models in relation to their training processes."

Award-winning computer scientist and data analyst. One of the founding members of deepsense.ai. He specializes in applying data structures and algorithms in artificial intelligence.

Frontiers of Continual Learning

Continual Learning is an emerging paradigm in machine learning where models learn new tasks in sequence without access to previously acquired knowledge. The key challenge in this field is addressing the problem of catastrophic forgetting of previous information. This is crucial because in many ...

Sebastian is a postdoctoral researcher at IDEAS NCBR and also an assistant professor at the Gdańsk University of Technology, where he earned his PhD. Previously, he was employed as an Applied Scientist at Amazon and contributed to projects such as the visual perception system for the autonomous ...

Exploring the potential of GPT models in education: lessons learned and future directions

Models from GPT family offer state of the art performance on many tasks and are accessible through simple API. This makes them an interesting technology to experiment with and build products around. We believe that one of the more interesting areas to use GPT models is education, as they make it ...

Agata works as Senior AI Scientist in Pearson. She has couple of years of experience in Artificial Intelligence/Machine Learning. Her professional interests are connected with Natural Language Processing. She obtained her Master degree in Applied Mathematics from Wroclaw University of Science and ...

The problem of label noise is critically important in supervised learning - after all, when the training data is corrupted, it may be hard to recover the correct training signal. A simple solution, i.e. more thorough data annotation, is unfortunately rarely possible in practice, as machine ...

Senior Research Engineer in the Machine Learning Research team at Allegro, where he works on applying and advancing NLP methods in the e-commerce domain. He obtained his PhD from the University of Warsaw, in which he focused on machine learning methods for histopathology.