We are pleased to announce the eighth London Information Retrieval Meetup, a free evening meetup aimed to Information Retrieval passionates and professionals who are curious to explore and discuss the latest trends in the field.
This time we go fully remote, given the COVID-19 situation and the impossibility of hosting the event live.
The evening will be structured with 2 technical talks, with Q&A session after each talk.
Explainability for learning to Rank
In the last few years, Artificial Intelligence applications have become more and more sophisticated and often operate like algorithmic “black boxes” for decision-making. Due to this fact, some questions naturally arise when working with these models: why should we trust a certain decision taken by these algorithms? Why and how was this prediction made? Which variables mostly influenced the prediction? The most crucial challenge with complex machine learning models is therefore their interpretability and explainability. This talk aims to illustrate an overview of the most popular explainability techniques and their application in Learning to Rank. In particular, we will examine in depth a powerful library called SHAP with both theoretical and practical insights; we will talk about its amazing tools to give an explanation of the model behaviour, especially how each feature impacts the model’s output, and we will explain to you how to interpret the results in a Learning to Rank scenario.
Anna Ruggero is a software engineer passionate about Information Retrieval and Data Mining.
She loves to find new solutions to problems, suggesting and testing new ideas, especially those that concern the integration of machine learning techniques into information retrieval systems.
Anna came into contact with search engines during her studies falling in love with this world, therefore she decided to investigate this topic further participating to the 12th European Summer School in Information Retrieval and doing her master degree dissertation on Entity Search.
Thanks to this path, she has expanded and improved her knowledges of Java and Python languages, information retrieval systems, clustering and word embeddings.
Ilaria is a Data Scientist passionate about the world of Artificial Intelligence. She got a Master in Data Science, strongly believing in the power of Big Data and Digital Transformation. Thanks to the practical application on Flight Delay Prediction developed during her thesis work, she implemented several Data Mining and Machine Learning techniques and became familiar with the programming language R. She is also involved in a Research Project, deepening her knowledge about Ensemble Learning, with a specific focus on the Super Learner algorithm.
The Split: Introduction and Discussion about Apache Lucene and Solr as Separate Top-Level Projects
Jan HøydahlApache Solr PMC Chair
Jan is an Apache Lucene/Solr committer, PMC member and Apache member, and has more than 24 years of professional experience within IT and Telecom. He was FAST’s 2nd professional service consultant in 2000. Jan runs the consulting company Cominvent AS, delivering consulting services and training within mission critical and large scale search.
Throughout his career, Jan had deep experience in training, software development, IT architecture, technical sales support, senior consulting, entrepreneur, CTO and manager.
Michael SokolovApache Lucene PMC Chair
Sokolov is a Software Engineer with a diverse background, currently at Amazon building its new product search service using Lucene. He has built many search-based applications for the reference and academic publishing industry such as Oxford English Dictionary, DeGruyter Online, and O’Reilly Safari Books, using a wide variety of search engine technologies, and is a contributor to the Apache Lucene project.
Join our Group
Researchers, scientists, and other practitioners in the field of Information Retrieval, Machine Learning, and Data Science… join us, and let’s create a group of passionate and professionals!