What’s coming up at #IJCAI2024?

by Lucy Smith

31 July 2024

Keynote talks

There are six invited keynotes at this year’s conference. These will be delivered from 6-9 August.

Gillian Hadfield – Normative Infrastructure for AI Alignment
Thomas Dietterich – AI in Open Worlds: A Progress Report
Bo An – From Algorithmic and RL-based to LLM-powered Agents
Nisarg Shah – Democratic Foundations of Fair AI via Social Choice
David Blei – Beyond Roll Call: Inferring Politics from Text
Francesca Toni – Arguing with Machines: Bridging Explainable AI and Contestability

Panel discussions

There are two panel discussions planned. These will cover the following topics:

The importance of public engagement
Reviewing and large AI conference

Tutorials

There will be 31 tutorials this year, to be held on the first three days of the conference: 3-5 August.

T01: Deep Learning for Brain Encoding and Decoding: Principles, Practices and Beyond
T02: Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods
T03: Enhancing Graph Representation Learning through Subgraph Strategies
T04: Continual Learning on Graphs: Challenges, Solutions, and Opportunities
T05: Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLM
T06: All You Ever Need to Know About Counterfactual Explanations: Fundamentals, Methods, & User Studies for XAI
T07: Knowledge Editing for Large Language Models
T08: Beyond Human Creativity: A Tutorial on Advancements in AI Generated Content
T09: AI and Multi-Agent Techniques for Decentralised Energy Systems
T10: AI for Financial Markets – Agent based models applied to Bond Markets: Can we build a better market using ABM’s?
T11: Mechanism Design without Money: Facility Location Problems
T12: Machine Learning for Streaming Data
T13: Deep Variational Learning
T14: Visually-Rich Document Understanding and Intelligence: Recent Advances and Benchmarks
T15: Recommender Systems in the Era of Large Language Models (LLMs)
T16: Private Information Retrieval for Personalization Tutorial
T17: Machine ethics. A tutorial for prospective researchers
T18: Automated Reasoning for Social Choice Theory
T19: Tournaments in Computational Social Choice
T20: Generative AI for Education (GAIED): Advances, Opportunities, and Challenges
T21: Trustworthy Machine Learning under Imperfect Data
T22: Strategic ML: How to Learn With Data That ‘Behaves’
T23: AI Meets Values: History, Essence, and Recent Advances of Big Model’s Value Alignment
T24: Demystifying RL for Large Language Models: A training paradigm shift?
T25: Advances in Fairness-aware Reinforcement Learning: Theory and Applications
T26: Toward Mitigating Misinformation and Social Media Manipulation in Foundation Model Era
T27: A Copyright War: Authentication for Large Language Models
T28: Towards Causal Reinforcement Learning: Empowering Agents with Causality
T29: Safe Reinforcement Learning: Algorithms, Theory and Applications
T30: Probing Machine Learning Models in Angluin’s Style
T31: Curriculum Learning: Theories, Approaches, Applications, Tools, and Future Directions in the Era of Large Language Models