v199: Proceedings of CoLLAs 2022

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

Volume 199 presents the proceedings of "The 1st Conference on Lifelong Learning Agents," held from August 22-24, 2022, at McGill University in Montréal, Québec, Canada. This collection features research across various facets of lifelong and continual learning, addressing challenges such as catastrophic forgetting, non-stationarity, and generalization in dynamic environments. Papers explore diverse methodologies including Energy-Based Models, latent replay with Foundation Models, Elastic Federated Learning (EFL) on non-IID data, and Hamilton Equations for continual learning. Further contributions delve into practical tradeoffs in learned optimizers, anytime learning at macroscale, unsupervised optical flow estimation, and the application of meta-learning for improved generalization and feature discovery. The proceedings also cover critical aspects like privacy-preserving unlearning, safety rules for agents, open set recognition, and benchmarks for continual reinforcement learning, highlighting the breadth of research in developing adaptive and robust AI systems.

Key takeaway

The 1st Conference on Lifelong Learning Agents (CoLLA 2022) explores critical challenges in continuous AI adaptation, including catastrophic forgetting, non-stationary data, and resource constraints. Papers present novel techniques like Energy-Based Models, Latent Replay, Elastic Federated Learning (EFL), and Meta-Gradient Descent to enhance robust learning and generalization. This volume offers essential insights for AI/ML researchers and engineers developing adaptive systems for robotics, embedded devices, and dynamic real-world applications.

Topics

• Lifelong Learning
• Continual Learning
• Reinforcement Learning
• Federated Learning
• Meta-Learning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

Related on AIssential

• v232: Proceedings of CoLLAs 2023 — The 2nd Conference on Lifelong Learning Agents (CoLLA 2023), held August 22-25, 2023, at McGill University, Montréal, presented diverse research on AI systems capable of continuous learning.
• v274: Proceedings of CoLLAs 2024 — The conference proceedings highlight diverse strategies to enable AI models to continuously learn and adapt without forgetting.
• Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems — A dual-timescale FCL framework improves autonomous system adaptation by preventing immediate forgetting and recovering from long-term drift.
• Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments — Continual learning benchmarks must isolate online improvement from static model capabilities using expert-validated, latent-structure tasks.
• Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation — FedNL enables federated models to learn adaptive memory update rules, not just static weights, for robust test-time adaptation.
• Learning, Fast and Slow: Towards LLMs That Adapt Continually — Combining slow parameter updates with fast in-context learning improves LLM adaptation and reduces forgetting.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.