Exploration in reinforcement learning is crucial for human-like problem-solving strategies.
Future machine learning approaches may shift towards simpler, more efficient models.
Addressing out-of-distribution generalization challenges in large models requires innovative methods like Invariant Risk Minimization.
Deep dives
Importance of Exploration in Reinforcement Learning
Exploration in reinforcement learning is seen as fundamental due to humans' efficient problem-solving strategies based on uncertainty reduction. The interest in exploration is not only related to out-of-distribution generalization but also encompasses broader areas like uncertainty estimation, anomaly detection, and curriculum learning.
Innovation in Machine Learning Paradigm
The current paradigm of using overparameterized neural networks with SGD raises concern about wastage of resources. The future may necessitate simpler, more efficient approaches, potentially veering away from large models towards more grounded learning, such as algorithms learned from input-output pairs like in neural symbolic algorithms.
Challenges of Out-of-Distribution Generalization in Practical Settings
In tackling the out-of-distribution generalization problem, particularly in overparameterized models like ImageNet classifiers, methods like Invariant Risk Minimization (IRM) face limitations. The inability to differentiate between zero training error predictors and overparameterization in large models poses significant challenges that need to be addressed.
Evolution of Research Goals During PhD Journey
The PhD journey initially revolved around proving oneself in machine learning research, aiming for publications and future job prospects. However, as time progressed, the focus shifted towards a more relaxed and exploratory approach, prioritizing deeper understanding of problems over external validation and academic competitiveness.
Advice for New Researchers
New researchers are advised to prioritize working on problems that genuinely interest them, emphasizing understanding the problem itself over being swayed by tools or solutions readily available. By focusing on the intrinsic interest in the problem, researchers can cultivate a more enriching and fulfilling research experience.
Martin Arjovsky is a postdoctoral researcher at INRIA. His research focuses on generative modeling, generalization, and exploration in RL.
Martin's PhD thesis is titled "Out of Distribution Generalization in Machine Learning", which he completed in 2019 at New York University. We discuss his work on the influential Wasserstein GAN early in his PhD, then discuss his thesis work on out-of-distribution generalization which focused on causal invariance and invariant risk minimization.
Episode notes: https://cs.nyu.edu/~welleck/episode24.html
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