2.3k citations · 2.3k across the 9 of their papers we have counts for
9 papers
Reinforcement Learning Towards Broadly and Persistently Beneficial Models
Akshay V. Jagadeesh, Rahul K. Arora, Khaled Saab +5
As AI systems are deployed across increasingly diverse and high-stakes settings, model alignment must generalize beyond the tasks and domains seen during training. This is especial…
Lifting uniform learners via distributional decomposition
Guy Blanc, Jane Lange, Ali Malik +1
We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distr…
Popular decision tree algorithms are provably noise tolerant
Guy Blanc, Jane Lange, Ali Malik +1
Using the framework of boosting, we prove that all impurity-based decision tree learning algorithms, including the classic ID3, C4.5, and CART, are highly noise tolerant. Our guara…
On the power of adaptivity in statistical adversaries
Guy Blanc, Jane Lange, Ali Malik +1
We study a fundamental question concerning adversarial noise models in statistical problems where the algorithm receives i.i.d. draws from a distribution . The definit…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Calibrated Model-Based Deep Reinforcement Learning
Ali Malik, Volodymyr Kuleshov, Jiaming Song +3
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from mod…