54 citations · 271 across the 20 of their papers we have counts for
4 papers · 1 filter
Fisher information and the geometry of memorization in neural networks
Daniel T. Bernstein, Chase Goddard, Luca Di Carlo +2
A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from idiosyncratic or even corrupte…
Stochastic Resetting Accelerates Reinforcement Learning Beyond Random Search
Jello Zhou, David J. Schwab, Vudtiwat Ngampruetikorn
Stochastic resetting -- intermittently returning a process to a fixed reference state -- has emerged as an effective mechanism for optimizing first-passage properties. Existing the…
When can in-context learning generalize out of task distribution?
Chase Goddard, Lindsay M. Smith, Vudtiwat Ngampruetikorn +1
In-context learning (ICL) is a remarkable capability of pretrained transformers that allows models to generalize to unseen tasks after seeing only a few examples. We investigate em…
Perturbation Theory for the Information Bottleneck
Vudtiwat Ngampruetikorn, David J. Schwab
Extracting relevant information from data is crucial for all forms of learning. The information bottleneck (IB) method formalizes this, offering a mathematically precise and concep…