3 papers
cs.LG2026
A Theory of Generalization in Deep Learning
Elon Litman, Gabe Guo
We present a non-asymptotic theory of generalization in deep learning where the empirical neural tangent kernel partitions the output space. In directions corresponding to signal,…
cs.LG2025
Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding
Gabe Guo, Stefano Ermon
In arbitrary-order language models, it is an open question how to sample tokens in parallel from the correct joint distribution. With discrete diffusion models, the more tokens the…
cs.LG2024
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks
Judah Goldfeder, Quinten Roets, Gabe Guo +2
Inferring the exact parameters of a neural network with only query access is an NP-Hard problem, with few practical existing algorithms. Solutions would have major implications for…