93 citations · 578 across the 83 of their papers we have counts for
4 papers · 1 filter
Sampling and Integration of Logconcave Functions by Algorithmic Diffusion
Yunbum Kook, Santosh S. Vempala
We study the complexity of sampling, rounding, and integrating arbitrary logconcave functions. Our new approach provides the first complexity improvements in nearly two decades for…
Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies
Max Dabagia, Daniel Mitropolsky, Christos H. Papadimitriou +1
How intelligence arises from the brain is a central problem in science. A crucial aspect of intelligence is dealing with uncertainty -- developing good predictions about one's envi…
Does GPT Really Get It? A Hierarchical Scale to Quantify Human vs AI's Understanding of Algorithms
Mirabel Reid, Santosh S. Vempala
As Large Language Models (LLMs) perform (and sometimes excel at) more and more complex cognitive tasks, a natural question is whether AI really understands. The study of understand…
In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
Yunbum Kook, Santosh S. Vempala, Matthew S. Zhang
We present a new random walk for uniformly sampling high-dimensional convex bodies. It achieves state-of-the-art runtime complexity with stronger guarantees on the output than prev…