93 citations · 578 across the 83 of their papers we have counts for
94 papers
The Localization Method for High-Dimensional Inequalities
Yunbum Kook, Santosh S. Vempala
We survey the localization method for proving inequalities in high dimension, pioneered by Lovász and Simonovits (1993), and its stochastic extension developed by Eldan (2012). The…
Why Language Models Hallucinate
Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala +1
Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty. Such…
Faster logconcave sampling from a cold start in high dimension
Yunbum Kook, Santosh S. Vempala
We present a faster algorithm to generate a warm start for sampling an arbitrary logconcave density specified by an evaluation oracle, leading to the first sub-cubic sampling algor…
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…