2 citations · 3 across the 7 of their papers we have counts for
10 papers
How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate
Jiawei Li, Zhiyang Xun, Lijie Chen +1
As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate oversight and supervision o…
Total Variation Distance Estimation in Autoregressive Models
Eric Price, Kevin Tian, Zhiyang Xun +1
Modern LLM deployments use a number of implementation choices and inference optimizations (e.g., batching, custom kernels, and quantization) on top of fixed weights, so two engines…
Query Lower Bounds for Diffusion Sampling
Zhiyang Xun, Eric Price
Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by minimizing the number of score e…
Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics
Zhiyang Xun, Shivam Gupta, Eric Price
Given a noisy linear measurement of a distribution , and a good approximation to the prior , when can we sample from the posterior ? Posterior…
Avoiding Obfuscation with Prover-Estimator Debate
Jonah Brown-Cohen, Geoffrey Irving, Georgios Piliouras +3
Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks. A promising approach to this pr…
Spectral Guarantees for Adversarial Streaming PCA
Eric Price, Zhiyang Xun
In streaming PCA, we see a stream of vectors and want to estimate the top eigenvector of their covariance matrix. This is easier if the spectral…
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar +2
Diffusion models are a remarkably effective way of learning and sampling from a distribution . In posterior sampling, one is also given a measurement model and…
On Pigeonhole Principles and Ramsey in TFNP
Siddhartha Jain, Jiawei Li, Robert Robere +1
We show that the TFNP problem RAMSEY is not black-box reducible to PIGEON, refuting a conjecture of Goldberg and Papadimitriou in the black-box setting. We prove this by giving red…
Improved Sample Complexity Bounds for Diffusion Model Training
Shivam Gupta, Aditya Parulekar, Eric Price +1
Diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability. From a theoretical standpo…
On Algorithms Based on Finitely Many Homomorphism Counts
Yijia Chen, Jörg Flum, Mingjun Liu +1
It is well known [Lovász, 67] that up to isomorphism a graph~ is determined by the homomorphism counts , i.e., the number of homomorphisms from to , where …