6 papers
Sample-Optimal Estimation of the Fréchet Inception Distance
Ziyun Chen, Jerry Li, Kevin Tian +1
The Fréchet Inception Distance (FID) is widely used to evaluate generative models, but its empirical plug-in estimator suffers from finite-sample bias [BSAG18, CF20]. We study the…
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…
The Tractability Landscape of Sampling with Inexact Scores
Anming Gu, Kevin Tian, Hubert Yang +1
We provide a simple and tight characterization of the types of inexact score oracle access that permit sampling with vanishing total variation bias, for a standard, well-behaved ta…
Functional Stochastic Localization
Anming Gu, Bobby Shi, Kevin Tian
Eldan's stochastic localization is a probabilistic construction that has proved instrumental to modern breakthroughs in high-dimensional geometry and the design of sampling algorit…
Separating Oblivious and Adaptive Models of Variable Selection
Ziyun Chen, Jerry Li, Kevin Tian +1
Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics. In this work, we investigate the statistical and computational landscape…
Perspectives on Stochastic Localization
Bobby Shi, Kevin Tian, Matthew S. Zhang
We survey different perspectives on the stochastic localization process of Eldan, a powerful construction that has had many exciting recent applications in high-dimensional probabi…