38 citations · 76 across the 7 of their papers we have counts for
8 papers
Learning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting
Siyu Zhou, Jialin Ouyang, Fan Xia +8
Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monitoring methods typically contr…
Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark
Xu Yao, Siyuan Zhou, Zhenbo Wu +6
Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, la…
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
Yuanye Liu, Siyuan Zhou, Ke Zhang +3
Pre-trained Vision Transformers (ViTs) are increasingly deployed for medical image classification. However, correcting their inevitable failure cases in dynamic clinical scenarios…
Adaptive auditing of AI systems with anytime-valid guarantees
Siyu Zhou, Patrick Vossler, Venkatesh Sivaraman +2
A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation. Consequently, adaptive testing paradigms have gai…
Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest
Siyu Zhou, Lucas Mentch
Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists. D…
Getting Better from Worse: Augmented Bagging and a Cautionary Tale of Variable Importance
Lucas Mentch, Siyu Zhou
As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predicti…
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Lucas Mentch, Siyu Zhou
Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and clas…
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
Giles Hooker, Lucas Mentch, Siyu Zhou
This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable importance measures propose…