Develops scalable clustering methods for network-aware A/B testing in two-sided markets that cut spillover while boosting sample size and power, plus a theoretical bias correction.
hub
Title resolution pending
4 Pith papers cite this work, alongside 1,444 external citations. Polarity classification is still indexing.
4
Pith papers citing it
1,444
external citations · external index
hub tools
verdicts
UNVERDICTED 4representative citing papers
Latent diffusion models exhibit geometric decoupling where curvature in out-of-distribution generation is misallocated to unstable semantic boundaries instead of image details, identifying geometric hotspots as the structural cause of editing instability.
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
pArticleMap combines article embeddings, graph-based frontier extraction, and agentic LLMs to map nanomedicine literature and generate hypotheses, achieving 10.8% gold recovery and 61% future-neighborhood rate in retrospective benchmarks.
citing papers explorer
-
Scalable Network-Aware Experiment Design for Two-Sided Marketplaces
Develops scalable clustering methods for network-aware A/B testing in two-sided markets that cut spillover while boosting sample size and power, plus a theoretical bias correction.
-
Geometric Decoupling: Diagnosing the Structural Instability of Latent
Latent diffusion models exhibit geometric decoupling where curvature in out-of-distribution generation is misallocated to unstable semantic boundaries instead of image details, identifying geometric hotspots as the structural cause of editing instability.
-
On Diffusion Modeling for Anomaly Detection
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
-
Evidence-Grounded Frontier Mapping and Agentic Hypothesis Generation in Nanomedicine
pArticleMap combines article embeddings, graph-based frontier extraction, and agentic LLMs to map nanomedicine literature and generate hypotheses, achieving 10.8% gold recovery and 61% future-neighborhood rate in retrospective benchmarks.