LOTTERY: New Method for Two-Sample Testing with Imbalanced Data Sizes
Researchers have introduced LOTTERY, a machine learning framework for two-sample testing that leverages abundant reference data to compensate for very few query samples. The method learns reference-dependent representations that capture both global and local distributional structure, then adaptively weights them using an uncertainty-guided principle applied solely to reference data. This addresses a practical gap in statistical testing where conventional data-splitting approaches perform poorly under severe sample-size imbalance.
LOTTERY (Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry) is a new method accepted at ICML 2026 that tackles a common but underserved scenario in hypothesis testing: when one sample (the reference) is large and the other (the query) contains only a handful of observations. Traditional data-adaptive two-sample tests rely on splitting data to separate the learning and testing phases, which becomes problematic when query samples are scarce. LOTTERY sidesteps this by learning representations exclusively from the reference distribution, capturing salient structural features that can signal departures when a small query set arrives. The framework aggregates multiple representation families—covering both global and local distributional structure—and weights them adaptively using an uncertainty-guided criterion that requires no query data. Theoretically, the authors prove permutation-based type I error control and show that test power converges to one as sample sizes grow, provided at least one representation in the collection is consistent. Empirical evaluations across multiple benchmarks demonstrate strong detection performance while maintaining valid error control.
What's missing
The specific benchmarks used for empirical evaluation are not described in the abstract, making it difficult to assess generalizability.
What different sources said
- arXiv cs.LGCenter
LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
Related
Gut Bacteria Enzyme Found to Break Down Heat-Processed Food Compounds, Producing Novel Biogenic Amines
Researchers have discovered that an enzyme in common gut bacteria can degrade N-epsilon-carboxymethyllysine (CML), a compound formed during thermal food processing, producing previously unknown biogenic amines. The enzyme, ornithine decarboxylase SpeC from enterobacteria, acts on CML and related modified lysine derivatives through a low-level 'underground' catalytic activity. This finding suggests a previously unrecognized communication axis between thermally processed dietary compounds and gut microbial physiology, with potential implications for host health.
Full-Length Gene Sequencing Reveals Two Distinct Bacterial Communities in Black-Legged Ticks Expanding Into Canada
Researchers used Oxford Nanopore full-length 16S rRNA gene sequencing to characterize the microbiome of Ixodes scapularis black-legged ticks collected in Nova Scotia, Canada, distinguishing between tick-adapted bacteria and environmentally acquired bacteria. The study comes as I. scapularis — the primary vector of Lyme disease — is rapidly expanding northward into Canada due to climate change. The findings suggest that environmentally derived bacteria in tick microbiomes are not mere contamination, which has implications for how tick microbiome data is collected and interpreted across surveillance studies.
Study Identifies Metabolic Link Between Cell Envelope Stress and Biofilm Formation in Bacteria
Researchers have discovered that the metabolite acetyl-CoA directly inhibits enzymes that degrade the bacterial signaling molecule c-di-GMP, connecting cell envelope biosynthesis stress to biofilm formation in Pseudomonas aeruginosa. The study found that sub-inhibitory concentrations of antibiotics targeting early peptidoglycan biosynthesis — but not other antibiotic classes — elevate c-di-GMP levels by reducing phosphodiesterase activity, with acetyl-CoA competing for the enzyme active site. Because the relevant enzyme domain is broadly conserved across bacterial species, this checkpoint mechanism may be widespread and could have implications for understanding antibiotic-induced biofilm responses.