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Meta Garbage Collection: Using OCaml's GC to GC Rust
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Intelligence arXiv
Provable diffusion-based posterior sampling for linear inverse problems via DDIM
Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantees or incur substantial computational overhead. We propose a simple and…
Research arXiv
Adaptive deep nonparametric regression from dependent data under covariate shift
Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions. In this case, the standard metric under the source distribution is not appropriate. This paper considers…
Research arXiv
Zero-Flow Two-Sample Tests
We propose a new approach to two-sample testing for deciding whether two sets of samples are drawn from the same distribution. The test is built on a statistical discrepancy based on the zero-flow criterion, termed zero-flow discrepancy (ZFD). We prove the…
Research arXiv
Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
This paper considers a multi-environment factor model in which high-dimensional covariates are collected from heterogeneous environments, with auxiliary labels available in a subset of these environments. The joint distribution of the covariates may vary…
Research arXiv
Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana
A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patterns. Anomalies were highly structured in space and time. Ashanti and Northern Regions accounted for most…
Research arXiv
Fundamental limits of distributed multiclass classification from simple binary decisions
We consider the problem of constructing a $K$-class classifier from the combination of $O(\log K)$ simple binary classifiers -- this is a natural paradigm to construct a sophisticated classifier in a distributed manner with each agent performing a relatively…
Research arXiv
Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference
We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential outcome framework. In the two-step method, vector search retrieves action-specific neighboring…