Zobrazeno 1 - 10
of 12 094
pro vyhledávání: '"WASSERMAN P"'
We address the null paradox in epidemic models, where standard methods estimate a non-zero treatment effect despite the true effect being zero. This occurs when epidemic models mis-specify how causal effects propagate over time, especially when covar
Externí odkaz:
http://arxiv.org/abs/2410.11743
Data integration has become increasingly common in aligning multiple heterogeneous datasets. With high-dimensional outcomes, data integration methods aim to extract low-dimensional embeddings of observations to remove unwanted variations, such as bat
Externí odkaz:
http://arxiv.org/abs/2410.04996
Retrieval augmented generation (RAG) has become the standard in long context question answering (QA) systems. However, typical implementations of RAG rely on a rather naive retrieval mechanism, in which texts whose embeddings are most similar to that
Externí odkaz:
http://arxiv.org/abs/2410.03754
Autor:
Wasserman, Max, Mateos, Gonzalo
Large-scale latent variable models require expressive continuous distributions that support efficient sampling and low-variance differentiation, achievable through the reparameterization trick. The Kumaraswamy (KS) distribution is both expressive and
Externí odkaz:
http://arxiv.org/abs/2410.00660
Autor:
Chang, Woo Je, Green, Allison M., Sakotic, Zarko, Wasserman, Daniel, Truskett, Thomas M., Milliron, Delia J.
Based on experimental and simulation methods we helped develop, we are advancing mechanistic understanding of how self-assembled NC metamaterials can produce distinctive near- and far-field optical properties not readily achievable in lithographicall
Externí odkaz:
http://arxiv.org/abs/2409.15573
Growing observational evidence suggests that enhanced mass loss from the progenitors of core-collapse supernovae (SNe) is common during $\sim1$ yr preceding the explosion, creating an optically thick circum-stellar medium (CSM) shell at $\sim10^{14.5
Externí odkaz:
http://arxiv.org/abs/2409.14233
Autor:
Zhu, Huanbiao, Desai, Krish, Kuusela, Mikael, Mikuni, Vinicius, Nachman, Benjamin, Wasserman, Larry
In many experimental contexts, it is necessary to statistically remove the impact of instrumental effects in order to physically interpret measurements. This task has been extensively studied in particle physics, where the deconvolution task is calle
Externí odkaz:
http://arxiv.org/abs/2409.10421
Searches of new signals in particle physics are usually done by training a supervised classifier to separate a signal model from the known Standard Model physics (also called the background model). However, even when the signal model is correct, syst
Externí odkaz:
http://arxiv.org/abs/2409.06399
Autor:
Wasserman, Max, Mateos, Gonzalo
Graphs serve as generic tools to encode the underlying relational structure of data. Often this graph is not given, and so the task of inferring it from nodal observations becomes important. Traditional approaches formulate a convex inverse problem w
Externí odkaz:
http://arxiv.org/abs/2406.14786
Image-to-fMRI encoding is important for both neuroscience research and practical applications. However, such "Brain-Encoders" have been typically trained per-subject and per fMRI-dataset, thus restricted to very limited training data. In this paper w
Externí odkaz:
http://arxiv.org/abs/2406.12179