Search Results for author: Layne C. Price

Found 7 papers, 3 papers with code

Variational Causal Inference

no code implementations13 Sep 2022 Yulun Wu, Layne C. Price, Zichen Wang, Vassilis N. Ioannidis, George Karypis

Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.

Causal Inference

Improving Generalizability of Protein Sequence Models via Data Augmentations

no code implementations1 Jan 2021 Hongyu Shen, Layne C. Price, Mohammad Taha Bahadori, Franziska Seeger

While protein sequence data is an emerging application domain for machine learning methods, small modifications to protein sequences can result in difficult-to-predict changes to the protein's function.

BIG-bench Machine Learning Contrastive Learning +2

Robust posterior inference when statistically emulating forward simulations

1 code implementation24 Apr 2020 Grigor Aslanyan, Richard Easther, Nathan Musoke, Layne C. Price

Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters.

Discovering Invariances in Healthcare Neural Networks

no code implementations8 Nov 2019 Mohammad Taha Bahadori, Layne C. Price

We also analyze the invariances of BioBERT on clinical notes and discover words that it is invariant to.

Time Series

Estimating Cosmological Parameters from the Dark Matter Distribution

no code implementations6 Nov 2017 Siamak Ravanbakhsh, Junier Oliva, Sebastien Fromenteau, Layne C. Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe.

The Spectrum of the Axion Dark Sector

1 code implementation10 Jun 2017 Matthew J. Stott, David J. E. Marsh, Chakrit Pongkitivanichkul, Layne C. Price, Bobby S. Acharya

We compute the background cosmological (quasi-)observables for models with a large number of axion fields, $n_{\rm ax}\sim \mathcal{O}(10-100)$, with the masses and decay constants drawn from statistical distributions.

Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology High Energy Physics - Theory

Cannot find the paper you are looking for? You can Submit a new open access paper.