Fast Function to Function Regression. - Junier B. Oliva; Willie Neiswanger; Barnabás Póczos; Eric P. Xing; Hy Trac; Shirley Ho; Jeff G. Schneider (AISTATS, 2015)

Finding Galaxies in the Shadows of Quasars with Gaussian Processes. - Roman Garnett; Shirley Ho; Jeff G. Schneider (ICML, 2015)

Optimal Ridge Detection using Coverage Risk. - Yen-Chi Chen; Christopher R. Genovese; Shirley Ho; Larry A. Wasserman (NIPS, 2015)

Estimating Cosmological Parameters from the Dark Matter Distribution. - Siamak Ravanbakhsh; Junier B. Oliva; Sebastian Fromenteau; Layne Price; Shirley Ho; Jeff G. Schneider; Barnabás Póczos (ICML, 2016)

Analysis of Cosmic Microwave Background with Deep Learning. - Siyu He; Siamak Ravanbakhsh; Shirley Ho (ICLR (Workshop), 2018)

CosmoFlow: using deep learning to learn the universe at scale. - Amrita Mathuriya; Deborah Bard; Peter Mendygral; Lawrence Meadows; James Arnemann; Lei Shao; Siyu He; Tuomas Kärnä; Diana Moise; Simon J. Pennycook; Kristyn J. Maschhoff; Jason Sewall; et al. (SC, 2018)

Learning to Predict the Cosmological Structure Formation. - Siyu He; Yin Li 009; Yu Feng; Shirley Ho; Siamak Ravanbakhsh; Wei Chen; Barnabás Póczos (CoRR, 2018)

From Dark Matter to Galaxies with Convolutional Networks. - Xinyue Zhang; Yanfang Wang; Wei Zhang; Yueqiu Sun; Siyu He; Gabriella Contardo; Francisco Villaescusa-Navarro; Shirley Ho (CoRR, 2019)

HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks. - Juan Zamudio-Fernandez; Atakan Okan; Francisco Villaescusa-Navarro; Seda Bilaloglu; Asena Derin Cengiz; Siyu He; Laurence Perreault Levasseur; Shirley Ho (CoRR, 2019)

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. - Miles D. Cranmer; Richard Galvez; Lauren Anderson; David N. Spergel; Shirley Ho (CoRR, 2019)

Learning Symbolic Physics with Graph Networks. - Miles D. Cranmer; Rui Xu; Peter W. Battaglia; Shirley Ho (CoRR, 2019)

Learning neutrino effects in Cosmology with Convolutional Neural Networks. - Elena Giusarma; Mauricio Reyes Hurtado; Francisco Villaescusa-Navarro; Siyu He; Shirley Ho; ChangHoon Hahn (CoRR, 2019)

From Dark Matter to Galaxies with Convolutional Neural Networks. - Jacky H. T. Yip; Xinyue Zhang; Yanfang Wang; Wei Zhang; Yueqiu Sun; Gabriella Contardo; Francisco Villaescusa-Navarro; Siyu He; Shy Genel; Shirley Ho (CoRR, 2019)

Discovering Symbolic Models from Deep Learning with Inductive Biases. - Miles D. Cranmer; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Rui Xu; Kyle Cranmer; David N. Spergel; Shirley Ho (NeurIPS, 2020)

Lagrangian Neural Networks. - Miles D. Cranmer; Sam Greydanus; Stephan Hoyer; Peter W. Battaglia; David N. Spergel; Shirley Ho (CoRR, 2020)

Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. - Ademola Oladosu; Tony Xu; Philip Ekfeldt; Brian A. Kelly; Miles D. Cranmer; Shirley Ho; Adrian M. Price-Whelan; Gabriella Contardo (CoRR, 2020)

Anomaly Detection for Multivariate Time Series of Exotic Supernovae. - V. Ashley Villar; Miles D. Cranmer; Gabriella Contardo; Shirley Ho; Joshua Yao-Yu Lin (CoRR, 2020)

deep21: a Deep Learning Method for 21cm Foreground Removal. - T. Lucas Makinen; Lachlan Lancaster; Francisco Villaescusa-Navarro; Peter Melchior; Shirley Ho; Laurence Perreault Levasseur; David N. Spergel (CoRR, 2020)

Fast and Accurate Non-Linear Predictions of Universes with Deep Learning. - Renan Alves de Oliveira; Yin Li 009; Francisco Villaescusa-Navarro; Shirley Ho; David N. Spergel (CoRR, 2020)

Learning the Evolution of the Universe in N-body Simulations. - Chang Chen; Yin Li 009; Francisco Villaescusa-Navarro; Shirley Ho; Anthony Pullen (CoRR, 2020)

A Bayesian neural network predicts the dissolution of compact planetary systems. - Miles D. Cranmer; Daniel Tamayo; Hanno Rein; Peter W. Battaglia; Samuel Hadden; Philip J. Armitage; Shirley Ho; David N. Spergel (CoRR, 2021)

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. - V. Ashley Villar; Miles D. Cranmer; Edo Berger; Gabriella Contardo; Shirley Ho; Griffin Hosseinzadeh; Joshua Yao-Yu Lin (CoRR, 2021)

Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes. - Ji Won Park; V. Ashley Villar; Yin Li 009; Yan-Fei Jiang; Shirley Ho; Joshua Yao-Yu Lin; Philip J. Marshall; Aaron Roodman (CoRR, 2021)

Super-resolving Dark Matter Halos using Generative Deep Learning. - David Schaurecker; Yin Li 009; Jeremy Tinker; Shirley Ho; Alexandre Refregier (CoRR, 2021)

Learned Coarse Models for Efficient Turbulence Simulation. - Kimberly L. Stachenfeld; Drummond B. Fielding; Dmitrii Kochkov; Miles D. Cranmer; Tobias Pfaff; Jonathan Godwin; Can Cui; Shirley Ho; Peter W. Battaglia; Alvaro Sanchez-Gonzalez (CoRR, 2021)

Learned Simulators for Turbulence. - Kimberly L. Stachenfeld; Drummond Buschman Fielding; Dmitrii Kochkov; Miles D. Cranmer; Tobias Pfaff; Jonathan Godwin; Can Cui; Shirley Ho; Peter W. Battaglia; Alvaro Sanchez-Gonzalez (ICLR, 2022)

Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. - Digvijay Wadekar; Leander Thiele; Francisco Villaescusa-Navarro; J. Colin Hill; David N. Spergel; Miles D. Cranmer; Nicholas Battaglia; Daniel Anglés-Alcázar; Lars Hernquist; Shirley Ho (CoRR, 2022)

Simple lessons from complex learning: what a neural network model learns about cosmic structure formation. - Drew Jamieson; Yin Li 009; Siyu He; Francisco Villaescusa-Navarro; Shirley Ho; Renan Alves de Oliveira; David N. Spergel (CoRR, 2022)

Field Level Neural Network Emulator for Cosmological N-body Simulations. - Drew Jamieson; Yin Li 009; Renan Alves de Oliveira; Francisco Villaescusa-Navarro; Shirley Ho; David N. Spergel (CoRR, 2022)

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. - Digvijay Wadekar; Leander Thiele; J. Colin Hill; Shivam Pandey; Francisco Villaescusa-Navarro; David N. Spergel; Miles D. Cranmer; Daisuke Nagai; Daniel Anglés-Alcázar; Shirley Ho; Lars Hernquist (CoRR, 2022)

Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning. - Yan-Mong Chan; Natascha Manger; Yin Li 009; Chao-Chin Yang; Zhaohuan Zhu; Philip J. Armitage; Shirley Ho (CoRR, 2022)

Mangrove: Learning Galaxy Properties from Merger Trees. - Christian Kragh Jespersen; Miles D. Cranmer; Peter Melchior; Shirley Ho; Rachel S. Somerville; Austen Gabrielpillai (CoRR, 2022)

A Neural Network Subgrid Model of the Early Stages of Planet Formation. - Thomas Pfeil; Miles D. Cranmer; Shirley Ho; Philip J. Armitage; Tilman Birnstiel; Hubert Klahr (CoRR, 2022)

Learning Integrable Dynamics with Action-Angle Networks. - Ameya Daigavane; Arthur Kosmala; Miles D. Cranmer; Tess E. Smidt; Shirley Ho (CoRR, 2022)

Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. - Leander Thiele; Miles D. Cranmer; William R. Coulton; Shirley Ho; David N. Spergel (Mach. Learn. Sci. Technol., 2022)

Predicting the Initial Conditions of the Universe using Deep Learning. - Vaibhav Jindal; Drew Jamieson; Albert Liang; Aarti Singh; Shirley Ho (CoRR, 2023)

Learnable wavelet neural networks for cosmological inference. - Christian Pedersen; Michael Eickenberg; Shirley Ho (CoRR, 2023)

Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. - Christian Pedersen; Tiberiu Tesileanu; Tinghui Wu; Siavash Golkar; Miles D. Cranmer; Zijun Zhang; Shirley Ho (CoRR, 2023)

xVal: A Continuous Number Encoding for Large Language Models. - Siavash Golkar; Mariel Pettee; Michael Eickenberg; Alberto Bietti; Miles D. Cranmer; Géraud Krawezik; François Lanusse; Michael McCabe; Ruben Ohana; Liam Holden Parker; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Multiple Physics Pretraining for Physical Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (CoRR, 2023)

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. - François Lanusse; Liam Holden Parker; Siavash Golkar; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Géraud Krawezik; Michael McCabe; Ruben Ohana; Mariel Pettee; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering. - Pablo Lemos; Liam Holden Parker; ChangHoon Hahn; Shirley Ho; Michael Eickenberg; Jiamin Hou; Elena Massara; Chirag Modi 002; Azadeh Moradinezhad Dizgah; Bruno Régaldo-Saint Blancard; David N. Spergel (CoRR, 2023)

Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations. - Keiya Hirashima; Kana Moriwaki; Michiko S. Fujii; Yutaka Hirai; Takayuki R. Saitoh; Junichiro Makino; Shirley Ho (CoRR, 2023)

Scientific discovery in the age of artificial intelligence. - Hanchen Wang 002; Tianfan Fu; Yuanqi Du; Wenhao Gao 001; Kexin Huang; Ziming Liu; Payal Chandak; Shengchao Liu; Peter Van Katwyk; Andreea Deac; Anima Anandkumar; Karianne Bergen; et al. (Nat., 2023)

Robust simulation-based inference in cosmology with Bayesian neural networks. - Pablo Lemos; Miles D. Cranmer; Muntazir Abidi; ChangHoon Hahn; Michael Eickenberg; Elena Massara; David Yallup; Shirley Ho (Mach. Learn. Sci. Technol., 2023)

Rediscovering orbital mechanics with machine learning. - Pablo Lemos; Niall Jeffrey; Miles D. Cranmer; Shirley Ho; Peter W. Battaglia (Mach. Learn. Sci. Technol., 2023)

Point Clouds with Geometric and Semantic Confidence Intervals. - Victor Sim; Shirley Ho (ICPRAI (2), 2024)

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. - Eirini Angeloudi; Jeroen Audenaert; Micah Bowles; Benjamin M. Boyd; David Chemaly; Brian Cherinka; Ioana Ciuca; Miles D. Cranmer; Aaron Do; Matthew Grayling; Erin E. Hayes; Tom Hehir; et al. (NeurIPS, 2024)

Multiple Physics Pretraining for Spatiotemporal Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (NeurIPS, 2024)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. - Ruben Ohana; Michael McCabe; Lucas Meyer; Rudy Morel; Fruzsina J. Agocs; Miguel Beneitez; Marsha Berger; Blakesley Burkhart; Stuart B. Dalziel; Drummond B. Fielding; Daniel Fortunato; Jared A. Goldberg; et al. (NeurIPS, 2024)

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. - Siavash Golkar; Alberto Bietti; Mariel Pettee; Michael Eickenberg; Miles D. Cranmer; Keiya Hirashima; Géraud Krawezik; Nicholas Lourie; Michael McCabe; Rudy Morel; Ruben Ohana; Liam Holden Parker; et al. (CoRR, 2024)

Accelerating Giant Impact Simulations with Machine Learning. - Caleb Lammers; Miles D. Cranmer; Samuel Hadden; Shirley Ho; Norman Murray; Daniel Tamayo (CoRR, 2024)

ASURA-FDPS-ML: Star-by-star Galaxy Simulations Accelerated by Surrogate Modeling for Supernova Feedback. - Keiya Hirashima; Kana Moriwaki; Michiko S. Fujii; Yutaka Hirai; Takayuki R. Saitoh; Junnichiro Makino; Ulrich P. Steinwandel; Shirley Ho (CoRR, 2024)

Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation. - François Rozet; Ruben Ohana; Michael McCabe; Gilles Louppe; François Lanusse; Shirley Ho (CoRR, 2025)