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dblp: Miles D. Cranmer
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2020 – today
- 2025
- [j6]Ho Fung Tsoi
, Dylan S. Rankin, Cecile Caillol, Miles D. Cranmer, Sridhara Dasu, Javier M. Duarte, Philip C. Harris, Elliot Lipeles, Vladimir Loncar:
SymbolFit: Automatic Parametric Modeling with Symbolic Regression. Comput. Softw. Big Sci. 9(1): 12 (2025) - [j5]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava:
SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Trans. Evol. Comput. 29(4): 1127-1137 (2025) - [j4]Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An Thai Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles D. Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffmann:
Machine Learning with Physics Knowledge for Prediction: A Survey. Trans. Mach. Learn. Res. 2025 (2025) - [i44]Guilherme Seidyo Imai Aldeia, Hengzhe Zhang, Geoffrey F. Bomarito, Miles D. Cranmer, Alcides Fonseca, Bogdan Burlacu, William G. La Cava, Fabrício Olivetti de França:
Call for Action: towards the next generation of symbolic regression benchmark. CoRR abs/2505.03977 (2025) - [i43]Licong Xu, Milind Sarkar, Anto I. Lonappan, Íñigo Zubeldia, Pablo Villanueva-Domingo, Santiago Casas, Christian Fidler, Chetana Amancharla, Ujjwal Tiwari, Adrian E. Bayer, Chadi Ait Ekioui, Miles D. Cranmer, Adrian Dimitrov, James Fergusson, Kahaan Gandhi, Sven Krippendorf, Andrew Laverick, Julien Lesgourgues, Antony Lewis, Thomas Meier, Blake Sherwin, Kristen Surrao, Francisco Villaescusa-Navarro, Chi Wang, Xueqing Xu, Boris Bolliet:
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery. CoRR abs/2507.07257 (2025) - [i42]Payel Mukhopadhyay, Michael McCabe, Ruben Ohana, Miles D. Cranmer:
Controllable Patching for Compute-Adaptive Surrogate Modeling of Partial Differential Equations. CoRR abs/2507.09264 (2025) - [i41]Jeff Shen, François Lanusse, Liam Holden Parker, Ollie Liu, Tom Hehir, Leopoldo Sarra, Lucas Meyer, Micah Bowles, Sebastian Wagner-Carena, Helen Qu, Siavash Golkar, Alberto Bietti, Hatim Bourfoune, Nathan Cassereau, Pierre Cornette, Keiya Hirashima, Géraud Krawezik, Ruben Ohana, Nicholas Lourie, Michael McCabe, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho:
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning. CoRR abs/2510.17959 (2025) - [i40]Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz:
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms. CoRR abs/2510.23166 (2025) - [i39]Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo, Adrian E. Bayer, Aidan Acquah, Chetana Amancharla, Almog Barzilay-Siegal, Pablo Bermejo, Camille L. Bilodeau, Pablo Cárdenas Ramírez, Miles D. Cranmer, Urbano França, ChangHoon Hahn, Yan-Fei Jiang, Raúl Jiménez, Jun-Young Lee, Antonio Lerario, Osman Mamun, Thomas Meier, Anupam A. Ojha, Pavlos Protopapas, Shimanto Roy, David N. Spergel, Pedro Tarancón-Álvarez, Ujjwal Tiwari, Matteo Viel, Digvijay Wadekar, Chi Wang, Bonny Y. Wang, Licong Xu, Yossi Yovel, Shuwen Yue, Wen-Han Zhou, Qiyao Zhu, Jiajun Zou, Íñigo Zubeldia:
The Denario project: Deep knowledge AI agents for scientific discovery. CoRR abs/2510.26887 (2025) - [i38]Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu, Lucas Meyer, Kaze W. K. Wong, Hadi Sotoudeh, Alberto Bietti, Irina Espejo, Rio Fear, Siavash Golkar, Tom Hehir, Keiya Hirashima, Géraud Krawezik, François Lanusse, Rudy Morel, Ruben Ohana, Liam Holden Parker, Mariel Pettee, Jeff Shen, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho:
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics. CoRR abs/2511.15684 (2025) - [i37]Rudy Morel, Francesco Pio Ramunno, Jeff Shen, Alberto Bietti, Kyunghyun Cho, Miles D. Cranmer, Siavash Golkar, Olexandr Gugnin, Géraud Krawezik, Tanya Marwah, Michael McCabe, Lucas Meyer, Payel Mukhopadhyay, Ruben Ohana, Liam Holden Parker, Helen Qu, François Rozet, K. D. Leka, François Lanusse, David Fouhey, Shirley Ho:
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme. CoRR abs/2511.19390 (2025) - [i36]Rio Alexa Fear, Payel Mukhopadhyay, Michael McCabe, Alberto Bietti, Miles D. Cranmer:
Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model. CoRR abs/2511.20798 (2025) - 2024
- [c7]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, Shirley Ho, Marc Huertas-Company, Kartheik Iyer, Maja Jablonska, François Lanusse, Henry Leung, Kaisey Mandel, Rafael Martínez-Galarza, Peter Melchior, Lucas Meyer, Liam Holden Parker, Helen Qu, Jeff Shen, Michael J. Smith, Connor Stone, Mike Walmsley, John F. Wu:
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. NeurIPS 2024 - [c6]Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles D. Cranmer, Swarat Chaudhuri:
Symbolic Regression with a Learned Concept Library. NeurIPS 2024 - [c5]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, Kyunghyun Cho, Shirley Ho:
Multiple Physics Pretraining for Spatiotemporal Surrogate Models. NeurIPS 2024 - [c4]Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho:
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. NeurIPS 2024 - [i35]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, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Shirley Ho:
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. CoRR abs/2406.02585 (2024) - [i34]Caleb Lammers, Miles D. Cranmer, Samuel Hadden, Shirley Ho, Norman Murray, Daniel Tamayo:
Accelerating Giant Impact Simulations with Machine Learning. CoRR abs/2408.08873 (2024) - [i33]Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An T. Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles D. Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffmann:
Machine Learning with Physics Knowledge for Prediction: A Survey. CoRR abs/2408.09840 (2024) - [i32]Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles D. Cranmer, Swarat Chaudhuri:
Symbolic Regression with a Learned Concept Library. CoRR abs/2409.09359 (2024) - [i31]Ho Fung Tsoi, Dylan S. Rankin, Cecile Caillol, Miles D. Cranmer, Sridhara Dasu, Javier M. Duarte, Philip C. Harris, Elliot Lipeles, Vladimir Loncar:
SymbolFit: Automatic Parametric Modeling with Symbolic Regression. CoRR abs/2411.09851 (2024) - [i30]Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho:
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. CoRR abs/2412.00568 (2024) - 2023
- [j3]Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho:
Robust simulation-based inference in cosmology with Bayesian neural networks. Mach. Learn. Sci. Technol. 4(1): 01 (2023) - [j2]Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia:
Rediscovering orbital mechanics with machine learning. Mach. Learn. Sci. Technol. 4(4): 45002 (2023) - [i29]Fabrício Olivetti de França, Marco Virgolin, Michael Kommenda, Maimuna S. Majumder, Miles D. Cranmer, Guilherme Espada, Leon Ingelse, Alcides Fonseca, Mikel Landajuela, Brenden K. Petersen, Ruben Glatt, T. Nathan Mundhenk, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava:
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR abs/2304.01117 (2023) - [i28]Miles D. Cranmer:
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. CoRR abs/2305.01582 (2023) - [i27]Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles D. Cranmer, Sridhara Dasu, Peter Elmer, Philip C. Harris, Isobel Ojalvo, Maurizio Pierini:
Symbolic Regression on FPGAs for Fast Machine Learning Inference. CoRR abs/2305.04099 (2023) - [i26]Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer, Zijun Zhang, Shirley Ho:
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. CoRR abs/2309.16645 (2023) - [i25]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, Kyunghyun Cho, Shirley Ho:
xVal: A Continuous Number Encoding for Large Language Models. CoRR abs/2310.02989 (2023) - [i24]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, Kyunghyun Cho, Shirley Ho:
Multiple Physics Pretraining for Physical Surrogate Models. CoRR abs/2310.02994 (2023) - [i23]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, Kyunghyun Cho, Shirley Ho:
AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR abs/2310.03024 (2023) - 2022
- [j1]Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel:
Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. Mach. Learn. Sci. Technol. 3(3): 35002 (2022) - [c3]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:
Learned Simulators for Turbulence. ICLR 2022 - [i22]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:
Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. CoRR abs/2201.01305 (2022) - [i21]Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia:
Rediscovering orbital mechanics with machine learning. CoRR abs/2202.02306 (2022) - [i20]Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel:
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks. CoRR abs/2203.00026 (2022) - [i19]Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho:
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks. CoRR abs/2207.08435 (2022) - [i18]Kaze W. K. Wong, Miles D. Cranmer:
Automated discovery of interpretable gravitational-wave population models. CoRR abs/2207.12409 (2022) - [i17]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:
The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. CoRR abs/2209.02075 (2022) - [i16]Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai:
Mangrove: Learning Galaxy Properties from Merger Trees. CoRR abs/2210.13473 (2022) - [i15]Thomas Pfeil, Miles D. Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr:
A Neural Network Subgrid Model of the Early Stages of Planet Formation. CoRR abs/2211.04160 (2022) - [i14]Ji Won Park, Simon Birrer, Madison Ueland, Miles D. Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman:
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks. CoRR abs/2211.07807 (2022) - [i13]David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré:
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. CoRR abs/2211.09008 (2022) - [i12]Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho:
Learning Integrable Dynamics with Action-Angle Networks. CoRR abs/2211.15338 (2022) - 2021
- [i11]Miles D. Cranmer, Daniel Tamayo, Hanno Rein, Peter W. Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel:
A Bayesian neural network predicts the dissolution of compact planetary systems. CoRR abs/2101.04117 (2021) - [i10]V. Ashley Villar, Miles D. Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin:
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. CoRR abs/2103.12102 (2021) - [i9]Miles D. Cranmer, Peter Melchior, Brian Nord:
Unsupervised Resource Allocation with Graph Neural Networks. CoRR abs/2106.09761 (2021) - [i8]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:
Learned Coarse Models for Efficient Turbulence Simulation. CoRR abs/2112.15275 (2021) - 2020
- [c2]Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho:
Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS 2020 - [c1]Miles D. Cranmer, Peter Melchior, Brian Nord:
Unsupervised Resource Allocation with Graph Neural Networks. Preregister@NeurIPS 2020: 272-284 - [i7]Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho:
Lagrangian Neural Networks. CoRR abs/2003.04630 (2020) - [i6]Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho:
Discovering Symbolic Models from Deep Learning with Inductive Biases. CoRR abs/2006.11287 (2020) - [i5]Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles D. Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo:
Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. CoRR abs/2007.04459 (2020) - [i4]V. Ashley Villar, Miles D. Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin:
Anomaly Detection for Multivariate Time Series of Exotic Supernovae. CoRR abs/2010.11194 (2020)
2010 – 2019
- 2019
- [i3]Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho:
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. CoRR abs/1908.08045 (2019) - [i2]Miles D. Cranmer, Rui Xu, Peter W. Battaglia, Shirley Ho:
Learning Symbolic Physics with Graph Networks. CoRR abs/1909.05862 (2019) - 2017
- [i1]Miles D. Cranmer, Benjamin R. Barsdell, Danny C. Price, Jayce Dowell, Hugh Garsden, Veronica Dike, Tarraneh Eftekhari, Alexander M. Hegedus, Joseph Malins, Kenneth S. Obenberger, Frank Schinzel, Kevin Stovall, Gregory B. Taylor, Lincoln J. Greenhill:
Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy. CoRR abs/1708.00720 (2017)
Coauthor Index
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