Kristofer E. Bouchard

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All published works

Action	Title	Year	Authors
+ PDF Chat	Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics	2024	Ankit Kumar Loren M. Frank Kristofer E. Bouchard
+ PDF Chat	Methods for Linking Data to Online Resources and Ontologies with Applications to Neurophysiology	2024	Matthew Avaylon Ryan Ly Andrew Tritt Ben Dichter Kristofer E. Bouchard Chris Mungall Oliver Ruebel
+ PDF Chat	The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies	2024	Marcin P. Joachimiak Mark A. Miller J. Harry Caufield Ryan Ly Nomi L. Harris Andrew Tritt Chris Mungall Kristofer E. Bouchard
+ PDF Chat	Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits	2024	S. J. Ben Yoo Luis El-Srouji Suman Datta Shimeng Yu Jean Anne C. Incorvia Alberto Salleo Volker J. Sorger Juejun Hu Lionel C. Kimerling Kristofer E. Bouchard
+	AutoCT: Automated CT registration, segmentation, and quantification	2024	Zhe Bai Abdelilah Essiari Talita Perciano Kristofer E. Bouchard
+ PDF Chat	FAIR for AI: An interdisciplinary and international community building perspective	2023	E. A. Huerta Ben Blaiszik L. Catherine Brinson Kristofer E. Bouchard D. Díaz C. Doglioni J. Duarte Murali Emani Ian Foster Geoffrey Fox
+	Perspectives for self-driving labs in synthetic biology	2023	Héctor García Martín Tijana Radivojević Jeremy Zucker Kristofer E. Bouchard Jess Sustarich Sean Peisert Dan Arnold Nathan J. Hillson G. Babnigg Jose Manuel Martí
+	AutoCT: Automated CT registration, segmentation, and quantification	2023	Zhe Bai Abdelilah Essiari Talita Perciano Kristofer E. Bouchard
+ PDF Chat	Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks	2023	Rui Meng Herbert K. H. Lee Kristofer E. Bouchard
+ PDF Chat	Numerical characterization of support recovery in sparse regression with correlated design	2022	Ankit Kumar Sharmodeep Bhattacharyya Kristofer E. Bouchard
+	Compressed Predictive Information Coding	2022	Rui Meng T. David Luo Kristofer E. Bouchard
+ PDF Chat	Hangul Fonts Dataset: A Hierarchical and Compositional Dataset for Investigating Learned Representations	2022	Jesse A. Livezey Ahyeon Hwang Jacob Yeung Kristofer E. Bouchard
+	FAIR for AI: An interdisciplinary and international community building perspective	2022	E. A. Huerta Ben Blaiszik L. Catherine Brinson Kristofer E. Bouchard D. Díaz C. Doglioni J. Duarte Murali Emani Ian Foster Geoffrey Fox
+	Perspectives for self-driving labs in synthetic biology	2022	Héctor García Martín Tijana Radivojević Jeremy Zucker Kristofer E. Bouchard Jess Sustarich Sean Peisert Dan Arnold Nathan J. Hillson G. Babnigg Jose Manuel Martí
+	Numerical characterization of support recovery in sparse regression with correlated design	2022	Ankit Kumar Sharmodeep Bhattacharyya Kristofer E. Bouchard
+	Learning from learning machines: a new generation of AI technology to meet the needs of science	2021	Luca Pion-Tonachini Kristofer E. Bouchard Héctor García Martín Sean Peisert W. Holtz Anil Aswani Dipankar Dwivedi Haruko Wainwright Ghanshyam Pilania Benjamin Nachman
+ PDF Chat	Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model	2021	Rui Meng Kristofer E. Bouchard
+	Collaborative Nonstationary Multivariate Gaussian Process Model.	2021	Rui Meng Herbie Lee Kristofer E. Bouchard
+	Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses	2021	Charles G. Frye James B. Simon Neha S. Wadia Andrew Ligeralde Michael R. DeWeese Kristofer E. Bouchard
+	Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model	2021	Rui Meng Kristofer E. Bouchard
+	Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network	2021	Rui Meng Herbie Lee Kristofer E. Bouchard
+	Learning from learning machines: a new generation of AI technology to meet the needs of science	2021	Luca Pion-Tonachini Kristofer E. Bouchard Héctor García Martín Sean Peisert W. Holtz Anil Aswani Dipankar Dwivedi Haruko Wainwright Ghanshyam Pilania Benjamin Nachman
+	Numerical Characterization of Support Recovery in Sparse Regression with Correlated Design	2021	Ankit Kumar Sharmodeep Bhattacharyya Kristofer E. Bouchard
+	Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models	2020	Trevor Ruiz Sharmodeep Bhattacharyya M. Balasubramanian Kristofer E. Bouchard
+	Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses	2020	Charles G. Frye James E. Simon Neha S. Wadia Andrew Ligeralde Michael R. DeWeese Kristofer E. Bouchard
+ PDF Chat	PyUoI: The Union of Intersections Framework in Python	2019	Pratik Sachdeva Jesse A. Livezey Andrew Tritt Kristofer E. Bouchard
+ PDF Chat	Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex	2019	Jesse A. Livezey Kristofer E. Bouchard Edward F. Chang
+ PDF Chat	iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology	2019	Chris Holdgraf Stefan Appelhoff Stephan Bickel Kristofer E. Bouchard Sasha D’Ambrosio Olivier David Orrin Devinsky Ben Dichter Adeen Flinker Brett L. Foster
+	Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis	2019	David G. Clark Jesse A. Livezey Kristofer E. Bouchard
+	Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Interrogating Learned Representations	2019	Jesse A. Livezey Ahyeon Hwang Kristofer E. Bouchard
+	Numerically Recovering the Critical Points of a Deep Linear Autoencoder	2019	Charles G. Frye Neha S. Wadia Michael R. DeWeese Kristofer E. Bouchard
+	Sparse, Low-bias, and Scalable Estimation of High Dimensional Vector Autoregressive Models via Union of Intersections	2019	Trevor Ruiz M. Balasubramanian Kristofer E. Bouchard Sharmodeep Bhattacharyya
+	Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis	2019	David G. Clark Jesse A. Livezey Kristofer E. Bouchard
+	Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis	2019	David G. Clark Jesse A. Livezey Kristofer E. Bouchard
+	Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations	2019	Jesse A. Livezey Ahyeon Hwang Jacob Yeung Kristofer E. Bouchard
+	BIDS-iEEG: an extension to the brain imaging data structure (BIDS) specification for human intracranial electrophysiology	2018	Chris Holdgraf Stefan Appelhoff Stephan Bickel Kristofer E. Bouchard Sasha D’Ambrosio Olivier David Orrin Devinsky Ben Dichter Adeen Flinker Brett L. Foster
+	Run Procrustes, Run! On the convergence of accelerated Procrustes Flow.	2018	Anastasios Kyrillidis Shashanka Ubaru Γεώργιος Κόλλιας Kristofer E. Bouchard
+	Provably convergent acceleration in factored gradient descent with applications in matrix sensing	2018	Tayo Ajayi David Mildebrath Anastasios Kyrillidis Shashanka Ubaru Γεώργιος Κόλλιας Kristofer E. Bouchard
+	Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces	2018	David G. Clark Jesse A. Livezey Edward F. Chang Kristofer E. Bouchard
+	Optimizing the Union of Intersections LASSO ($UoI_{LASSO}$) and Vector Autoregressive ($UoI_{VAR}$) Algorithms for Improved Statistical Estimation at Scale	2018	Mahesh Balasubramanian Trevor Ruiz Brandon Cook Sharmodeep Bhattacharyya Prabhat Aviral Shrivastava Kristofer E. Bouchard
+	Provably convergent acceleration in factored gradient descent with applications in matrix sensing	2018	Tayo Ajayi David Mildebrath Anastasios Kyrillidis Shashanka Ubaru Γεώργιος Κόλλιας Kristofer E. Bouchard
+	Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction	2017	Kristofer E. Bouchard Alejandro F. Bujan Farbod Roosta-Khorasani Shashanka Ubaru Prabhat Antoine M. Snijders Jian‐Hua Mao Edward F. Chang Michael W. Mahoney Sharmodeep Bhattacharyya
+	Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction	2017	Kristofer E. Bouchard Alejandro F. Bujan Farbod Roosta-Khorasani Shashanka Ubaru Prabhat Antoine M. Snijders Jian‐Hua Mao Edward F. Chang Michael W. Mahoney Sharmodeep Bhattacharyya
+ PDF Chat	An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition	2015	Jason D. Wittenbach Kristofer E. Bouchard Michael S. Brainard Dezhe Z. Jin
+	Modeling neural activity at the ensemble level	2015	Joaquín Rapela Mark Kostuk Peter F. Rowat Tim Mullen Edward F. Chang Kristofer E. Bouchard
+	Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation	2015	Kristofer E. Bouchard
+	Modeling neural activity at the ensemble level	2015	Joaquín Rapela Mark Kostuk Peter F. Rowat Tim Mullen Edward F. Chang Kristofer E. Bouchard

Common Coauthors

Coauthor	Papers Together
Jesse A. Livezey	9
Sharmodeep Bhattacharyya	8
Edward F. Chang	6
Shashanka Ubaru	5
Prabhat	5
Héctor García Martín	4
David G. Clark	4
Sean Peisert	4
Michael W. Mahoney	4
Rui Meng	4
Chris Mungall	4
Neha S. Wadia	3
Michael R. DeWeese	3
Anastasios Kyrillidis	3
Andrew Tritt	3
Charles G. Frye	3
Γεώργιος Κόλλιας	3
Ahyeon Hwang	3
Trevor Ruiz	3
Jeremy Zucker	2
Orrin Devinsky	2
Alejandro F. Bujan	2
Anil Aswani	2
Brett L. Foster	2
Jacob Yeung	2
W. Holtz	2
Jonathan Winawer	2
Abdelilah Essiari	2
Ian Foster	2
Rui Meng	2
David Mildebrath	2
Murali Emani	2
Olivier David	2
L. Heinrich	2
Zhe Bai	2
Ravi Madduri	2
Stephan Bickel	2
Stefan Appelhoff	2
Krzysztof J. Gorgolewski	2
Oliver Rübel	2
Deepti Tanjore	2
Peter J. Bickel	2
Amy C. Justice	2
Tim Mullen	2
Haruko Wainwright	2
Iris I.A. Groen	2
Ryan Ly	2
D. Agarwal	2
L. Catherine Brinson	2
Bobbie‐Jo Webb‐Robertson	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex	2019	Jesse A. Livezey Kristofer E. Bouchard Edward F. Chang	4
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	4
+	Definitions, methods, and applications in interpretable machine learning	2019	William J. Murdoch Chandan Singh Karl Kumbier Reza Abbasi-Asl Bin Yu	3
+	Gradient Descent Only Converges to Minimizers	2016	Jason D. Lee Max Simchowitz Michael I. Jordan Benjamin Recht	3
+	Scikit-learn: Machine Learning in Python	2012	Fabián Pedregosa Gaël Varoquaux Alexandre Gramfort Vincent Michel Bertrand Thirion Olivier Grisel Mathieu Blondel Peter Prettenhofer Ron J. Weiss Vincent Dubourg	3
+	Log Gaussian Cox Processes	1998	Jesper Møller Anne Randi Syversveen Rasmus Waagepetersen	3
+	Exact solutions to the nonlinear dynamics of learning in deep linear neural networks	2013	Andrew Saxe James L. McClelland Surya Ganguli	3
+ PDF Chat	Nonstationary multivariate Gaussian processes for electronic health records	2021	Rui Meng Braden Soper Herbert K. H. Lee Jean‐Louis Vincent J Greene Priyadip Ray	3
+ PDF Chat	Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints	2016	Laurent Lessard Benjamin Recht Andrew Packard	2
+ PDF Chat	CosmoFlow: Using Deep Learning to Learn the Universe at Scale	2018	Amrita Mathuriya Deborah Bard P. J. Mendygral Lawrence Meadows James Arnemann Lei Shao Siyu He Tuomas Kärnä Diana Moise S. J. Pennycook	2
+	BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography	2018	Cyril Pernet Stefan Appelhoff Guillaume Flandin Christophe Phillips Arnaud Delorme Robert Oostenveld	2
+	Gaussian Process Regression Networks	2011	Andrew Gordon Wilson David A. Knowles Zoubin Ghahramani	2
+	Black Box Variational Inference	2014	Rajesh Ranganath Sean Gerrish David M. Blei	2
+	A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements	2015	Qinqing Zheng John Lafferty	2
+ PDF Chat	Self-driving laboratory for accelerated discovery of thin-film materials	2020	Benjamin P. MacLeod Fraser G. L. Parlane Thomas D. Morrissey Florian Häse Loı̈c M. Roch Kevan E. Dettelbach Raphaell Moreira Lars P. E. Yunker Michael B. Rooney Joseph R. Deeth	2
+ PDF Chat	ADMiRA: Atomic Decomposition for Minimum Rank Approximation	2010	Kiryung Lee Yoram Bresler	2
+	A Direct Estimation of High Dimensional Stationary Vector Autoregressions	2013	Fang Han Huanran Lu Han Liu	2
+	Dynamic reconfiguration of human brain networks during learning	2011	Danielle S. Bassett Nicholas F. Wymbs Mason A. Porter Peter J. Mucha Jean M. Carlson Scott T. Grafton	2
+	Inferring high-dimensional poisson autoregressive models	2016	Eric C. Hall Garvesh Raskutti Rebecca Willett	2
+ PDF Chat	CUR matrix decompositions for improved data analysis	2009	Michael W. Mahoney Petros Drineas	2
+	A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization	2003	Samuel Burer Renato D. C. Monteiro	2
+	Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably.	2016	Dohyung Park Anastasios Kyrillidis Constantine Caramanis Sujay Sanghavi	2
+	Geometry of neural network loss surfaces via random matrix theory	2017	Jeffrey Pennington Yasaman Bahri	2
+	Provable quantum state tomography via non-convex methods	2017	Anastasios Kyrillidis Amir Kalev Dohyung Park Srinadh Bhojanapalli Constantine Caramanis Sujay Sanghavi	2
+	Newton-MR: Newton's Method Without Smoothness or Convexity	2018	Fred Roosta Yang Liu Peng Xu Michael W. Mahoney	2
+	Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees	2015	Yudong Chen Martin J. Wainwright	2
+	Guaranteed Minimum Rank Approximation from Linear Observations by Nuclear Norm Minimization with an Ellipsoidal Constraint	2009	Kiryung Lee Yoram Bresler	2
+	Universal low-rank matrix recovery from Pauli measurements	2011	Yikai Liu	2
+ PDF Chat	Nearly unbiased variable selection under minimax concave penalty	2010	Cun‐Hui Zhang	2
+ PDF Chat	A rank minimization heuristic with application to minimum order system approximation	2001	Maryam Fazel H. Hindi Stephen Boyd	2
+	Regularization Paths for Generalized Linear Models via Coordinate Descent.	2010	Jerome H. Friedman Trevor Hastie Rob Tibshirani	2
+ PDF Chat	Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization	2010	Benjamin Recht Maryam Fazel Pablo A. Parrilo	2
+	Some methods of speeding up the convergence of iteration methods	1964	B. T. Polyak	2
+	Guaranteed Rank Minimization via Singular Value Projection	2010	Prateek Jain Raghu Meka Inderjit S. Dhillon	2
+	Distilling the Knowledge in a Neural Network	2015	Geoffrey E. Hinton Oriol Vinyals Jay B. Dean	2
+ PDF Chat	Going deeper with convolutions	2015	Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke Andrew Rabinovich	2
+ PDF Chat	Deep Learning Face Attributes in the Wild	2015	Ziwei Liu Ping Luo Xiaogang Wang Xiaoou Tang	2
+	Analysis of Newton’s Method at Irregular Singularities	1983	Andreas Griewank M. R. Osborne	2
+ PDF Chat	Joint Estimation of Multiple Graphical Models from High Dimensional Time Series	2015	Huitong Qiu Fang Han Han Liu Brian Caffo	2
+	Multivariable variogram and its application to the linear model of coregionalization	1991	Gilles Bourgault Denis Marcotte	2
+ PDF Chat	Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators	2012	Steven T. Flammia David Groß Yi-Kai Liu Jens Eisert	2
+ PDF Chat	Matrix Recipes for Hard Thresholding Methods	2013	Anastasios Kyrillidis Volkan Cevher	2
+ PDF Chat	The Jackknife and the Bootstrap for General Stationary Observations	1989	Hans R. Künsch	2
+ PDF Chat	Robust principal component analysis?	2011	Emmanuel J. Candès Xiaodong Li Yi Ma John Wright	2
+ PDF Chat	The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes	2013	Patrick Danaher Pei Wang Daniela Witten	2
+	Stochastic variational inference	2013	Matthew D. Hoffman David M. Blei Chong Wang John Paisley	2
+	Guaranteed Matrix Completion via Non-Convex Factorization	2016	Ruoyu Sun Zhi‐Quan Luo	2
+	No spurious local minima in nonconvex low rank problems: a unified geometric analysis	2017	Rong Ge Chi Jin Yi Zheng	2
+	Interior-Point Method for Nuclear Norm Approximation with Application to System Identification	2009	Zhang Liu Lieven Vandenberghe	2
+	Provable Burer-Monteiro factorization for a class of norm-constrained matrix problems	2016	Dohyung Park Anastasios Kyrillidis Srinadh Bhojanapalli Constantine Caramanis Sujay Sanghavi	2