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Automatic Construction and Natural-Language Description of Nonparametric Regression Models

18 February 2014

Papers citing "Automatic Construction and Natural-Language Description of Nonparametric Regression Models"

50 / 80 papers shown

Title
Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects Mattias Tiger Daniel Jakobsson Anders Ynnerman Fredrik Heintz Daniel Jonsson 53 0 0 05 Sep 2024
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes Andreas Besginow J. D. Hüwel Thomas Pawellek Christian Beecks Markus Lange-Hegermann 35 0 0 14 Mar 2024
Automated Statistical Model Discovery with Language Models Michael Y. Li Emily B. Fox Noah D. Goodman 89 19 0 27 Feb 2024
Uncertainty-Aware Calibration of a Hot-Wire Anemometer With Gaussian Process Regression Rubén A. García-Ruiz J. Blanco-Claraco J. López-Martínez Á. Callejón-Ferre 18 7 0 16 Jan 2024
Learning material synthesis-process-structure-property relationship by data fusion: Bayesian Coregionalization N-Dimensional Piecewise Function Learning A. Kusne A. McDannald Brian L. DeCost 55 2 0 10 Nov 2023
On the Identifiability and Interpretability of Gaussian Process Models Jiawen Chen W. Mu Yun Li Didong Li 37 3 0 25 Oct 2023
A Primer on Bayesian Neural Networks: Review and Debates Federico Danieli Konstantinos Pitas M. Vladimirova Vincent Fortuin BDL AAML 103 20 0 28 Sep 2023
Sequential Monte Carlo Learning for Time Series Structure Discovery Feras A. Saad Brian Patton Matt Hoffman Rif A. Saurous Vikash K. Mansinghka AI4TS 51 11 0 13 Jul 2023
Beyond Intuition, a Framework for Applying GPs to Real-World Data K. Tazi J. Lin Ross Viljoen A. Gardner S. T. John Hong Ge Richard Turner GP 55 4 0 06 Jul 2023
Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels M. Bitzer Mona Meister Christoph Zimmer 73 9 0 16 Jun 2023
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning Micah Goldblum Marc Finzi K. Rowan A. Wilson UQCV FedML 133 43 0 11 Apr 2023
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems M. Bitzer Mona Meister Christoph Zimmer 68 6 0 17 Mar 2023
Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting Yunyao Cheng Chenjuan Guo Kai Chen Kai Zhao B. Yang Jiandong Xie Christian S. Jensen Feiteng Huang Kai Zheng AI4TS 72 1 0 20 Dec 2022
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport M. Bitzer Mona Meister Christoph Zimmer 88 9 0 21 Oct 2022
Trust in Language Grounding: a new AI challenge for human-robot teams David M. Bossens C. Evers 90 1 0 05 Sep 2022
A Survey of Open Source Automation Tools for Data Science Predictions Nicholas Hoell 63 0 0 24 Aug 2022
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes Gregory W. Benton Wesley J. Maddox A. Wilson AI4TS 45 3 0 13 Jul 2022
A Change Dynamic Model for the Online Detection of Gradual Change Chris Browne 42 0 0 02 May 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 149 58 0 23 Feb 2022
Online Time Series Anomaly Detection with State Space Gaussian Processes Christian Bock Franccois-Xavier Aubet Jan Gasthaus Andrey Kan Ming Chen Laurent Callot AI4TS 62 8 0 18 Jan 2022
Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships Eunice Jun Audrey Seo Jeffrey Heer René Just 143 19 0 07 Jan 2022
Online structural kernel selection for mobile health Eura Shin Pedja Klasnja Susan Murphy Finale Doshi-Velez 49 1 0 21 Jul 2021
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection Kieran Wood Stephen J. Roberts S. Zohren 35 21 0 28 May 2021
Model Selection for Production System via Automated Online Experiments Zhenwen Dai Praveen Chandar G. Fazelnia Ben Carterette M. Lalmas 64 4 0 27 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 137 134 0 14 May 2021
Automating Data Science: Prospects and Challenges Tijl De Bie Luc de Raedt José Hernández-Orallo Holger H. Hoos Padhraic Smyth C. Williams 124 38 0 12 May 2021
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain F. Simpson A. Boukouvalas Václav Cadek E. Sarkans N. Durrande 51 3 0 11 Mar 2021
Tractable Computation of Expected Kernels Wenzhe Li Zhe Zeng Antonio Vergari Guy Van den Broeck TPM 54 1 0 21 Feb 2021
Gaussian Process Latent Class Choice Models G. Sfeir Filipe Rodrigues Maya Abou-Zeid GP 34 12 0 28 Jan 2021
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior Anh Tong Toan M. Tran Hung Bui Jaesik Choi 49 3 0 21 Dec 2020
Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries Louis Raynal Sixing Chen Antonietta Mira J. Onnela 26 3 0 09 Nov 2020
Marginalised Gaussian Processes with Nested Sampling F. Simpson V. Lalchand C. Rasmussen GP 131 10 0 30 Oct 2020
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation R. Maulik T. Botsas Nesar Ramachandra L. Mason Indranil Pan 38 31 0 23 Jul 2020
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification Edward Raff Charles K. Nicholas AAML 70 57 0 15 Jun 2020
Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits Jack Parker-Holder Vu Nguyen Stephen J. Roberts OffRL 155 86 0 06 Feb 2020
Human-like Time Series Summaries via Trend Utility Estimation Pegah Jandaghi Jay Pujara AI4TS 41 1 0 16 Jan 2020
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression T. Teng Jie Chen Yehong Zhang K. H. Low BDL 78 23 0 05 Dec 2019
Evolving Gaussian Process kernels from elementary mathematical expressions Ibai Roman Roberto Santana A. Mendiburu Jose A. Lozano 42 3 0 11 Oct 2019
Inference of modes for linear stochastic processes R. MacKay 22 1 0 23 Sep 2019
Heterogeneous Relational Kernel Learning A. Nguyen Edward Raff BDL 27 1 0 24 Aug 2019
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling Feras A. Saad Marco F. Cusumano-Towner Ulrich Schaechtle Martin Rinard Vikash K. Mansinghka 58 62 0 14 Jul 2019
On the Semantic Interpretability of Artificial Intelligence Models V. S. Silva André Freitas Siegfried Handschuh AI4CE 47 8 0 09 Jul 2019
Learning GPLVM with arbitrary kernels using the unscented transformation Daniel Augusto R. M. A. de Souza Diego Mesquita C. L. C. Mattos Joao P. P. Gomes 61 0 0 03 Jul 2019
Neurally-Guided Structure Inference Sidi Lu Jiayuan Mao J. Tenenbaum Jiajun Wu 61 7 0 17 Jun 2019
Deep Gaussian Processes with Importance-Weighted Variational Inference Hugh Salimbeni Vincent Dutordoir J. Hensman M. Deisenroth BDL 99 44 0 14 May 2019
"Why did you do that?": Explaining black box models with Inductive Synthesis Görkem Paçaci David Johnson S. McKeever A. Hamfelt 35 6 0 17 Apr 2019
Sentiment analysis with genetically evolved Gaussian kernels Ibai Roman A. Mendiburu Roberto Santana Jose A. Lozano GP 36 10 0 01 Apr 2019
Functional Variational Bayesian Neural Networks Shengyang Sun Guodong Zhang Jiaxin Shi Roger C. Grosse BDL 68 240 0 14 Mar 2019
Automated Adaptation Strategies for Stream Learning Rashid Bakirov Bogdan Gabrys D. Fay 49 10 0 27 Dec 2018
How to improve the interpretability of kernel learning Jinwei Zhao Qizhou Wang Yufei Wang Yu Liu Zhenghao Shi Xinhong Hei FAtt 33 0 0 21 Nov 2018