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Coresets for Scalable Bayesian Logistic Regression
[article]
2017
arXiv
pre-print
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In this paper, we develop an efficient coreset construction algorithm for Bayesian logistic regression models. ...
We provide theoretical guarantees on the size and approximation quality of the coreset -- both for fixed, known datasets, and in expectation for a wide class of data generative models. ...
Although our coreset algorithm is specifically for logistic regression, our approach is broadly applicable to other Bayesian generative models. ...
arXiv:1605.06423v3
fatcat:7h27aedambbujlurg4sfbz42im
Automated Scalable Bayesian Inference via Hilbert Coresets
[article]
2019
arXiv
pre-print
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Building on the Bayesian coresets framework, this work instead takes advantage of data redundancy to shrink the dataset itself as a preprocessing step, providing fully-automated, scalable Bayesian inference ...
To address these shortcomings we develop Hilbert coresets, i.e., Bayesian coresets constructed under a norm induced by an inner-product on the log-likelihood function space. ...
We also thank Sushrutha Reddy for finding and correcting a bug in the proof of Theorem 3.2. ...
arXiv:1710.05053v2
fatcat:n2fyqlemdjeu3jqtcckee3tfgm
β-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers
[article]
2020
arXiv
pre-print
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Moreover, relying on the recent formulations of Riemannian coresets for scalable Bayesian inference, we propose a sparse variational approximation of the robustified posterior and an efficient stochastic ...
We illustrate the applicability of our approach in diverse simulated and real datasets, and various statistical models, including Gaussian mean inference, logistic and neural linear regression, demonstrating ...
Figure 2 : 2 Predictive accuracy vs coreset size for logistic regression experiments over 10 trials on 3 large-scale datasets. ...
arXiv:2008.13600v2
fatcat:65hmo56nn5fpvkc3ggsv5alihi
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2007.00715v1 fatcat:tukuzyetfja6xca7jjam5dh6yq
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. ...
Leveraging recent advances in accelerated optimization methods, we propose and analyze a novel algorithm for coreset selection. ...
Introduction Bayesian coresets have emerged as a promising approach for scalable Bayesian inference [22, 12, 13, 11] . ...
arXiv:2007.00715v2
fatcat:awoiiwz5jnbrbenlva4cdc3dmq
Sparse Variational Inference: Bayesian Coresets from Scratch
[article]
2019
arXiv
pre-print
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Recent work on Bayesian coresets takes the approach of compressing the dataset before running a standard inference algorithm, providing both scalability and guarantees on posterior approximation error. ...
, the proposed algorithm is able to continually improve the coreset, providing state-of-the-art Bayesian dataset summarization with orders-of-magnitude reduction in KL divergence to the exact posterior ...
Bayesian logistic and Poisson regression Finally, we compared the methods on logistic and Poisson regression applied to six datasets (details may be found in Appendix C) with N = 500 and dimension ranging ...
arXiv:1906.03329v2
fatcat:t3gfmln6uncvroymmgbuz4tl2q
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
[article]
2018
arXiv
pre-print
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But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior uncertainty estimation in the pursuit of scalability. ...
To address this shortcoming, we develop greedy iterative geodesic ascent (GIGA), a novel algorithm for Bayesian coreset construction that scales the coreset log-likelihood optimally. ...
Bayesian Posterior Approximation In this experiment, we used GIGA to generate Bayesian coresets for logistic and Poisson regression. ...
arXiv:1802.01737v2
fatcat:myl4ewuszrfdrgr76gxf4zoa7a
A Novel Sequential Coreset Method for Gradient Descent Algorithms
[article]
2021
arXiv
pre-print
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However, most of existing coreset methods are problem-dependent and cannot be used as a general tool for a broader range of applications. ...
Moreover, our method is particularly suitable for sparse optimization whence the coreset size can be further reduced to be only poly-logarithmically dependent on the dimension. ...
Coresets for
scalable bayesian logistic regression. In Advances in Neural Information
Processing Systems, pages 4080–4088, 2016.
[27] Praneeth Kacham and David P. Woodruff. ...
arXiv:2112.02504v1
fatcat:ca6ik4vfgfgqrmoqottujcbhwa
Text/Conference Paper
2019
Jahrestagung der Gesellschaft für Informatik
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First, we show the negative result that no strongly sublinear sized coresets exist for logistic regression. ...
Coresets are one of the central methods to facilitate the analysis of large data.We continue a recent line of research applying the theory of coresets to logistic regression. ...
Acknowledgments We thank the anonymous reviewers for their valuable comments. We also thank our assistant Moritz Paweletz. ...
doi:10.18420/inf2019_37
dblp:conf/gi/MunteanuSSW19
fatcat:7smn24bxybblrlo2qzjc2ykvq4
Data Summarization via Bilevel Optimization
[article]
2021
arXiv
pre-print
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However, existing coreset constructions are highly model-specific and are limited to simple models such as linear regression, logistic regression, and k-means. ...
Coresets are weighted subsets of the data that provide approximation guarantees for the optimization objective. ...
We standardize the features and solve Figure 6 : Coresets for binary logistic regression. ...
arXiv:2109.12534v1
fatcat:f5yewtrb3nehfcs2s5i6jxbfgy
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
[article]
2021
arXiv
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(ii) A proof that, under a natural assumption that holds e.g. for logistic regression and the sigmoid activation functions, a small coreset exists for any input P. ...
In this work we provide: (i) A lower bound which proves that there are sets with no coresets smaller than n=|P| for general monotonic loss functions. ...
The following theorems construct a coreset for sums of sigmoid functions and for the logistic regression log-likelihood, for normalized input sets. ...
arXiv:1802.07382v3
fatcat:7lapvbmpubal5gj3e2j662637e
On Coresets for Logistic Regression
[article]
2018
arXiv
pre-print
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First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. ...
The experiments are conducted on real world benchmark data for logistic regression. ...
Acknowledgments We thank the anonymous reviewers for their valuable comments. We also thank our student assistant Moritz Paweletz for implementing and conducting the experiments. ...
arXiv:1805.08571v2
fatcat:z4fw6euzrbahhe3ajg7u22cuaq
Bayesian Batch Active Learning as Sparse Subset Approximation
[article]
2021
arXiv
pre-print
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In this paper, we introduce a novel Bayesian batch active learning approach that mitigates these issues. ...
We demonstrate the benefits of our approach on several large-scale regression and classification tasks. ...
We thank Adrià Garriga-Alonso, James Requeima, Marton Havasi, Carl Edward Rasmussen and Trevor Campbell for helpful feedback and discussions. ...
arXiv:1908.02144v4
fatcat:zovwsi2oenftzfm3jqwl3habdi
Surrogate Likelihoods for Variational Annealed Importance Sampling
[article]
2021
arXiv
pre-print
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Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling. ...
For these reasons researchers have sought to combine the strengths of both classes of algorithms, with recent approaches coming closer to realizing this vision in practice. ...
Hand-
scalable bayesian logistic regression. Advances in Neural book of markov chain monte carlo, 2(11):2, 2011. ...
arXiv:2112.12194v1
fatcat:2qhyj7y73bdtvfxh2frq7s6jme
Interpreting Black Box Predictions using Fisher Kernels
[article]
2018
arXiv
pre-print
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To answer this question, we make use of Fisher kernels as the defining feature embedding of each data point, combined with Sequential Bayesian Quadrature (SBQ) for efficient selection of examples. ...
Our goal is to ask 'which training examples are most responsible for a given set of predictions'? ...
Figure 4 : Performance for logistic regression over two datasets (left is ChemReact while right is CovType) of our method (Fisher) vs coreset selection [11] and random data selection. ...
arXiv:1810.10118v1
fatcat:ii35eupdejfuzn6kbftueffdni
p-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets
[article]
2022
arXiv
pre-print
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We study the p-generalized probit regression model, which is a generalized linear model for binary responses. ...
subsampling to obtain a small data summary called coreset. ...
Katja Ickstadt for pointing us to the probit model and for valuable discussions on that topic. ...
arXiv:2203.13568v1
fatcat:fdbiroscdrderfn5ftphdxevoa
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