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1Bayesian linear regression for practitioners • Max Halford
https://maxhalford.github.io/blog/bayesian-linear-regression/
a year ago by @becker
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bayesian
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explained
tutorial
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bayesianlinearregressionexplainedtutorialtheory
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1Bayesian Linear Regression
https://gregorygundersen.com/blog/2020/02/04/bayesian-linear-regression/
2 years ago by @becker
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bayesian
linear
regression
background
knowledge
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bayes
bayesianlinearregressionbackgroundknowledgeintegrationbayes
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1(2) Manuel Gomez Rodriguez - Learning Under Algorithmic Triage - YouTube
https://www.youtube.com/watch?v=008F-4tc0MM&ab_channel=ForschungszentrumL3S
3 years ago by @becker
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uncertainty
triage
policy
human
deferral
subgroups
subgroup
ridge
regression
optimization
uncertaintytriagepolicyhumandeferralsubgroupssubgroupridgeregressionoptimization
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1Bayesian regression predictions - Tutorials - Pyro Discussion Forum
https://forum.pyro.ai/t/bayesian-regression-predictions/1115/8
5 years ago by @becker
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bayes
pyro
bayesian
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bayespyrobayesianregressionsampleprediction
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1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro
https://github.com/pyro-ppl/pyro/issues/1930
5 years ago by @becker
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bayespyrobayesianregressionsampleprediction
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1Financial forecasting with probabilistic programming and Pyro
https://medium.com/@alexrachnog/financial-forecasting-with-probabilistic-programming-and-pyro-db68ab1a1dba
5 years ago by @becker
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bayespyrobayesianregressionsampleprediction
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1Don’t Sweat the Solver Stuff - Towards Data Science
https://towardsdatascience.com/dont-sweat-the-solver-stuff-aea7cddc3451
5 years ago by @becker
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logistic
regression
sklearn
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model
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learning
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science
solver
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py
logisticregressionsklearnlinearmodelmachinelearningdatasciencesolverpythonpy
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1scikit learn - logistic regression python solvers' defintions - Stack Overflow
https://stackoverflow.com/questions/38640109/logistic-regression-python-solvers-defintions
5 years ago by @becker
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logistic
regression
sklearn
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solvers
python
machine
learning
linear
data
science
logisticregressionsklearnsolversolverspythonmachinelearninglineardatascience
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1Gaussian process regression networks
https://dl.acm.org/citation.cfm?id=3042720
6 years ago by @becker
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regression
process
gene
expression
bio
lab
nalab
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1Fix PyPi release (missing libgfortran.so.3) · Issue #38 · bbalasub1/glmnet_python
https://github.com/bbalasub1/glmnet_python/issues/38
6 years ago by @becker
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glmnet
python
py
python3
elasticnet
elastic
net
fortran
libgfortran
error
issue
hastie
linear
regression
fix
pypi
bug
github
glmnetpythonpypython3elasticnetelasticnetfortranlibgfortranerrorissuehastielinearregressionfixpypibuggithub
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1The perils of leave-one-out crossvalidation for individual difference analyses
http://www.russpoldrack.org/2012/
6 years ago by @becker
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en
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1Response to “Perils of LOO crossvalidation”
http://not2hastie.tumblr.com/
6 years ago by @becker
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validation
leave
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out
loo
negative
correlation
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net
en
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1Translation and scaling invariance in regression models
https://roamanalytics.com/2016/11/17/translation-and-scaling-invariance-in-regression-models/#Scaling-affects-regularized-regression
6 years ago by @becker
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regression
scaling
linear
regularization
center
centering
machine
learning
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mining
regressionscalinglinearregularizationcentercenteringmachinelearningdatamining
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1Small, Portable Sensors Allow Users <br> to Monitor Exposure to Pollution on Their Smart Phones
Latent Variable Gaussian Regression
11 years ago by @becker
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airquality
article
calibration
gaussian
latent
regression
s:2013-08-22_calibration
sensorbox
variable
airqualityarticlecalibrationgaussianlatentregressions:2013-08-22_calibrationsensorboxvariable
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1R - Regression
http://www.r-bloggers.com/sab-r-metrics-basics-of-loess-regression-2/
12 years ago by @becker
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R
error
polygon
regression
Rerrorpolygonregression
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1Feature-weighted elastic net: using "features of features" for better prediction
J. Tay, N. Aghaeepour, T. Hastie, and R. Tibshirani. Statistica Sinica, 33 (1): 259--279 (January 2023)
9 months ago by @becker
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background
bki
ien
informed
integration
knowledge
learning
linear
machine
ml
regression
backgroundbkiieninformedintegrationknowledgelearninglinearmachinemlregression
(0)
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1Time series extrinsic regression: Predicting numeric values from time series data
C. Tan, C. Bergmeir, F. Petitjean, and G. Webb. Data Mining and Knowledge Discovery, (2021)
a year ago by @becker
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time
series
extrinsic
regression
citedby:scholar:count:65
citedby:scholar:timestamp:2023-11-4
timeseriesextrinsicregressioncitedby:scholar:count:65citedby:scholar:timestamp:2023-11-4
(0)
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9Regression shrinkage and selection via the lasso
R. Tibshirani. Journal of the Royal Statistical Society: Series B (Methodological), 58 (1): 267--288 (1996)
3 years ago by @becker
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um1
regression
shrinkage
lasso
citedby:scholar:count:43656
citedby:scholar:timestamp:2022-1-28
um1regressionshrinkagelassocitedby:scholar:count:43656citedby:scholar:timestamp:2022-1-28
(0)
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1Bayesian multitask learning regression for heterogeneous patient cohorts
A. Goncalves, P. Ray, B. Soper, D. Widemann, M. Nyg\aard, J. Nyg\aard, and A. Sales. Journal of Biomedical Informatics: X, (2019)
3 years ago by @becker
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bayes
bayesian
multitask
multi
task
learning
regression
heterogeneous
patient
medical
records
citedby:scholar:count:4
citedby:scholar:timestamp:2022-1-26
bayesbayesianmultitaskmultitasklearningregressionheterogeneouspatientmedicalrecordscitedby:scholar:count:4citedby:scholar:timestamp:2022-1-26
(0)
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14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
F. Lautenschlager, M. Becker, K. Kobs, M. Steininger, P. Davidson, A. Krause, and A. Hotho. Atmospheric Environment, (2020)
5 years ago by @becker
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air
automl
bayes
eva
everyaware
land
lur
myown
open
optimization
project:bmbf
quality
regression
use
airautomlbayesevaeveryawarelandlurmyownopenoptimizationproject:bmbfqualityregressionuse
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1Principal component-guided sparse regression
J. Tay, J. Friedman, and R. Tibshirani. arXiv preprint arXiv:1810.04651, (2018)
5 years ago by @becker
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component
guided
nalab
principal
r01
regression
sparse
componentguidednalabprincipalr01regressionsparse
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4Overlapping community detection at scale: a nonnegative matrix factorization approach
J. Yang, and J. Leskovec. Proceedings of the sixth ACM international conference on Web search and data mining, page 587--596. ACM, (2013)
5 years ago by @becker
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community
correlation
detection
graph
matrix
nalab
network
regression
communitycorrelationdetectiongraphmatrixnalabnetworkregression
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4Variational AutoEncoder For Regression: Application to Brain Aging Analysis
Q. Zhao, E. Adeli, N. Honnorat, T. Leng, and K. Pohl. arXiv preprint arXiv:1904.05948, (2019)
5 years ago by @becker
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auto
autoencoder
conditional
cvae
encoder
regression
vae
variational
autoautoencoderconditionalcvaeencoderregressionvaevariational
(0)
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2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, and Z. He. Sustainability, 8 (8): 786 (2016)
7 years ago by @becker
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p2map
eva
osm
lur
land
use
regression
open
street
map
air
quality
pollution
prediction
linear
citedby:scholar:count:4
citedby:scholar:timestamp:2018-2-21
p2mapevaosmlurlanduseregressionopenstreetmapairqualitypollutionpredictionlinearcitedby:scholar:count:4citedby:scholar:timestamp:2018-2-21
(0)
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2Land use regression models for ultrafine particles in six European areas
E. van Nunen, R. Vermeulen, M. Tsai, N. Probst-Hensch, A. Ineichen, M. Davey, M. Imboden, R. Ducret-Stich, A. Naccarati, D. Raffaele and 1 other author(s). Environmental science & technology, 51 (6): 3336--3345 (2017)
7 years ago by @becker
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air
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
eva
features
land
lur
map
open
osm
p2map
pollution
quality
regression
street
use
aircitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21evafeatureslandlurmapopenosmp2mappollutionqualityregressionstreetuse
(0)
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, and A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
7 years ago by @becker
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air
eva
features
land
lur
map
open
osm
p2map
pollution
prediction
quality
regression
roads
street
use
airevafeatureslandlurmapopenosmp2mappollutionpredictionqualityregressionroadsstreetuse
(0)
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3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING
M. Adams. (2015)
7 years ago by @becker
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lur
land
use
regression
air
quality
pollution
neural
networks
nn
prediction
p2map
eva
lurlanduseregressionairqualitypollutionneuralnetworksnnpredictionp2mapeva
(0)
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6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, and A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628--640 (2015)
7 years ago by @becker
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lur
eva
p2map
nn
pm10
neural
network
land
use
regression
air
pollution
quality
non
parametric
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
lurevap2mapnnpm10neuralnetworklanduseregressionairpollutionqualitynonparametriccitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21
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2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models
M. Adams, and P. Kanaroglou. Journal of environmental management, (2016)
7 years ago by @becker
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lur
land
use
regression
air
pollution
quality
eva
p2map
neural
network
nn
mobile
stationary
prediction
citedby:scholar:count:31
citedby:scholar:timestamp:2018-2-21
lurlanduseregressionairpollutionqualityevap2mapneuralnetworknnmobilestationarypredictioncitedby:scholar:count:31citedby:scholar:timestamp:2018-2-21
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3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, and S. Lin. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2017)
7 years ago by @becker
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air
deep
eva
land
lur
network
neural
nn
p2map
pollution
quality
regression
temporal
use
airdeepevalandlurnetworkneuralnnp2mappollutionqualityregressiontemporaluse
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1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter
R. Brokamp. University of Cincinnati, (2016)
7 years ago by @becker
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air
dissertation
eva
forest
land
learning
lur
lurf
machine
p2map
pm
pollution
quality
random
regression
use
variance
airdissertationevaforestlandlearninglurlurfmachinep2mappmpollutionqualityrandomregressionusevariance
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3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
C. Brokamp, R. Jandarov, M. Rao, G. LeMasters, and P. Ryan. Atmospheric Environment, (2017)
7 years ago by @becker
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air
citedby:scholar:count:11
citedby:scholar:timestamp:2018-2-21
comparison
eva
forest
land
learning
linear
lur
machine
ml
p2map
pollution
quality
random
regression
use
aircitedby:scholar:count:11citedby:scholar:timestamp:2018-2-21comparisonevaforestlandlearninglinearlurmachinemlp2mappollutionqualityrandomregressionuse
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4Air pollution mapping using nonlinear land use regression models
A. Champendal, M. Kanevski, and P. Huguenot. International Conference on Computational Science and Its Applications, page 682--690. Springer, (2014)
7 years ago by @becker
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citedby:scholar:count:6
citedby:scholar:timestamp:2018-2-21
eva
forest
land
lur
network
neural
p2map
random
regression
use
citedby:scholar:count:6citedby:scholar:timestamp:2018-2-21evaforestlandlurnetworkneuralp2maprandomregressionuse
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2Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models
L. Dekoninck, D. Botteldooren, and L. Int Panis. International Journal of Environmental Research and Public Health, 14 (6): 586 (2017)
7 years ago by @becker
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p2map
land
use
regression
lur
participatory
sensing
ps
everyaware
eva
p2maplanduseregressionlurparticipatorysensingpseveryawareeva
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2Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes
M. Venanzi, A. Rogers, and N. Jennings. First AAAI Conference on Human Computation and Crowdsourcing, (2013)
8 years ago by @becker
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p2map
heatmap
interpolation
gauss
map
bc
regression
bayes
crowdsourcing
citedby:scholar:count:14
citedby:scholar:timestamp:2017-5-26
p2mapheatmapinterpolationgaussmapbcregressionbayescrowdsourcingcitedby:scholar:count:14citedby:scholar:timestamp:2017-5-26
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3Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors.
B. Maag, O. Saukh, D. Hasenfratz, and L. Thiele. EWSN, page 169--180. (2016)
8 years ago by @becker
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airprobe
neural
networks
multi
linear
regression
calibration
sensor
air
quality
citedby:scholar:count:3
citedby:scholar:timestamp:2017-5-12
airprobeneuralnetworksmultilinearregressioncalibrationsensorairqualitycitedby:scholar:count:3citedby:scholar:timestamp:2017-5-12
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4Retrospective Higher-Order Markov Processes for User Trails
T. Wu, and D. Gleich. arXiv preprint arXiv:1704.05982, (2017)
8 years ago by @becker
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auto
autoregressive
chain
extension
higher
markov
order
process
regression
regressive
retrospective
toread
trails
autoautoregressivechainextensionhighermarkovorderprocessregressionregressiveretrospectivetoreadtrails
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3Dynamic bayesian networks: Representation, inference and learning
K. Murphy. University of California, Berkeley, (2002)
8 years ago by @becker
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ar
auto
chain
citedby:scholar:count:2586
citedby:scholar:timestamp:2017-3-30
diss
hidden
inthesis
markov
mixedtrails
regression
arautochaincitedby:scholar:count:2586citedby:scholar:timestamp:2017-3-30disshiddeninthesismarkovmixedtrailsregression
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2Exceptionally Monotone Models: The Rank Correlation Model Class for Exceptional Model Mining
L. Downar, and W. Duivesteijn. International Conference on Data Mining (ICDM), page 111--120. (2015)
8 years ago by @becker
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application
correlation
diss
emm
exceptional
function
imported
inthesis
kendall
mining
model
potential
prediction
quality
regression
spearman
subtrails
tau
applicationcorrelationdissemmexceptionalfunctionimportedinthesiskendallminingmodelpotentialpredictionqualityregressionspearmansubtrailstau
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1Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates
M. Altini, P. Casale, J. Penders, G. ten Velde, G. Plasqui, and O. Amft. Journal of Applied Physiology, 120 (9): 1082--1096 (May 2016)
8 years ago by @becker
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health
heart
linear
rate
regression
sensor
sport
svm
wearable
healthheartlinearrateregressionsensorsportsvmwearable
(0)
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2Development of land use regression models for PM2. 5, PM2. 5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project
M. Eeftens, R. Beelen, K. de Hoogh, T. Bellander, G. Cesaroni, M. Cirach, C. Declercq, A. Dedele, E. Dons, A. de Nazelle and 1 other author(s). Environmental science & technology, 46 (20): 11195--11205 (2012)
10 years ago by @becker
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air
land
lur
model
modeling
pollution
quality
regression
sitc
urban
use
airlandlurmodelmodelingpollutionqualityregressionsitcurbanuse
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2Multi-dimensional regression analysis of time-series data streams
Y. Chen, G. Dong, J. Han, B. Wah, and J. Wang. Proceedings of the 28th international conference on Very Large Data Bases, page 323--334. VLDB Endowment, (2002)
11 years ago by @becker
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analysis
dimensional
mining
multi
regression
s:2013-12-18_time-series-mining
series
time
analysisdimensionalminingmultiregressions:2013-12-18_time-series-miningseriestime
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1Gaussian processes for regression
C. Williams, and C. Rasmussen. Advances in Neural Information Processing Systems 8, MIT, Copyright of the Massachusetts Institute of Technology Press (MIT Press).(June 1996)
11 years ago by @becker
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calibration
gaussian
prediction
processes
regression
s:2013-08-22_calibration
calibrationgaussianpredictionprocessesregressions:2013-08-22_calibration
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