Manfred Opper
26 papers · 2003–2024 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (6) πΊοΈ Taxonomy Completionist (15) π Interdisciplinary Bridge π£ Hot Topic Early Bird
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Cross-Pollinator
(13)
πΊοΈ
Taxonomy Completionist
(15)
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Renaissance Researcher
(6)
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Keyword Trendsetter Combo
(6)
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Triple Crown
π¬
Deep Specialist
(12)
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Keyword Champion
(2)
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Grand Slam
π±
Topic Pioneer
ποΈ
Keyword Collector
(70)
π₯
Unstoppable
(9)
π
Trend Setter
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Conference Pioneer
π
Century Club
(26)
Conferences
NIPS (13)
JMLR (5)
AISTATS (4)
AAAI (1)
ICLR (1)
ICML (1)
UAI (1)
Top co-authors
Research topics
Keywords
gaussian process
(12)
variational inference
(12)
approximate inference
(7)
bayesian inference
(7)
stochastic process
(4)
state estimation
(3)
poisson process
(3)
expectation propagation
(3)
gaussian process classification
(2)
gaussian processes
(2)
continuous time
(2)
mean field approximation
(2)
chinese restaurant process
(2)
posterior inference
(2)
stochastic processes
(2)
markov chain monte carlo
(2)
point process
(2)
data augmentation
(2)
parameter inference
(2)
latent variable model
(2)
Papers
Generative Fractional Diffusion Models
NIPS 2024
Bridging discrete and continuous state spaces: Exploring the Ehrenfest process in time-continuous diffusion models
ICML 2024
Variational Inference for SDEs Driven by Fractional Noise
ICLR 2024
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
AISTATS 2020
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
UAI 2019
Efficient Gaussian Process Classification Using PΓ³lya-Gamma Data Augmentation
AAAI 2019
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
JMLR 2018
Perturbative Black Box Variational Inference
NIPS 2017
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
NIPS 2015
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
NIPS 2014
Optimal Neural Codes for Control and Estimation
NIPS 2014
Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models
JMLR 2013
Approximate inference in latent Gaussian-Markov models from continuous time observations
NIPS 2013
Approximate Gaussian process inference for the drift function in stochastic differential equations
NIPS 2013
Bayesian Inference for Change Points in Dynamical Systems with Reusable States - a Chinese Restaurant Process Approach
AISTATS 2012
Inference in continuous-time change-point models
NIPS 2011
Analytical Results for the Error in Filtering of Gaussian Processes
NIPS 2011
Approximate inference in continuous time Gaussian-Jump processes
NIPS 2010
Approximate parameter inference in a stochastic reaction-diffusion model
AISTATS 2010
Regret Bounds for Gaussian Process Bandit Problems
AISTATS 2010
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
JMLR 2009
Improving on Expectation Propagation
NIPS 2008
Variational inference for Markov jump processes
NIPS 2007
Variational Inference for Diffusion Processes
NIPS 2007
Expectation Consistent Approximate Inference
JMLR 2005
An Approximate Analytical Approach to Resampling Averages
JMLR 2003