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Manfred Opper

26 papers · 2003–2024 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (21) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) πŸ—ΊοΈ Taxonomy Completionist (15) 🌈 Renaissance Researcher (6) 🌟 Keyword Trendsetter Combo (6) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion (2) πŸ† Grand Slam 🌱 Topic Pioneer πŸ—ƒοΈ Keyword Collector (70) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ’Ž Century Club (26)

Conferences

NIPS (13) JMLR (5) AISTATS (4) AAAI (1) ICLR (1) ICML (1) UAI (1)

Top co-authors

Andreas Ruttor (6) Guido Sanguinetti (4) Ole Winther (4) Florian Wenzel (3) Florian Stimberg (3) Ulrich Paquet (3) Ron Meir (3) ThΓ©o Galy-Fajou (3) Christian Donner (2) Alex K. Susemihl (2)

Research topics

Probability (1)

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