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ORCIDChristoph Lippert
Person information
- affiliation: Hasso Plattner Institute, Potsdam, Germany
- affiliation: Hasso Plattner Institute for Digital Health at Mount Sinai (HPI-MS), New York, NY, USA
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2020 – today
- 2024
[j15]Masoumeh Javanbakhat
, Ludger Starke, Sonia Waiczies, Christoph Lippert:
Quantifying model uncertainty for semantic segmentation of Fluorine-19 MRI using stochastic gradient MCMC. Comput. Vis. Image Underst. 241: 103967 (2024)- [j14]Jana Fehr, Brian Citro, Rohit Malpani, Christoph Lippert, Vince I. Madai:
A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Frontiers Digit. Health 6 (2024) - [c21]Eshant English, Matthias Kirchler, Christoph Lippert:
Kernelised Normalising Flows. ICLR 2024 - [c20]Eshant English, Matthias Kirchler, Christoph Lippert:
MixerFlow: MLP-Mixer Meets Normalising Flows. ECML/PKDD (1) 2024: 180-196 - [i21]Masoumeh Javanbakhat, Md Tasnimul Hasan, Christoph Lippert:
Assessing Uncertainty Estimation Methods for 3D Image Segmentation under Distribution Shifts. CoRR abs/2402.06937 (2024) - [i20]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i19]Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert:
JANET: Joint Adaptive predictioN-region Estimation for Time-series. CoRR abs/2407.06390 (2024) - 2023
- [j13]Sophie Wharrie, Zhiyu Yang, Vishnu Raj, Remo Monti, Rahul Gupta, Ying Wang, Alicia Martin, Luke J. O'Connor, Samuel Kaski, Pekka Marttinen, Pier Francesco Palamara, Christoph Lippert, Andrea Ganna:
HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes. Bioinform. 39(9) (2023) - [c19]Benjamin Bergner, Christoph Lippert, Aravindh Mahendran:
Iterative Patch Selection for High-Resolution Image Recognition. ICLR 2023 - [c18]Matthias Kirchler, Christoph Lippert, Marius Kloft:
Training Normalizing Flows from Dependent Data. ICML 2023: 17105-17121 - [c17]Alexander Rakowski, Christoph Lippert:
DCID: Deep Canonical Information Decomposition. ECML/PKDD (2) 2023: 20-35 - [i18]Alexander Rakowski, Christoph Lippert:
DCID: Deep Canonical Information Decomposition. CoRR abs/2306.15619 (2023) - [i17]Eshant English, Matthias Kirchler, Christoph Lippert:
Kernelised Normalising Flows. CoRR abs/2307.14839 (2023) - [i16]Masoumeh Javanbakhat, Christoph Lippert:
A Probabilistic Approach to Self-Supervised Learning using Cyclical Stochastic Gradient MCMC. CoRR abs/2308.01271 (2023) - [i15]Eshant English, Matthias Kirchler, Christoph Lippert:
MixerFlow for Image Modelling. CoRR abs/2310.16777 (2023) - 2022
- [j12]Matthias Kirchler, Stefan Konigorski, Matthias Norden, Christian Meltendorf, Marius Kloft, Claudia Schurmann, Christoph Lippert:
transferGWAS: GWAS of images using deep transfer learning. Bioinform. 38(14): 3621-3628 (2022) - [c16]Matthias Norden, Oliver T. Wolf, Lennart Lehmann, Katja Langer, Christoph Lippert, Hanna Drimalla:
Automatic Detection of Subjective, Annotated and Physiological Stress Responses from Video Data. ACII 2022: 1-8 - [c15]Marius Pullig, Benjamin Bergner, Amish Doshi, Anja Hennemuth, Zahi A. Fayad, Christoph Lippert:
Deep Learning Models for 3D MRI Brain Classification - A Multi-sequence Comparison. Bildverarbeitung für die Medizin 2022: 204-209 - [c14]Aiham Taleb, Matthias Kirchler, Remo Monti, Christoph Lippert:
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics. CVPR 2022: 20876-20889 - [c13]Benjamin Bergner, Csaba Rohrer, Aiham Taleb, Martha Duchrau, Guilherme De Leon, Jonas Almeida Rodrigues, Falk Schwendicke, Joachim Krois, Christoph Lippert:
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays. MIDL 2022: 130-149 - [c12]Jakob Lindinger, Barbara Rakitsch, Christoph Lippert:
Laplace approximated Gaussian process state-space models. UAI 2022: 1199-1209 - [i14]Josafat-Mattias Burmeister, Marcel Fernandez Rosas, Johannes Hagemann, Jonas Kordt, Jasper Blum, Simon Shabo, Benjamin Bergner, Christoph Lippert:
Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation. CoRR abs/2207.00845 (2022) - [i13]Matthias Kirchler, Christoph Lippert, Marius Kloft:
Training Normalizing Flows from Dependent Data. CoRR abs/2209.14933 (2022) - [i12]Benjamin Bergner, Christoph Lippert, Aravindh Mahendran:
Iterative Patch Selection for High-Resolution Image Recognition. CoRR abs/2210.13007 (2022) - 2021
- [j11]Jana Fehr, Stefan Konigorski, Stephen Olivier, Resign Gunda, Ashmika Surujdeen, Dickman Gareta, Theresa Smit, Kathy Baisley, Sashen Moodley, Yumna Moosa, Willem Hanekom, Olivier Koole, Thumbi Ndung'u, Deenan Pillay, Alison D. Grant, Mark J. Siedner, Christoph Lippert, Emily B. Wong, Anand Ramnanan, Anele Mkhwanazi, Antony Rapulana, Anupa Singh, Ashentha Govender, Ayanda Zungu, Boitsholo Mfolo, Bongani Magwaza, Bongumenzi Ndlovu, Clive Mavimbela, Costa Criticos, Day Munatsi, Dilip Kalyan, Doctar Mlambo, Fezeka Mfeka, Freddy Mabetlela, Gregory Ording-Jespersen, Hannah Keal, Hlengiwe Dlamini, Hlengiwe Khathi, Hlobisile Chonco, Hlobisile Gumede, Hlolisile Khumalo, Hloniphile Ngubane, Hollis Shen, Hosea Kambonde, Innocentia Mpofana, Jabu Kwinda, Jaco Dreyer, Jade Cousins, Jaikrishna Kalideen, Janet Seeley, Kandaseelan Chetty, Kayleen Brien, Kennedy Nyamande, Kgaugelo Moropane, Khabonina Malomane, Khadija Khan, Khanyisani Buthelezi, Kimeshree Perumal, Kobus Herbst, Lindani Mthembu, Logan Pillay, Mandisi Dlamini, Mandlakayise Zikhali, Mbali Mbuyisa, Mbuti Mofokeng, Melusi Sibiya, Mlungisi Dube, Mosa Suleman, Mpumelelo Steto, Mzamo Buthelezi, Nagavelli Padayachi, Nceba Gqaleni, Ngcebo Mhlongo, Nokukhanya Ntshakala, Nomathamsanqa Majozi, Nombuyiselo Zondi, Nomfundo Luthuli, Nomfundo Ngema, Nompilo Buthelezi, Nonceba Mfeka, Nondumiso Khuluse, Nondumiso Mabaso, Nondumiso Zitha, Nonhlanhla Mfekayi, Nonhlanhla Mzimela, Nozipho Mbonambi, Ntombiyenhlanhla Mkhwanazi, Ntombiyenkosi Ntombela, Pamela Ramkalawon, Pfarelo Tshivase, Phakamani Mkhwanazi, Philippa Mathews, Phumelele Mthethwa, Phumla Ngcobo, Ramesh Jackpersad, Raynold Zondo, Rochelle Singh, Rose Myeni, Sanah Bucibo, Sandile Mthembu, Sashin Harilall, Senamile Makhari, Seneme Mchunu, Senzeni Mkhwanazi, Sibahle Gumbi, Siboniso Nene, Sibusiso Mhlongo, Sibusiso Mkhwanazi, Sibusiso Nsibande, Simphiwe Ntshangase, Siphephelo Dlamini, Sithembile Ngcobo, Siyabonga Nsibande, Siyabonga Nxumalo, Sizwe Ndlela, Skhumbuzo Mthombeni, Smangaliso Zulu, Sphiwe Clement Mthembu, Sphiwe Ntuli, Talente Ntimbane, Thabile Zondi, Thandeka Khoza, Thengokwakhe Nkosi, Thokozani Bhengu, Thokozani Simelane, Tshwaraganang Modise, Tumi Madolo, Velile Vellem, Welcome Petros Mthembu, Xolani Mkhize, Zamashandu Mbatha, Zinhle Buthelezi, Zinhle Mthembu, Zizile Sikhosana:
Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa. npj Digit. Medicine 4 (2021) - [j10]Jana Fehr, Stefan Konigorski, Stephen Olivier, Resign Gunda, Ashmika Surujdeen, Dickman Gareta, Theresa Smit, Kathy Baisley, Sashen Moodley, Yumna Moosa, Willem Hanekom, Olivier Koole, Thumbi Ndung'u, Deenan Pillay, Alison D. Grant, Mark J. Siedner, Christoph Lippert, Emily B. Wong, Anand Ramnanan, Anele Mkhwanazi, Antony Rapulana, Anupa Singh, Ashentha Govender, Ayanda Zungu, Boitsholo Mfolo, Bongani Magwaza, Bongumenzi Ndlovu, Clive Mavimbela, Costa Criticos, Day Munatsi, Dilip Kalyan, Doctar Mlambo, Fezeka Mfeka, Freddy Mabetlela, Gregory Ording-Jespersen, Hannah Keal, Hlengiwe Dlamini, Hlengiwe Khathi, Hlobisile Chonco, Hlobisile Gumede, Hlolisile Khumalo, Hloniphile Ngubane, Hollis Shen, Hosea Kambonde, Innocentia Mpofana, Jabu Kwinda, Jaco Dreyer, Jade Cousins, Jaikrishna Kalideen, Janet Seeley, Kandaseelan Chetty, Kayleen Brien, Kennedy Nyamande, Kgaugelo Moropane, Khabonina Malomane, Khadija Khan, Khanyisani Buthelezi, Kimeshree Perumal, Kobus Herbst, Lindani Mthembu, Logan Pillay, Mandisi Dlamini, Mandlakayise Zikhali, Mbali Mbuyisa, Mbuti Mofokeng, Melusi Sibiya, Mlungisi Dube, Mosa Suleman, Mpumelelo Steto, Mzamo Buthelezi, Nagavelli Padayachi, Nceba Gqaleni, Ngcebo Mhlongo, Nokukhanya Ntshakala, Nomathamsanqa Majozi, Nombuyiselo Zondi, Nomfundo Luthuli, Nomfundo Ngema, Nompilo Buthelezi, Nonceba Mfeka, Nondumiso Khuluse, Nondumiso Mabaso, Nondumiso Zitha, Nonhlanhla Mfekayi, Nonhlanhla Mzimela, Nozipho Mbonambi, Ntombiyenhlanhla Mkhwanazi, Ntombiyenkosi Ntombela, Pamela Ramkalawon, Pfarelo Tshivase, Phakamani Mkhwanazi, Philippa Mathews, Phumelele Mthethwa, Phumla Ngcobo, Ramesh Jackpersad, Raynold Zondo, Rochelle Singh, Rose Myeni, Sanah Bucibo, Sandile Mthembu, Sashin Harilall, Senamile Makhari, Seneme Mchunu, Senzeni Mkhwanazi, Sibahle Gumbi, Siboniso Nene, Sibusiso Mhlongo, Sibusiso Mkhwanazi, Sibusiso Nsibande, Simphiwe Ntshangase, Siphephelo Dlamini, Sithembile Ngcobo, Siyabonga Nsibande, Siyabonga Nxumalo, Sizwe Ndlela, Skhumbuzo Mthombeni, Smangaliso Zulu, Sphiwe Clement Mthembu, Sphiwe Ntuli, Talente Ntimbane, Thabile Zondi, Thandeka Khoza, Thengokwakhe Nkosi, Thokozani Bhengu, Thokozani Simelane, Tshwaraganang Modise, Tumi Madolo, Velile Vellem, Welcome Petros Mthembu, Xolani Mkhize, Zamashandu Mbatha, Zinhle Buthelezi, Zinhle Mthembu, Zizile Sikhosana:
Publisher Correction: Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa. npj Digit. Medicine 4 (2021) - [c11]Aiham Taleb, Christoph Lippert, Tassilo Klein, Moin Nabi:
Multimodal Self-supervised Learning for Medical Image Analysis. IPMI 2021: 661-673 - [c10]Alexander Rakowski, Christoph Lippert:
Disentanglement and Local Directions of Variance. ECML/PKDD (3) 2021: 19-34 - [c9]Jonas Kordt, Paul Brachmann, Daniel Limberger, Christoph Lippert:
Interactive Volumetric Region Growing for Brain Tumor Segmentation on MRI using WebGL. Web3D 2021: 2:1-2:8 - [i11]Matthias Kirchler, Martin Graf, Marius Kloft, Christoph Lippert:
Explainability Requires Interactivity. CoRR abs/2109.07869 (2021) - [i10]Yamen Ali, Aiham Taleb, Marina M.-C. Höhne, Christoph Lippert:
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout. CoRR abs/2109.14288 (2021) - [i9]Aiham Taleb, Matthias Kirchler, Remo Monti, Christoph Lippert:
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics. CoRR abs/2111.13424 (2021) - [i8]Benjamin Bergner, Csaba Rohrer, Aiham Taleb, Martha Duchrau, Guilherme De Leon, Jonas Almeida Rodrigues, Falk Schwendicke, Joachim Krois, Christoph Lippert:
Interpretable and Interactive Deep Multiple Instance Learning for Dental Caries Classification in Bitewing X-rays. CoRR abs/2112.09694 (2021) - 2020
- [j9]Mina Rezaei, Janne J. Näppi, Christoph Lippert, Christoph Meinel, Hiroyuki Yoshida:
Generative multi-adversarial network for striking the right balance in abdominal image segmentation. Int. J. Comput. Assist. Radiol. Surg. 15(11): 1847-1858 (2020) - [c8]Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert:
Two-sample Testing Using Deep Learning. AISTATS 2020: 1387-1398 - [c7]Mina Rezaei, Tomoki Uemura, Janne Näppi, Hiroyuki Yoshida, Christoph Lippert, Christoph Meinel:
Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis. Medical Imaging: Computer-Aided Diagnosis 2020 - [c6]Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch:
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. NeurIPS 2020 - [c5]Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gärtner, Benjamin Bergner, Christoph Lippert:
3D Self-Supervised Methods for Medical Imaging. NeurIPS 2020 - [i7]Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch:
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties. CoRR abs/2005.11110 (2020) - [i6]Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gärtner, Benjamin Bergner, Christoph Lippert:
3D Self-Supervised Methods for Medical Imaging. CoRR abs/2006.03829 (2020)
2010 – 2019
- 2019
- [i5]Matthias Kirchler, Shahryar Khorasani, Marius Kloft, Christoph Lippert:
Two-sample Testing Using Deep Learning. CoRR abs/1910.06239 (2019) - [i4]Aiham Taleb, Christoph Lippert, Tassilo Klein, Moin Nabi:
Multimodal Self-Supervised Learning for Medical Image Analysis. CoRR abs/1912.05396 (2019) - 2018
- [j8]Seunghak Lee, Nico Görnitz, Eric P. Xing, David Heckerman, Christoph Lippert:
Ensembles of Lasso Screening Rules. IEEE Trans. Pattern Anal. Mach. Intell. 40(12): 2841-2852 (2018) - [i3]Stefan Konigorski, Shahryar Khorasani, Christoph Lippert:
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease. CoRR abs/1812.00448 (2018) - 2017
- [j7]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse probit linear mixed model. Mach. Learn. 106(9-10): 1621-1642 (2017) - 2016
- [c4]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft:
Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI 2016: 48-58 - 2015
- [j6]Christoph Lippert, David Heckerman:
Computational and statistical issues in personalized medicine. XRDS 21(4): 24-27 (2015) - [i2]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse Estimation in a Correlated Probit Model. CoRR abs/1507.04777 (2015) - 2014
- [j5]Christoph Lippert, Jing Xiang, Danilo Horta, Christian Widmer, Carl Myers Kadie, David Heckerman, Jennifer Listgarten:
Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinform. 30(22): 3206-3214 (2014) - 2013
- [j4]Barbara Rakitsch, Christoph Lippert, Oliver Stegle, Karsten M. Borgwardt:
A Lasso multi-marker mixed model for association mapping with population structure correction. Bioinform. 29(2): 206-214 (2013) - [j3]Nicoló Fusi, Christoph Lippert, Karsten M. Borgwardt, Neil D. Lawrence, Oliver Stegle:
Detecting regulatory gene-environment interactions with unmeasured environmental factors. Bioinform. 29(11): 1382-1389 (2013) - [j2]Jennifer Listgarten, Christoph Lippert, Eun Yong Kang, Jing Xiang, Carl Myers Kadie, David Heckerman:
A powerful and efficient set test for genetic markers that handles confounders. Bioinform. 29(12): 1526-1533 (2013) - [c3]Barbara Rakitsch, Christoph Lippert, Karsten M. Borgwardt, Oliver Stegle:
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals. NIPS 2013: 1466-1474 - 2012
- [i1]Dominik G. Grimm, Bastian Greshake, Stefan Kleeberger, Christoph Lippert, Oliver Stegle, Bernhard Schölkopf, Detlef Weigel, Karsten M. Borgwardt:
easyGWAS: An integrated interspecies platform for performing genome-wide association studies. CoRR abs/1212.4788 (2012) - 2011
- [c2]Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt:
Efficient inference in matrix-variate Gaussian models with \iid observation noise. NIPS 2011: 630-638 - 2010
- [j1]Christoph Lippert, Zoubin Ghahramani, Karsten M. Borgwardt:
Gene function prediction from synthetic lethality networks via ranking on demand. Bioinform. 26(7): 912-918 (2010)
2000 – 2009
- 2009
- [c1]Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten M. Borgwardt:
A kernel method for unsupervised structured network inference. AISTATS 2009: 368-375
Coauthor Index
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