Empirical Inference


2022


no image
Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022)

Schölkopf, B., Uhler, C., Zhang, K.

177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)

link (url) [BibTex]

2022

link (url) [BibTex]

2021


no image
Proceedings of the 1st Workshop on NLP for Positive Impact

Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C.

Association for Computational Linguistics, August 2021 (proceedings)

link (url) [BibTex]

2021

link (url) [BibTex]


no image
Pulling back information geometry

Arvanitidis, G., González Duque, M., Pouplin, A., Kalatzis, D., Hauberg, S.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


A Robot Cluster for Reproducible Research in Dexterous Manipulation
A Robot Cluster for Reproducible Research in Dexterous Manipulation

Wüthrich*, M., Widmaier*, F., Bauer*, S., Funk, N., Urain, J., Peters, J., Watson, J., Chen, C., Srinivasan, K., Zhang, J., Zhang, J., Walter, M. R., Madan, R., Schaff, C., Maeda, T., Yoneda, T., Yarats, D., Allshire, A., Gordon, E. K., Bhattacharjee, T., Srinivasa, S. S., Garg, A., Buchholz, A., Stark, S., Steinbrenner, T., Akpo, J., Joshi, S., Agrawal, V., Schölkopf, B.

2021, *equal contribution (misc)

arXiv [BibTex]

arXiv [BibTex]


no image
Reinforcement Learning Algorithms: Analysis and Applications

Belousov, B., H., A., Klink, P., Parisi, S., Peters, J.

883, Studies in Computational Intelligence, Springer International Publishing, 2021 (book)

DOI [BibTex]

DOI [BibTex]


no image
Nonpar MANOVA via Independence Testing

Panda, S., Shen, C., Perry, R., Zorn, J., Lutz, A., Priebe, C. E., Vogelstein, J. T.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


no image
On the Impact of Stable Ranks in Deep Nets

Georgiev, B., Franken, L., Mukherjee, M., Arvanitidis, G.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


no image
Manifold forests: closing the gap on neural networks

Perry, R., Tomita, T. M., Mehta, R., Arroyo, J., Patsolic, J., Falk, B., Vogelstein, J. T.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


no image
Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities

Perry, R., Mehta, R., Guo, R., Yezerets, E., Arroyo, J., Powell, M., Helm, H., Shen, C., Vogelstein, J. T.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


no image
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger

Allshire, A., Mittal, M., Lodaya, V., Makoviychuk, V., Makoviichuk, D., Widmaier, F., Wüthrich, M., Bauer, S., Handa, A., Garg, A.

2021 (misc)

arXiv [BibTex]

arXiv [BibTex]


no image
Learning Neural Causal Models from Unknown Interventions

Ke, R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., Bengio, Y.

2020 (misc)

arXiv Project Page [BibTex]

arXiv Project Page [BibTex]

2018


no image
Die kybernetische Revolution

Schölkopf, B.

S{\"u}ddeutsche Zeitung, 2018, (15-Mar-2018) (misc)

link (url) [BibTex]

2018

link (url) [BibTex]


no image
Large sample analysis of the median heuristic

Garreau, D., Jitkrittum, W., Kanagawa, M.

2018 (misc) In preparation

arXiv [BibTex]

2017


no image
Elements of Causal Inference - Foundations and Learning Algorithms

Peters, J., Janzing, D., Schölkopf, B.

Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

PDF [BibTex]

2017

PDF [BibTex]

2016


no image
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI)

Ihler, A. T., Janzing, D.

pages: 869 pages, AUAI Press, June 2016 (proceedings)

link (url) [BibTex]

2016


no image
Empirical Inference (2010-2015)
Scientific Advisory Board Report, 2016 (misc)

pdf [BibTex]

pdf [BibTex]


no image
Unsupervised Domain Adaptation in the Wild : Dealing with Asymmetric Label Set

Mittal, A., Raj, A., Namboodiri, V. P., Tuytelaars, T.

2016 (misc)

Arxiv [BibTex]

2014


no image
Learning Motor Skills: From Algorithms to Robot Experiments

Kober, J., Peters, J.

97, pages: 191, Springer Tracts in Advanced Robotics, Springer, 2014 (book)

DOI [BibTex]

2014

DOI [BibTex]


no image
Computational Diffusion MRI and Brain Connectivity

Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O’Donnell, L., Panagiotaki, E.

pages: 255, Mathematics and Visualization, Springer, 2014 (book)

Web [BibTex]

Web [BibTex]


no image
Development of advanced methods for improving astronomical images

Schmeißer, N.

Eberhard Karls Universität Tübingen, Germany, Eberhard Karls Universität Tübingen, Germany, 2014 (diplomathesis)

[BibTex]

[BibTex]

2013


no image
Camera-specific Image Denoising

Schober, M.

Eberhard Karls Universität Tübingen, Germany, October 2013 (diplomathesis)

PDF [BibTex]

2013

PDF [BibTex]


no image
Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

Web [BibTex]

Web [BibTex]

2012


no image
Machine Learning and Interpretation in Neuroimaging - Revised Selected and Invited Contributions

Langs, G., Rish, I., Grosse-Wentrup, M., Murphy, B.

pages: 266, Springer, Heidelberg, Germany, International Workshop, MLINI, Held at NIPS, 2012, Lecture Notes in Computer Science, Vol. 7263 (proceedings)

DOI [BibTex]

2012

DOI [BibTex]


no image
MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication)

Panagiotaki, E., O’Donnell, L., Schultz, T., Zhang, G.

15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI , 2012 (proceedings)

PDF [BibTex]

PDF [BibTex]

2011


no image
Optimization for Machine Learning

Sra, S., Nowozin, S., Wright, S.

pages: 494, Neural information processing series, MIT Press, Cambridge, MA, USA, December 2011 (book)

Abstract
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Web [BibTex]

2011

Web [BibTex]


no image
Bayesian Time Series Models

Barber, D., Cemgil, A., Chiappa, S.

pages: 432, Cambridge University Press, Cambridge, UK, August 2011 (book)

[BibTex]

[BibTex]


no image
JMLR Workshop and Conference Proceedings Volume 19: COLT 2011

Kakade, S., von Luxburg, U.

pages: 834, MIT Press, Cambridge, MA, USA, 24th Annual Conference on Learning Theory , June 2011 (proceedings)

Web [BibTex]

Web [BibTex]