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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Information-Theoretic Diffusion.\n \n \n \n \n\n\n \n Kong, X.; Brekelmans, R.; and Ver Steeg, G.\n\n\n \n\n\n\n In The Eleventh International Conference on Learning Representations, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"Information-TheoreticPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{konginformation,\n  title={Information-Theoretic Diffusion},\n  author={Kong, Xianghao and Brekelmans, Rob and Ver Steeg, Greg},\n  booktitle={The Eleventh International Conference on Learning Representations},\n  year = {2023},\n  url={https://arxiv.org/abs/2302.03792}\n}\n
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\n  \n 2022\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples.\n \n \n \n \n\n\n \n Neklyudov, K.; Brekelmans, R.; Severo, D.; and Makhzani, A.\n\n\n \n\n\n\n arXiv preprint arXiv:2210.06662. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"ActionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{neklyudov2022action,\n  title={Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples},\n  author={Neklyudov, Kirill and Brekelmans, Rob and Severo, Daniel and Makhzani, Alireza},\n  journal={arXiv preprint arXiv:2210.06662},\n  year={2022},\n  url={https://arxiv.org/abs/2210.06662}\n}\n
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\n \n\n \n \n \n \n \n \n Rho-Tau Bregman Information and the Geometry of Annealing Paths.\n \n \n \n \n\n\n \n Brekelmans, R.; and Nielsen, F.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Rho-TauPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{brekelmans2022rhotau,\n  url = {https://arxiv.org/abs/2209.07481},\n  author = {Brekelmans, Rob and Nielsen, Frank},\n  title = {Rho-Tau Bregman Information and the Geometry of Annealing Paths},\n  publisher = {arXiv},\n  year = {2022}\n}\n
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\n \n\n \n \n \n \n \n \n Improving Mutual Information Estimation with Annealed and Energy-Based Bounds.\n \n \n \n \n\n\n \n Brekelmans, R.; Huang, S.; Ghassemi, M.; Steeg, G. V.; Grosse, R. B.; and Makhzani, A.\n\n\n \n\n\n\n In International Conference on Learning Representations, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"ImprovingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\nbrekelmans2022improving,\ntitle={Improving Mutual Information Estimation with Annealed and Energy-Based Bounds},\nauthor={Rob Brekelmans and Sicong Huang and Marzyeh Ghassemi and Greg Ver Steeg and Roger Baker Grosse and Alireza Makhzani},\nbooktitle={International Conference on Learning Representations},\nyear={2022},\nurl={https://openreview.net/forum?id=T0B9AoM_bFg}\n}\n
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\n \n\n \n \n \n \n \n \n Your Policy Regularizer is Secretly an Adversary.\n \n \n \n \n\n\n \n Brekelmans, R.; Genewein, T.; Grau-Moya, J.; Delétang, G.; Kunesch, M.; Legg, S.; and Ortega, P.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"YourPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{brekelmans2022policy,\n      title={Your Policy Regularizer is Secretly an Adversary}, \n      author={Rob Brekelmans and Tim Genewein and Jordi Grau-Moya and Grégoire Delétang and Markus Kunesch and Shane Legg and Pedro Ortega},\n      year={2022},\n      eprint={2203.12592},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG},\n      url={https://arxiv.org/abs/2203.12592}\n}\n
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\n  \n 2021\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Model-Free Risk-Sensitive Reinforcement Learning.\n \n \n \n \n\n\n \n Delétang, G.; Grau-Moya, J.; Kunesch, M.; Genewein, T.; Brekelmans, R.; Legg, S.; and Ortega, P. A\n\n\n \n\n\n\n arXiv preprint arXiv:2111.02907. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Model-FreePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{deletang2021model,\n  title={Model-Free Risk-Sensitive Reinforcement Learning},\n  author={Del{\\'e}tang, Gr{\\'e}goire and Grau-Moya, Jordi and Kunesch, Markus and Genewein, Tim and Brekelmans, Rob and Legg, Shane and Ortega, Pedro A},\n  journal={arXiv preprint arXiv:2111.02907},\n  url={https://arxiv.org/abs/2111.02907},\n  year={2021}\n}\n
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\n \n\n \n \n \n \n \n \n q-Paths: Generalizing the geometric annealing path using power means.\n \n \n \n \n\n\n \n Masrani, V.; Brekelmans, R.; Bui, T.; Nielsen, F.; Galstyan, A.; Ver Steeg, G.; and Wood, F.\n\n\n \n\n\n\n In Uncertainty in Artificial Intelligence, pages 1938–1947, 2021. PMLR\n \n\n\n\n
\n\n\n\n \n \n \"q-Paths:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{masrani2021q,\n  title={q-Paths: Generalizing the geometric annealing path using power means},\n  author={Masrani, Vaden and Brekelmans, Rob and Bui, Thang and Nielsen, Frank and Galstyan, Aram and Ver Steeg, Greg and Wood, Frank},\n  booktitle={Uncertainty in Artificial Intelligence},\n  pages={1938--1947},\n  year={2021},\n  organization={PMLR},\n  url={https://arxiv.org/abs/2107.00745}\n}\n
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\n  \n 2020\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Annealed Importance Sampling with q-Paths.\n \n \n \n \n\n\n \n Brekelmans, R.; Masrani, V.; Bui, T.; Wood, F.; Galstyan, A.; Steeg, G. V.; and Nielsen, F.\n\n\n \n\n\n\n NeurIPS Workshop on Deep Learning through Information Geometry. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AnnealedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{brekelmans2020qpaths,\n  title={Annealed Importance Sampling with q-Paths},\n  author={Brekelmans, Rob and Masrani, Vaden and Bui, Thang and Wood, Frank and Galstyan, Aram and Steeg, Greg Ver and Nielsen, Frank},\n  journal={NeurIPS Workshop on Deep Learning through Information Geometry},\n  url={https://openreview.net/forum?id=ZBJ20FRVPD},\n  year={2020}\n }\n
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\n \n\n \n \n \n \n \n \n Likelihood Ratio Exponential Families.\n \n \n \n \n\n\n \n Brekelmans, R.; Nielsen, F.; Makhzani, A.; Galstyan, A.; and Steeg, G. V.\n\n\n \n\n\n\n NeurIPS Workshop on Deep Learning through Information Geometry. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"LikelihoodPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{brekelmans2020lref,\n  title={Likelihood Ratio Exponential Families},\n  author={Brekelmans, Rob and Nielsen, Frank and Makhzani, Alireza and Galstyan, Aram and Steeg, Greg Ver},\n  journal={NeurIPS Workshop on Deep Learning through Information Geometry},\n  url={https://openreview.net/forum?id=RoTADibt26_},\n  year={2020}\n }\n
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\n \n\n \n \n \n \n \n \n All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference.\n \n \n \n \n\n\n \n Brekelmans, R.; Masrani, V.; Wood, F.; Steeg, G. V.; and Galstyan, A.\n\n\n \n\n\n\n International Conference on Machine Learning. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"AllPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{brekelmans2020all,\n  title={All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference},\n  author={Brekelmans, Rob and Masrani, Vaden and Wood, Frank and Steeg, Greg Ver and Galstyan, Aram},\n  journal={International Conference on Machine Learning},\n  url={https://arxiv.org/abs/2007.00642},\n  year={2020}\n }\n
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\n  \n 2019\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Exact Rate-Distortion in Autoencoders via Echo Noise.\n \n \n \n \n\n\n \n Brekelmans, R.; Moyer, D.; Galstyan, A.; and Steeg, G. V.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"ExactPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{brekelmans2019exact,\n  title={Exact Rate-Distortion in Autoencoders via Echo Noise},\n  author={Brekelmans, Rob and Moyer, Daniel and Galstyan, Aram and Steeg, Greg Ver},\n  booktitle={Advances in Neural Information Processing Systems},\n  url={https://arxiv.org/abs/1904.07199},\n  year={2019}\n}\n
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\n \n\n \n \n \n \n \n \n Discovery and Separation of Features for Invariant Representation Learning.\n \n \n \n \n\n\n \n Jaiswal, A. B.; Rob; Moyer, D. S.; and Greg Ver; AbdAlmageed, W. N.\n\n\n \n\n\n\n In 2019. \n \n\n\n\n
\n\n\n\n \n \n \"DiscoveryPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{jaiswal2019dsf,\n  title={Discovery and Separation of Features for Invariant Representation Learning},\n  author={Jaiswal, Ayush; Brekelmans, Rob; Moyer, Daniel; Steeg, Greg Ver; AbdAlmageed, Wael; Natarajan, Premkumar},\n  booktitle={},\n  url={https://arxiv.org/abs/1912.00646},\n  year={2019}\n}\n
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\n \n\n \n \n \n \n \n \n Auto-Encoding Total Correlation Explanation.\n \n \n \n \n\n\n \n Gao, S.; Brekelmans, R.; Ver Steeg, G.; and Galstyan, A.\n\n\n \n\n\n\n In The 22nd International Conference on Artificial Intelligence and Statistics, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Auto-EncodingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{gao2019auto,\n  title={Auto-Encoding Total Correlation Explanation},\n  author={Gao, Shuyang and Brekelmans, Rob and Ver Steeg, Greg and Galstyan, Aram},\n  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},\n  url={https://arxiv.org/abs/1802.05822},\n  year={2019}\n}\n
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\n  \n 2018\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Invariant representations without adversarial training.\n \n \n \n \n\n\n \n Moyer, D.; Gao, S.; Brekelmans, R.; Galstyan, A.; and Ver Steeg, G.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems, 2018. \n \n\n\n\n
\n\n\n\n \n \n \"InvariantPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{moyer2018invariant,\n  title={Invariant representations without adversarial training},\n  author={Moyer, Daniel and Gao, Shuyang and Brekelmans, Rob and Galstyan, Aram and Ver Steeg, Greg},\n  booktitle={Advances in Neural Information Processing Systems},\n  url={https://arxiv.org/abs/1805.09458},\n  year={2018}\n}\n
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\n \n\n \n \n \n \n \n \n Disentangled representations via synergy minimization.\n \n \n \n \n\n\n \n Ver Steeg, G.; Brekelmans, R.; Harutyunyan, H.; and Galstyan, A.\n\n\n \n\n\n\n In 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 180–187, 2017. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"DisentangledPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{ver2017disentangled,\n  title={Disentangled representations via synergy minimization},\n  author={Ver Steeg, Greg and Brekelmans, Rob and Harutyunyan, Hrayr and Galstyan, Aram},\n  booktitle={2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)},\n  pages={180--187},\n  url={https://arxiv.org/abs/1710.03839},\n  year={2017},\n  organization={IEEE}\n}\n
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