We are excited to announce that our paper “Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning” by Simon Rittel and Sebastian Tschiatschek has been accepted at the European Conference on Artificial Intelligence (ECAI 2023).

Have a look at our paper to learn how to incorporate prior knowledge about individual edges and causal dependencies in deep learning based Bayesian approaches to causal structure learning via the prior belief.

Congrats Simon!

We are excited to announce that our paper “Posterior Consistency for Missing Data in Variational Autoencoders” by Timur Sudak and Sebastian Tschiatschek has been accepted at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML/PKDD 2023).

Have a look at our paper to learn how to regularize a variational autoencoder’s posterior in the face of missing data to achieve better performance.

Congrats Timur!

We are proud to announce that our paper “On the Impact of Explanations on Understanding of Algorithmic Decision-Making” by Timothée Schmude, Laura Koesten, Torsten Möller and Sebastian Tschiatschek has been accepted at this year’s ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT).

A preprint of our paper is available on here.

Congrats Tim!

Our paper “Learning Constraints From Human Stop-feedback in Reinforcement Learning” by Silvia Poletti, Alberto Testolin, and Sebastian Tschiatschek has been accepted for publication as an extended abstract in this year’s International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Congrats Sylvia!

I wrote an article for Der Standard about the influence of algorithms on our society and how explainable artifical intelligence might help us to evaluate them in democratic processes. You can find the article (in German) here. Make sure to leave a comment.

I was a panelist in the workshop on “Complex systems and their compatibility with democratic processes” organized by the Vienna Science and Technology Fund (WWTF) as part of the Digital Days 2022.

Further details can be found here.

Thanks for having me!

The paper “Replication Robust Payoff Allocation in Submodular Cooperative Games” by Han Dongge, Michael Wooldridge, Alex Rogers, Olga Ohrimenko, and Sebastian Tschiatschek has been accepted for publication in the IEEE Transactions on Artificial Intelligence.

Have a look at our paper if you want to learn more about payoff allocation in submodular cooperative games and their applications in machine learning data markets.

Congrats Dongge!

One paper was accepted at IJCAI (long presentation):

• Han Dongge, Sebastian Tschiatschek, “Option Transfer and SMDP Abstraction with Successor Features”

  Congrats Dongge!

One paper was accepted at EDM:

• Sebastian Tschiatschek, Maria Knobelsdorf, Adish Singla, “Equity and Fairness of Bayesian Knowledge Tracing”

One paper was accepted at IJCAI:

• Lukas Miklautz, Lena Greta-Marie Bauer, Dominik Mautz, Sebastian Tschiatschek, Christian Böhm, Claudia Plant, “Details (Don’t) Matter: Isolating Cluster Information in Deep Embedded Spaces”

  Congrats Lukas and Lena!

Adish Singla and I will give a tutorial on Recent Advances in Reinforcement Learning for Human-AI Collaboration at this year’s IJCAI. Details will follow.

Our workshop on Human-AI Collaboration in Sequential Decision-Making was accepted at this year’s ICML. Please consider the call for papers.

One paper was accepted at AAAI and one at EAAI:

• Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, Jose Miguel Hernandez Lobato, Simon Peyton Jones, Richard Baraniuk, Cheng Zhang, “Educational Question Mining At Scale: Prediction, Analysis and Personalization”
• Haiyan Yin, Jianda Chen, Sinno Pan, Sebastian Tschiatschek, “Sequential Generative Exploration Model for Partially Observable Reinforcement Learning”

One paper accepted at this year’s NeurIPS:

• Chao Ma, Sebastian Tschiatschek, Richard E Turner, José Miguel Hernández-Lobato, Cheng Zhang, “VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data”

• I gave a leture/tutorial on Machine Learning at the Pattern Recognition in Neuroimaging Summer School 2020.

Two new preprints are available on arxiv:

• Dongge Han, Shruti Tople, Alex Rogers, Michael Wooldridge, Olga Ohrimenko, Sebastian Tschiatschek, “Replication-Robust Payoff-Allocation with Applications in Machine Learning Marketplaces”
• Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard Turner, Cheng Zhang, VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

Here’s an interesting article on a project I was involved in while being at Microsoft Research Cambridge: Link

I have joined the University of Vienna as assistant professor (tenure-track) of machine learning.

Our paper was accepted at AAMAS 2020:

• Ahana Ghosh, Sebastian Tschiatschek, Hamed Mahdavi, Adish Singla, “Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies”

The following paper was accepted at ICLR 2020:

• Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann, “AMRL: Aggregated Memory For Reinforcement Learning” [Video presentation]

We have four papers accepted at NeurIPS 2019!

• Sebastian Tschiatschek, Ahana Ghosh, Luis Haug, Rati Devidze, Adish Singla, “Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints”
• David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, Jose Miguel Hernandez-Lobato, Sebastian Tschiatschek, “Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning”
• Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann, “Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck”
• Wenbo Gong, Sebastian Tschiatschek, Sebastian Nowozin, Richard E Turner, Jose Miguel Hernandez-Lobato, Cheng Zhang, “Icebreaker: Efficient Information Acquisition with Active Learning”

Please check for updates of the papers from time to time as these are not yet the final versions.

We have one paper at the main conference and one paper in the Real-world Sequential Decision Making: Reinforcement Learning and Beyond workshop:

• Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang, “EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE”
  Talk: Wed Jun 12th 11:40 AM — 12:00 PM @ Hall A
  Poster: Wed Jun 12th 6:30 PM @ Pacific Ballroom
• Chao Ma, Cheng Zhang, Sebastian Tschiatschek, Jose Miguel Hernandez-Lobato, Richard E. Turner, Sebastian Nowozin, “Bayesian EDDI: Sequential Variable Selection with Bayesian Partial VAE”
  Poster: Fri Jun 14th, Real-world Sequential Decision Making: Reinforcement Learning and Beyond Workshop

We have one paper at the main conference and one paper in the deep reinforcement learning workshop:

• Luis Haug, Sebastian Tschiatschek, and Adish Singla, “Teaching Inverse Reinforcement Learners via Features and Demonstrations” Poster: Wed Dec 5th 05:00 – 07:00 PM @ Room 517 AB #167
• David Janz, Jiri Hron, José Miguel Hernández-Lobato, Katja Hofmann, and Sebastian Tschiatschek, “Successor Uncertainties: exploration and uncertainty in temporal difference learning” Poster: Fri Dec 7th, Deep RL Workshop