← Authors

Sebastian Tschiatschek

31 papers · 2013–2025 · 9 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (9) πŸƒ Academic Marathon (12) 🌈 Renaissance Researcher (6) 🐝 Cross-Pollinator (15) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (16) 🧭 Keyword Pioneer πŸ† Keyword Champion πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (137) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (31) πŸ”₯ Unstoppable (10) ⚑ Prolific Year (5)

Conferences

NIPS (10) ICML (5) AISTATS (4) IJCAI (4) AAAI (2) ICLR (2) INTERSPEECH (2) JMLR (1) UAI (1)

Top co-authors

Andreas Krause (9) Franz Pernkopf (6) Cheng Zhang (6) JosΓ© Miguel HernΓ‘ndez-Lobato (5) Katja Hofmann (5) David Lindner (3) Kamil Ciosek (3) Robert Peharz (3) Adish Singla (3) Josip Djolonga (3)

Research topics

Applications (1)

Keywords

variational inference (4) variational autoencoder (4) submodular function (4) probabilistic modeling (3) active learning (3) inverse reinforcement learning (3) graphical model (3) reinforcement learning (3) bayesian deep learning (2) greedy algorithm (2) generative model (2) maximum margin (2) bayesian network (2) discriminative learning (2) uncertainty quantification (2) submodular optimization (2) sample complexity (2) preference learning (2) reward function (2) conditional random field (2)

Papers

Breaking the Reclustering Barrier in Centroid-based Deep Clustering ICLR 2025
On Constant Regret for Low-Rank MDPs UAI 2025
Rule-Guided Reinforcement Learning Policy Evaluation and Improvement IJCAI 2025
Learning Safety Constraints from Demonstrations with Unknown Rewards AISTATS 2024
Resource-Efficient Neural Networks for Embedded Systems JMLR 2024
Option Transfer and SMDP Abstraction with Successor Features IJCAI 2022
Interactively Learning Preference Constraints in Linear Bandits ICML 2022
Information Directed Reward Learning for Reinforcement Learning NIPS 2021
Sequential Generative Exploration Model for Partially Observable Reinforcement Learning AAAI 2021
Educational Question Mining At Scale: Prediction, Analysis and Personalization AAAI 2021
Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces IJCAI 2021
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data NIPS 2020
AMRL: Aggregated Memory For Reinforcement Learning ICLR 2020
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints NIPS 2019
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model NIPS 2019
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck NIPS 2019
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE ICML 2019
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning NIPS 2019
Differentiable Submodular Maximization IJCAI 2018
Teaching Inverse Reinforcement Learners via Features and Demonstrations NIPS 2018
Frame and Segment Level Recurrent Neural Networks for Phone Classification INTERSPEECH 2017
Guarantees for Greedy Maximization of Non-submodular Functions with Applications ICML 2017
Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification INTERSPEECH 2016
Variational Inference in Mixed Probabilistic Submodular Models NIPS 2016
Actively Learning Hemimetrics with Applications to Eliciting User Preferences ICML 2016
Cooperative Graphical Models NIPS 2016
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation AISTATS 2016
On Theoretical Properties of Sum-Product Networks AISTATS 2015
Learning Mixtures of Submodular Functions for Image Collection Summarization NIPS 2014
On the Asymptotic Optimality of Maximum Margin Bayesian Networks AISTATS 2013
The Most Generative Maximum Margin Bayesian Networks ICML 2013