Sebastian Tschiatschek
31 papers · 2013–2025 · 9 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (6) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (16) π£ Hot Topic Early Bird
π
Interdisciplinary Bridge
πΊοΈ
Taxonomy Completionist
(16)
π§
Keyword Pioneer
π
Keyword Champion
π
Grand Slam
ποΈ
Keyword Collector
(137)
π
Conference Pioneer
π
Trend Setter
π
Century Club
(31)
π₯
Unstoppable
(10)
β‘
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
Research topics
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