Sebastian Tschiatschek
31 papers · 2013–2025 · 9 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
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🗺️ Taxonomy Completionist (56) 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Half 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Grand Slam
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Keyword Pioneer
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Renaissance Researcher
(5)
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Unstoppable · Silver
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Prolific Year · Productive
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Conference Pioneer
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Century Club · Bronze
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Trend Setter
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Keyword Collector
(151)
Venues
Top co-authors
Research topics
Machine Learning
(25)
Learning Types
(14)
Artificial Intelligence
(13)
Optimization & Theory
(13)
Core Methods
(10)
Reinforcement Learning
(9)
Core AI
(9)
Deep Learning
(8)
Methods
(8)
Mathematics & Optimization
(5)
Bayesian & Probabilistic
(4)
Models
(4)
Optimization
(4)
Applications
(3)
Architectures
(3)
Keywords
submodular function
(4)
variational inference
(4)
variational autoencoder
(4)
reinforcement learning
(4)
active learning
(3)
graphical model
(3)
probabilistic modeling
(3)
inverse reinforcement learning
(3)
greedy algorithm
(2)
bayesian network
(2)
reward function
(2)
discriminative learning
(2)
sample efficiency
(2)
maximum margin
(2)
preference learning
(2)
deep clustering
(2)
submodular optimization
(2)
sample complexity
(2)
uncertainty quantification
(2)
generative model
(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
Interactively Learning Preference Constraints in Linear Bandits
ICML 2022
Option Transfer and SMDP Abstraction with Successor Features
IJCAI 2022
Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces
IJCAI 2021
Educational Question Mining At Scale: Prediction, Analysis and Personalization
AAAI 2021
Information Directed Reward Learning for Reinforcement Learning
NeurIPS 2021
Sequential Generative Exploration Model for Partially Observable Reinforcement Learning
AAAI 2021
AMRL: Aggregated Memory For Reinforcement Learning
ICLR 2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
NeurIPS 2020
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
ICML 2019
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
NeurIPS 2019
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
NeurIPS 2019
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
NeurIPS 2019
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
NeurIPS 2019
Differentiable Submodular Maximization
IJCAI 2018
Teaching Inverse Reinforcement Learners via Features and Demonstrations
NeurIPS 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
Actively Learning Hemimetrics with Applications to Eliciting User Preferences
ICML 2016
Cooperative Graphical Models
NeurIPS 2016
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
AISTATS 2016
Variational Inference in Mixed Probabilistic Submodular Models
NeurIPS 2016
Virtual Adversarial Training Applied to Neural Higher-Order Factors for Phone Classification
Interspeech 2016
On Theoretical Properties of Sum-Product Networks
AISTATS 2015
Learning Mixtures of Submodular Functions for Image Collection Summarization
NeurIPS 2014
On the Asymptotic Optimality of Maximum Margin Bayesian Networks
AISTATS 2013
The Most Generative Maximum Margin Bayesian Networks
ICML 2013