Maximilian Igl
16 papers · 2018–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Renaissance Researcher (5) π Interdisciplinary Bridge π Academic Marathon (7) π Conference Polyglot (7) πΊοΈ Taxonomy Completionist (30)
πΊοΈ
Taxonomy Completionist
(30)
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Keyword Pioneer
π£
Hot Topic Early Bird
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Triple Crown
π€
Dynamic Duo
(11)
π
Conference Pioneer
π
Century Club
(16)
π
Trend Setter
ποΈ
Keyword Collector
(55)
π₯
Unstoppable
(5)
β‘
Prolific Year
(5)
Conferences
ICLR (6)
ICML (4)
NIPS (2)
CORL (1)
CVPR (1)
JMLR (1)
UAI (1)
Top co-authors
Keywords
generative model
(2)
evidence lower bound
(2)
variational inference
(2)
approximate inference
(1)
behavior cloning
(1)
transfer learning
(1)
domain generalization
(1)
information bottleneck
(1)
multitask learning
(1)
hierarchical reinforcement learning
(1)
trajectory tracking
(1)
markov decision process
(1)
covariate shift
(1)
partially observable markov decision process
(1)
continuous control
(1)
particle filtering
(1)
exploration exploitation tradeoff
(1)
bayes-optimal policy
(1)
locomotion control
(1)
multi-task learning
(1)
Papers
Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
CVPR 2025
STORM: Spatio-TempOral Reconstruction Model For Large-Scale Outdoor Scenes
ICLR 2025
Communicating via Markov Decision Processes
ICML 2022
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving
CORL 2022
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning
ICLR 2021
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
ICLR 2021
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
ICML 2021
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
JMLR 2021
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing
NIPS 2021
Multitask Soft Option Learning
UAI 2020
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
ICLR 2020
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
NIPS 2019
Tighter Variational Bounds are Not Necessarily Better
ICML 2018
Deep Variational Reinforcement Learning for POMDPs
ICML 2018
Auto-Encoding Sequential Monte Carlo
ICLR 2018
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
ICLR 2018