← Authors

Stefan Bauer

40 papers · 2016–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (13) 🧭 Keyword Pioneer 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (9) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (7) πŸ† Keyword Champion (3) πŸ”¬ Deep Specialist (11) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (23) πŸ† Grand Slam πŸ’Ž Century Club (40) ❓ The Questioner ⚑ Prolific Year (6) πŸ”₯ Unstoppable (10) πŸ—ƒοΈ Keyword Collector (129)

Conferences

ICML (12) ICLR (10) NIPS (10) AAAI (2) UAI (2) ACL (1) AISTATS (1) CORL (1) JMLR (1)

Top co-authors

Bernhard SchΓΆlkopf (23) Francesco Locatello (8) Manuel Wuthrich (8) Yashas Annadani (7) Olivier Bachem (6) Yarin Gal (5) Frederik TrΓ€uble (4) Philippe Wenk (4) Andreas Krause (4) Andrea Dittadi (4)

Keywords

causal inference (7) disentangled representation (6) representation learning (5) gaussian process (4) variational inference (3) inductive bia (3) parameter inference (3) unsupervised learning (3) time series (3) dynamical system (3) ordinary differential equation (2) stochastic differential equation (2) directed acyclic graph (2) transfer learning (2) change point detection (2) generative model (2) bayesian inference (2) causal discovery (2) state inference (2) gradient matching (2)

Papers

Measuring temporal effects of agent knowledge by date-controlled tool use ACL 2025
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models ICLR 2025
Amortized Active Causal Induction with Deep Reinforcement Learning NIPS 2024
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery ICML 2024
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware ICLR 2023
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery NIPS 2023
Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery NIPS 2023
DiscoBAX: Discovery of optimal intervention sets in genomic experiment design ICML 2023
Differentiable Multi-Target Causal Bayesian Experimental Design ICML 2023
Structure by Architecture: Structured Representations without Regularization ICLR 2023
DRCFS: Doubly Robust Causal Feature Selection ICML 2023
Diffusion Based Representation Learning ICML 2023
Bayesian structure learning with generative flow networks UAI 2022
Exploring the Latent Space of Autoencoders with Interventional Assays NIPS 2022
Interventions, Where and How? Experimental Design for Causal Models at Scale NIPS 2022
The Role of Pretrained Representations for the OOD Generalization of RL Agents ICLR 2022
GeneDisco: A Benchmark for Experimental Design in Drug Discovery ICLR 2022
Adaptive Gaussian Process Change Point Detection ICML 2022
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning ICLR 2021
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling ICLR 2021
Function Contrastive Learning of Transferable Meta-Representations ICML 2021
On Disentangled Representations Learned from Correlated Data ICML 2021
On the Transfer of Disentangled Representations in Realistic Settings ICLR 2021
Spatially Structured Recurrent Modules ICLR 2021
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems AAAI 2020
Disentangling Factors of Variations Using Few Labels ICLR 2020
TriFinger: An Open-Source Robot for Learning Dexterity CORL 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation JMLR 2020
Bayesian Online Prediction of Change Points UAI 2020
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves AAAI 2020
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset NIPS 2019
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness ICML 2019
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs AISTATS 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations ICML 2019
On the Fairness of Disentangled Representations NIPS 2019
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs ICML 2019
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models NIPS 2018
Scalable Variational Inference for Dynamical Systems NIPS 2017
Efficient and Flexible Inference for Stochastic Systems NIPS 2017
The Arrow of Time in Multivariate Time Series ICML 2016