Stefan Bauer

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

Achievements

+13 more ↓

🧭 Keyword Pioneer 🌍 Conference Polyglot (9) 🗺️ Taxonomy Completionist (13) 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (9)

🏃 Academic Marathon (9) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (7) 🏆 Keyword Champion (3) 🔬 Deep Specialist (11) 👑 Triple Crown 🤝 Dynamic Duo (23) 🏆 Grand Slam 💎 Century Club (40) ❓ The Questioner ⚡ Prolific Year (6) 🔥 Unstoppable (10) 🗃️ 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 Schölkopf (23) Francesco Locatello (8) Manuel Wuthrich (8) Yashas Annadani (7) Olivier Bachem (6) Yarin Gal (5) Frederik Träuble (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