Yashas Annadani
PhD Student, TU Munich and Helmholtz AI
FIRSTNAME [DOT] LASTNAME [AT] helmholtz-munich.de
Bio
I am a PhD student in Machine Learning at TU Munich and Helmholtz AI advised by Stefan Bauer. I am also part of the ELLIS doctoral program at Max Planck Institute for Intelligent Systems co-supervised by Bernhard Schölkopf. During the course of my PhD, I have also spent time at Stanford University, KTH Stockholm, Sweden and Microsoft Research Cambridge, UK. Before this, I completed my master's degree in Electrical Engineeting and Information Technology at ETH Zürich in 2021.
Publications/Preprints
Amortized Active Causal Induction with Deep Reinforcement Learning
Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
In advances of Neural Information Processing Systems (NeurIPS), 2024
BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery
Yashas Annadani*, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong*
In advances of Neural Information Processing Systems (NeurIPS), 2023
Trust Your Gradients: Gradient-based Intervention Targeting for Causal Discovery
Mateusz Olko*, Michal Zajac*, Aleksandra Nowak*, Nino Scherrer, Yashas Annadani, Stefan Bauer, Lukasz Kucinski, Piotr Milos
In advances of Neural Information Processing Systems (NeurIPS), 2023
Differentiable Multi-Target Causal Bayesian Experimental Design
Yashas Annadani*, Panagiotis Tigas*, Desi R. Ivanova, Andrew Jesson, Yarin Gal, Adam Foster^, Stefan Bauer^
In International Conference on Machine Learning (ICML), 2023
Structure by Architecture: Structured Representations without Regularization.
Felix Leeb, Giulia Lanzillotta, Yashas Annadani, Michel Besserve, Stefan Bauer, Bernhard Schölkopf
In International Conference on Learning Representations (ICLR), 2023
Interventions, Where and How? Experimental Design for Causal Models at Scale
Panagiotis Tigas*, Yashas Annadani*, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
In advances of Neural Information Processing Systems (NeurIPS), 2022
Learning Neural Causal Models with Active Interventions
Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael Curtis Mozer, Yoshua Bengio, Stefan Bauer, Nan Rosemary Ke
arxiv preprint: arXiv:2109.02429. In NeurIPS workshop Causal Inference & Machine Learning: Why now? (WHY-21), 2021
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer
arxiv preprint: arXiv:2106.07635. Oral at KDD Workshop on Bayesian causal inference for real world interactive systems, 2021
Preserving Semantic Relations for Zero-Shot Learning
Yashas Annadani, Soma Biswas
In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2018