Check out my Google Scholar profile for more details.

Conference publications

• On Mutual Information Maximization for Representation Learning
  (ICLR 2020, paper, arxiv)
  Michael Tschannen, Josip Djolonga, PKR, Sylvain Gelly, Mario Lucic

   

• Practical and Consistent Estimation of f-Divergences
  (NeurIPS 2019, paper, arxiv)
  PKR, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin

   

• The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
  (UAI 2019, paper, arxiv, supplement)
  Luigi Gresele*, PKR*, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf

   

• From Deterministic ODEs to Dynamic Structural Causal Models
  (UAI 2018, paper, supplement, arxiv)
  PKR, Stephan Bongers, Bernhard Schölkopf, Joris M. Mooij

   

• Causal consistency of structural equation models
  (UAI 2017 with oral presentation, paper, supplement, arxiv, presentation slides)
  PKR*, Sebastian Weichwald*, Stephan Bongers, Joris M. Mooij, Dominik Janzing, Moritz Grosse-Wentrup, Bernhard Schölkopf

   

• A kernel test for three-variable interactions with random processes
  (UAI 2016, paper, supplement, arxiv)
  PKR, Kacper P. Chwialkowski, Arthur Gretton

Preprints, workshop papers, etc

• Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
  (NeurIPS 2019 Workshop “Do the right thing”, arxiv)
  Julius von Kügelgen, PKR, Bernhard Schölkopf, Adrian Weller
  
  
• An Empirical Study of Generative Models with Encoders
  (2018, arxiv)
  PKR, Yunpeng Li, Dominik Roblek
  
  
• Wasserstein Auto-encoders: Latent Dimensionality and Random Encoders
  (ICLR workshop paper, 2018, poster, paper)
  PKR, Bernhard Schölkopf, Ilya Tolstikhin
  
  
• Learning Disentangled Representations with Wasserstein Auto-Encoders
  (ICLR workshop paper, 2018, poster, paper)
  PKR, Bernhard Schölkopf, Ilya Tolstikhin
  
  
• On the Latent Space of Wasserstein Auto-Encoders
  (2018, arxiv)
  PKR, Bernhard Schölkopf, Ilya Tolstikhin
  
  
• Probabilistic Active Learning of Functions in Structural Causal Models
  (2017, arxiv, workshop poster, slides)
  PKR, Ilya Tolstikhin, Philipp Hennig, Bernhard Schölkopf
  
  
• Masters thesis: Three Variable Kernel Independence Testing with Time Series (2015)
  
  
• The VC-Dimension of Similarity Hypothesis Spaces
  (2015, arxiv)
  Mark Herbster, PKR, James Townsend