PhD candidate

University of Amsterdam

I am a third-year PhD student in Machine Learning at the University of Amsterdam, advised by Prof. Max Welling. My main area of interest is learning with structured data and structured representations/computations. This includes topics in reasoning, (multi-agent) reinforcement learning, and structured deep generative models.

My formal background is in Physics (M.Sc. hons. 2016, B.Sc. 2014 at FAU). During my studies, I have had exposure to a number of fields and—after a short interlude in Neuroscience-related research at the Max Planck Institute for Brain Research—eventually found my way into Machine Learning.

- 12/2018: My DeepMind internship project Compositional Imitation Learning: Explaining and executing one task at a time is accepted as a contributed talk at the Learning by Instruction Workshop at NeurIPS 2018.
- 06/2018: One accepted paper at UAI 2018: Hyperspherical Variational Auto-Encoders (plenary talk), a short paper at MIDL 2018 on Extraction of Airways using Graph Neural Networks and a spotlight paper at the KDD Deep Learning Day 2018 on Graph Convolutional Matrix Completion
- 06/2018: Our work on Relational GCNs has won the best student research paper award at ESWC 2018.
- 05/2018: Our work on learning latent interaction graphs: Neural Relational Inference for Interacting Systems is accepted as a conference paper at ICML 2018.
- 03/2018: I will be joining Google DeepMind for a summer internship in 2018.
- 03/2018: Our paper Modeling Relational Data with Graph Convolutional Networks is accepted as a conference paper at ESWC 2018.
- 2017: In the summer of 2017, I interned at Apple in Carlos Guestrin's Machine Learning team. I worked on self-attention models for sequence tagging and classification.

Unsupervised, differentiable sequence segmentation for option discovery.

T. Kipf, Y. Li, H. Dai, V. Zambaldi, E. Grefenstette, P. Kohli, P. Battaglia, Compositional Imitation Learning: Explaining and executing one task at a time, (NeurIPS Learning By Instruction Workshop 2018), Contributed Talk [Link, PDF (arXiv)].

Learning the latent interaction graph of a dynamical system

T. Kipf*, E. Fetaya*, K. Wang, M. Welling, R. Zemel, Neural Relational Inference for Interacting Systems, (ICML 2018) [Link, PDF (arXiv), code], *equal contribution.

Learning to generate molecular graphs using a combined GAN/RL-based objective

N. De Cao, T. Kipf, MolGAN: An implicit generative model for small molecular graphs, ICML Deep Generative Models Workshop (2018) [Link, PDF (arXiv), code].

Learning hyperspherical latent spaces

T. R. Davidson*, L. Falorsi*, N. De Cao*, T. Kipf, J. M. Tomczak, Hyperspherical Variational Auto-Encoders, (UAI 2018), Plenary Talk [Link, PDF (arXiv), code, blog], *equal contribution.

Graph refinement with graph neural nets

R. Selvan, T. Kipf, M. Welling, J. H. Pedersen, J. Petersen, M. de Bruijne, Extraction of Airways using Graph Neural Networks, (MIDL 2018 - short paper) [Link, PDF (arXiv)].

Link prediction and entity classification on knowledge graphs

M. Schlichtkrull*, T. N. Kipf*, P. Bloem, R. vd Berg, I. Titov, M. Welling, Modeling Relational Data with Graph Convolutional Networks, (ESWC 2018), Best Student Research Paper [Link, PDF (arXiv), code], *equal contribution.

Collaborative filtering with side information in bipartite graphs

R. vd Berg, T. N. Kipf, M. Welling, Graph Convolutional Matrix Completion, KDD Deep Learning Day (2018), Spotlight Talk [Link, PDF (arXiv), code]

Neural networks for node classification on graphs

T. N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017) [Link, PDF (arXiv), code, blog]

A latent variable model for graph-structured data

T. N. Kipf, M. Welling, Variational Graph Auto-Encoders, (NeurIPS Bayesian Deep Learning Workshop 2016) [Link, PDF (arXiv), code]

M.Sc. thesis (2016), Department of Connectomics, MPI for Brain Research

Thesis copy available here: [PDF (9 MB)]

For a full list, have a look at my Google Scholar page.