Archived Talks

2018 — spring

Date Time Room Speaker Title (Link)
Aug 28 16:00-17:00 C3.163 Tameem Adel On interpretable representations and the tradeoff between accuracy and interpretability
Jul 30 11:00-12:00 C3.163 Jesse Bettencourt Neural Ordinary Differential Equations
Jul 26 11:00-12:00 C1.112 Dmitry Vetrov Interesting properties of the variational dropout framework
Jun-Aug - - - No regular talks
May 29 16:00-17:00 C3.163 Diederik Roijers Multiple objectives: because we (should) care about the user
May 22 16:00-17:00 C3.163 Taco Cohen The Quite General Theory of Equivariant Convolutional Networks
May 15 16:00-17:00 C3.163 Emiel Hoogeboom G-HexaConv
May 8 - - - No talk
May 1 - - - No talk (ICLR)
Apr 24 16:00-17:00 C3.163 Zeynep Akata Representing and Explaining Novel Concepts with Minimal Supervision
Apr 17 16:00-17:00 C3.163 Tineke Blom Causal Modeling for Dynamical Systems using Generalized Structural Causal Models
Apr 10 16:00-17:00 F1.02 Petar Veličković Keeping our graphs attentive
Apr 9 16:00-17:00 C1.112 Avital Oliver Realistic Evaluation of Semi-Supervised Learning Algorithms
Apr 3 16:00-17:00 C3.163 Karen Ullrich Variational Bayes Wake-Sleep algorithm for expressive latent representations in 3D protein reconstruction
Mar 27 16:00-17:00 C3.163 Wouter Kool Attention Solves Your TSP
Mar 20 16:00-17:00 C3.163 Paul Baireuther Quantum Error Correction with Recurrent Neural Networks
Mar 13 16:00-17:00 C1.112 Max Welling Stochastic Deep Learning
Mar 6 16:00-17:00 C3.163 Thijs van Ommen Accurate and efficient causal discovery
Feb 27 - - - No talk
Feb 20 16:00-17:00 C3.163 Bas Veeling Uncertainty in Deep Neural Networks with Stochastic Quantized Activation Variational Inference
Feb 13 16:00-17:00 C3.163 ChangYong Oh BOCK: Bayesian Optimization with Cylindrical Kernels
Feb 6 - - - No talk
Jan 30 16:00-17:00 C3.163 Jorn Peters Binary Neural Networks: an overview
Jan 26 15:30-16:30 C3.163 Veronika Cheplygina Challenges of multiple instance learning in medical image analysis
Jan 16 16:00-17:00 C3.163 Jakub Tomczak Attention-based Deep Multiple Instance Learning

2017

Date Time Room Speaker Title (Link)
Dec 12 16:00-17:00 C3.163 Giorgio Patrini Federated learning on vertically partitioned data via entity resolution and homomorphic encryption
Dec 5 - - - No talk due to Sinterklaas (and also NIPS)
Nov 28 - - - No talk
Nov 21 - - - No talk
Nov 14 16:00-17:00 C3.163 Thomas Kipf End-to-end learning on graphs with graph convolutional networks
Nov 7 16:00-17:00 C3.163 Matthias Reisser Failure Modes of distributed Variational Inference
Oct 31 16:00-17:00 C3.163 Elise van der Pol Graph-based Sequential Decision Making Under Uncertainty
Oct 24 16:00-17:00 C3.163 Sara Magliacane  Joint Causal Inference from Observational and Experimental Datasets
Oct 17 - - - No talk
Oct 10 16:00-17:00 C3.163 Peter O'Connor Towards Event-Based online Learning
Oct 3 16:00-17:00 C3.163 Christos Louizos Bayesian Uncertainty and Compression for Deep Learning
Sep 26 - - - No talk
Sep 19 16:00-17:00 C3.163 Stephan Bongers Marginalization of Structural Causal Models with feedback
Sep 12 - - - No talk
Sep 5 16:00-17:00 C3.163 Patrick Forré Markov Properties for Probabilistic Graphical Models with Latent Confounding and Feedback Loops
- - - - No talks during summer
Jul 4 16:00-17:00 C3.163 Arnout Tilgenkamp (Flow Traders) Machine learning at Flow Traders: Past, Present, Future
Jun 27 - - - No talk
Jun 20 - - - No talk
Jun 13 16:00-17:00 C3.163 Vaishak Belle Open-Universe Probabilistic Models
Jun 6 16:00-17:00 C3.163 Ted Meeds Integrating Cancer Genomics Data using Autoencoders
May 30 16:00-17:00 C3.163 Maurice Weiler Learning steerable filters for rotation-equivariant CNNs
May 23 16:00-17:00 C3.163 Joris Mooij Causal Transfer Learning with Joint Causal Inference
May 16 16:00-17:00 C3.163 Tim van Erven MetaGrad: Multiple Learning Rates in Online Learning
May 9 16:00-17:00 C3.163 Raghavendra Selvan Segmenting Tree Structures with Probabilistic State-space Models and Bayesian Smoothing
May 2 16:00-17:00 C3.163 Zeynep Akata Vision and Language for Multimodal Deep Learning
Apr 25 - - - No talk due to ICLR
Apr 18 16:00-17:00 C3.163 Philip Versteeg Prediction and validation of causal effects in gene knockout experiments
Apr 11 16:00-17:00 C3.163 Rianne van den Berg Graph convolutional networks as recommender systems
Apr 4 16:00-17:00 C3.163 Tineke Blom Causal Discovery in the Presence of Measurement Error
Mar 28 - - - No talk
Mar 21 16:00-17:00 C3.163 Frederick Eberhardt Causal Macro Variables
Mar 14 16:00-17:00 C3.163 Taco Cohen Group Equivariant & Steerable CNNs
Mar 7 16:00-17:00 C3.163 Karen Ullrich Soft Weight-Sharing for Neural Network Compression
Feb 28 16:00-17:00 C3.163 ChangYong Oh High dimensional Bayesian Optimization
Feb 21 16:00-17:00 C3.163 Artem Grotov Deep Counterfactual Learning
Feb 14 16:00-17:00 C3.163 Jakub Tomczak Improving Variational Auto-Encoders using volume-preserving flows: A preliminary study
Feb 7 16:00-17:00 C3.163 Thijs van Ommen Recognizing linear structural equation models from observational data
Jan 31 16:00-17:00 C3.163 Max Welling AMLAB/QUVA's progress in Deep Learning
Jan 24 16:00-17:00 C3.163 Marco Loog Semi-Supervision, Surrogate Losses, and Safety Guarantees
Jan 17 - - - IvI nieuwjaarsborrel
Jan 10 - - - No talk
Jan 3 - - - Christmas break

2016

Date Time Room Speaker Title (Link)
Dec 27 - - - Christmas break
Dec 20 16:00-17:00 C3.163 Open Open
Dec 13 16:00-17:00 C3.163 Thomas Kipf Deep Learning for Graph-Structured Data
Dec 6 - - - No talk due to NIPS
Nov 29 16:00-17:00 C3.163 Sara Magliacane Ancestral Causal Inference
Nov 22 16:00-17:00 C3.163 Paul Rubenstein Structural Equation Models: Where do they come from?
Nov 16 16:00-17:00 C4.174 Tom Claassen Causal discovery in psychometric data sets: handling deterministic relations
Nov 8 16:00-17:00 C3.163 Open Open
Nov 1 16:00-17:00 C3.163 Open Open
Oct 25 16:00-17:00 C3.163 Open Open
Oct 18 16:00-17:00 C3.163 Open Open
Oct 11 16:00-17:00 C3.163 Joan Bruna Addressing Computational and Statistical Gaps with Deep Neural Networks
Aug 23 16:00-17:00 C3.163 Riaan Zoetmulder Deep Causal Inference
July 12 16:00-17:00 C3.163 Christios Louizos Bayesian Deep Learning and Uncertainty
July 5 16:00-17:00 C3.163 Peter O'Connor Deep Spiking Networks
June 28 16:00-17:00 C3.163 Tameem Adel Collapsed Variational Inference for Sum-Product Networks
June 21 16:00-17:00 C3.163 Matthias Reisser Distributed Bayesian Deep Learning
June 21 - - - Cancelled
June 7 16:00-17:00 C3.163 Thijs van Ommen Robust probability updating
May 31 16:00-17:00 C3.163 Matthijs Snel An introduction to market making and data science at Optiver
May 24 16:00-17:00 C4.174 Ted Meeds Likelihood-free Inference by Controlling Simulator Noise
May 17 16:00-17:00 C3.163 Karen Ullrich Combining generative models and deep learning
May 10 16:00-17:00 C3.163 Stephan Bongers Marginalization and Reduction of Structural Causal Models
May 3 16:00-17:00 C3.163 Patrick Putzky Neural Networks for estimation in inverse problems
Apr 26 16:00-17:00 C3.163 Joris Mooij Automating Causal Discovery and Prediction
Apr 19 16:00-17:00 C3.163 Yash Satsangi Exploiting Submodular Value Functions for Scaling Up Active Perception
Apr 12 16:00-17:00 C3.163 Philip Versteeg Causal inference and validation with micro-array data
Mar 8 16:00-17:00 C4.174 Sarod Yatawatta Modern radio astronomy: challenges and opportunities
Mar 1 16:00-17:00 C3.163 Joao Messias Variable-Order Markov Models for Sequence Prediction  (2/2)
Feb 23 16:00-17:00 C3.163 Joao Messias Variable-Order Markov Models for Sequence Prediction (1/2)
Feb 16 16:00-17:00 C3.163 Mandar Chandorkar Space Weather Prediction using Gaussian Process (GP) Non Linear Auto-Regressive Models
Feb 9 16:00-17:00 C3.163 Kyriacos Shiarli Learning cost functions for Sample-based robotic planners
Jan 26 16:00-17:00 C3.163 Mijung Park Bayesian methodologies for efficient data analysis
Jan 19 16:00-17:00 C3.163 Deepak Geetha Viswanathan Generalized parts-based models for unrectified images
Jan 12 16:00-17:00 C3.163 Albert Huizing Applying the art of deep learning to radar
Jan 5 - - - Christmas break

2015

Date Time Room Speaker Title (Link)
Dec 29 - - - Christmas break
Dec 22 - - - Christmas break
Dec 15 16:00-17:00 C3.163 John Ashley Burgoyne ‘Big data’ music-cognition style
Dec 8 - - - No talk due to NIPS
Dec 1  16:00-17:00 C3.163 Sara Magliacane Probabilistic logical causal inference
Nov 24 16:00-17:00 C3.163 Artem Grotov Self-learning search engines
Nov 17 16:00-17:00 C3.163 Antti Hyttinen Constraint Satisfaction Approach to Causal Inference
Nov 10 - - - Cancelled
Nov 3 - - - Cancelled
Oct 27  16:00-17:00 C3.163 Yash Satsangi Probably Approximately Correct Greedy Maximization
Oct 20  16:00-17:00 C3.163 Taco Cohen Harmonic Exponential Families
Oct 13 13:00-14:00 C3.163 Sach Mukherjee Towards empirical assessment of causal inference
Oct 6  - - - Cancelled
Sep 29  16:00-17:00 C3.163 Elena Sokolova Causal Discovery from Medical Data
Sep 22 16:00-17:00 C3.163 Video: Michael Jordan On Computational Thinking: Inferential Thinking and Big Data
Sep 15 - - - Opening QUvA Lab
Sep 8 - - - Cancelled
Sep 1 15:00-16:00 C1.112 Joost Kok Machine Learning in Scientific Applications

 

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