NEURIPS FEST 2022

The NeurIPS Fest 2022 is a preview party of the machine learning research which are happening in the Amsterdam AI Ecosystem and will be showcased at the 36th Conference on Neural Information Processing Systems (NeurIPS). The event is sponsored and organized by the ELLIS Amsterdam Unit. 
The event was held on 24 November 2022 (17:00) at Lab42 to celebrate the great works of our researchers and students and connect with the ELLIS Amsterdam community at large.

Papers which will be showcased at the event

NeurIPS Conference Papers

Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel

Changyong Oh · Roberto Bondesan · Efstratios Gavves · Max Welling

On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane

Gabriele Cesa · Arash Behboodi · Taco Cohen · Max Welling

Alleviating Adversarial Attacks on Variational Autoencoders with MCMC

Anna Kuzina · Max Welling · Jakub Tomczak

On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models

Kamil Deja · Anna Kuzina · Tomasz Trzcinski · Jakub Tomczak

Variational Model Perturbation for Source-Free Domain Adaptation

Mengmeng Jing · Xiantong Zhen · Jingjing Li · Cees Snoek

Association Graph Learning for Multi-Task Classification with Category Shifts

Jiayi Shen · Zehao Xiao · Xiantong Zhen · Cees Snoek · Marcel Worring

Neural Topological Ordering for Computation Graphs

Mukul Gagrani · Corrado Rainone · Yang Yang · Harris Teague · Wonseok Jeon · Roberto Bondesan · Herke van Hoof · Christopher Lott · Weiliang Zeng · Piero Zappi

Learning Expressive Meta-Representations with Mixture of Expert Neural Processes

Qi Wang · Herke van Hoof

Factored Adaptation for Non-Stationary Reinforcement Learning

Fan Feng · Biwei Huang · Kun Zhang · Sara Magliacane

LieGG: Studying Learned Lie Group Generators

Anna Sepliarskaia · Artem Moskalev · Ivan Sosnovik · Arnold Smeulders

Maximum Class Separation as Inductive Bias in One Matrix

Tejaswi Kasarla · Gertjan Burghouts · Max van Spengler · Elise van der Pol · Rita Cucchiara · Pascal Mettes

Contrastive Neural Ratio Estimation

Benjamin K Miller · Christoph Weniger · Patrick Forré

Generalization Bounds for Equivariant Networks

Arash Behboodi · Gabriele Cesa · Taco Cohen

Weakly supervised causal representation learning

Johann Brehmer · Pim de Haan · Phillip Lippe · Taco Cohen

Equivariant Networks for Zero-Shot Coordination

Darius Muglich · Christian Schroeder de Witt · Elise van der Pol · Shimon Whiteson · Jakob Foerster

Riemannian Diffusion Models

Chin-Wei Huang · Milad Aghajohari · Joey Bose · Prakash Panangaden · Aaron Courville

RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection

Yue Song · Nicu Sebe · Wei Wang

Video Diffusion Models

Jonathan Ho · Tim Salimans · Alexey Gritsenko · William Chan · Mohammad Norouzi · David Fleet

[Re] Explaining in Style: Training a GAN to explain a classifier in StyleSpace

Noah van der Vleuten · Tadija Radusinović · Rick Akkerman · Meilina Reksoprodjo

Hyperbolic Embedding Inference for Structured Multi-Label Prediction

Bo Xiong · Michael Cochez · Mojtaba Nayyeri · Steffen Staab

Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Chitwan Saharia · William Chan · Saurabh Saxena · Lala Li · Jay Whang · Emily Denton · Seyed Kamyar Seyed Ghasemipour · Raphael Gontijo Lopes · Burcu Ayan · Tim Salimans · Jonathan Ho · David Fleet · Mohammad Norouzi

NeurIPS Workshop Papers

Hyperbolic Image Segmentation
Mina Ghadimi Atigh* · Julian Schoep* · Erman Acar · Nanne van Noord ·  Pascal Mettes 
[Re] Replication Study of “Fairness and Bias in Online Selection”

Jaap Stefels ·  Diego van der Mast ·  Soufiane Ben Haddou · Jacky Chu

 

“[Re] Replication study of ’Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling’”

Vera Neplenbroek · Sabijn Perdijk

 

[Re] Exacerbating Algorithmic Bias through Fairness Attacks

Matteo Tafuro ·  Andrea Lombardo ·  Lasse Becker-Czarnetzki

Strategic classification made practical: reproduction 
Maks Kulicki  
Self-Guided Diffusion Models

Vincent Tao Hu ·  David W Zhang · Yuki M. Asano · Gertjan J. Burghouts ·  Cees Snoek

Robust Scheduling with GFlowNets

David W Zhang · Corrado Rainone ·  Markus Peschl · Roberto Bondesan 

Towards Parameter-Efficient Automation of Data Wrangling Tasks with Prefix-Tuning

David Vos · Till Döhmen · Sebastian Schelter

Self-Contained Entity Discovery from Captioned Videos

Melika Ayoughi ·  Paul Groth ·  Pascal Mettes

[Re] Replication Study of “Fairness and Bias in Online Selection” 

Roxana Petcu · Pim Praat · Jeroen Wijnen ·  Manolis Rerres

[Re] Reproduction Study of Variational Fair Clustering 

Floor Eijkelboom · Mark Fokkema ·  Anna Lau · Luuk Verheijen

Latent GP-ODEs with Informative Priors

Ilze Amanda Auzina ·  Cagatay Yildiz ·  Efstratios Gavves

Meta-Learning Makes a Better Multimodal Few-shot Learner

Ivona Najdenkoska ·  Xiantong Zhen ·  Marcel Worring 

 

Unlocking Slot Attention by Changing Optimal Transport Costs

Yan Zhang, David W Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees Snoek 

An Analytics of Culture: Modeling Subjectivity, Scalability, Contextuality, and Temporality

Melvin Wevers, Tobias Blanke, Julia Noordegraaf, Marcel Worring, Nanne van Noord

Kendall Shape-VAE : Learning Shapes in a Generative
Framework

Sharvaree Vadgama, Jakub Tomczak, Erik Bekkers

 

 

Deconfounded Imitation Learning

Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan