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
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 NorouziNeurIPS 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-CzarnetzkiStrategic classification made practical: reproduction
Maks KulickiSelf-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
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