1.6k citations · 12.4k across the 394 of their papers we have counts for
45 papers · 1 filter
On the Relationship Between Explanation and Prediction: A Causal View
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith +2
Being able to provide explanations for a model's decision has become a central requirement for the development, deployment, and adoption of machine learning models. However, we are…
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Jonas Wildberger, Maximilian Dax, Stephen R. Green +5
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accur…
A General Purpose Neural Architecture for Geospatial Systems
Nasim Rahaman, Martin Weiss, Frederik Träuble +6
Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. H…
Federated Causal Discovery From Interventions
Amin Abyaneh, Nino Scherrer, Patrick Schwab +3
Causal discovery serves a pivotal role in mitigating model uncertainty through recovering the underlying causal mechanisms among variables. In many practical domains, such as healt…
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Zhijing Jin, Sydney Levine, Fernando Gonzalez +6
AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, inter…
Neural Attentive Circuits
Nasim Rahaman, Martin Weiss, Francesco Locatello +5
Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities. General purpose models typically m…
Directed Acoustic Assembly in 3D
Kai Melde, Minghui Shi, Heiner Kremer +7
The creation of whole 3D objects in one shot is an ultimate goal for rapid prototyping, most notably biofabrication, where conventional methods are typically slow and apply mechani…
Iterative Teaching by Data Hallucination
Zeju Qiu, Weiyang Liu, Tim Z. Xiao +5
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of…
Spectral Representation Learning for Conditional Moment Models
Ziyu Wang, Yucen Luo, Yueru Li +2
Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of cond…
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Alexander Immer, Christoph Schultheiss, Julia E. Vogt +3
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect can be written as a function of the cause and a noise source independe…
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation
Alexander Dittrich, Jan Schneider, Simon Guist +5
To approach the level of advanced human players in table tennis with robots, generating varied ball trajectories in a reproducible and controlled manner is essential. Current ball…
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
Maximilian Dax, Stephen R. Green, Jonathan Gair +5
We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian po…
DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability
Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen +4
In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics fo…
A Generative Model for Quasar Spectra
Anna-Christina Eilers, David W. Hogg, Bernhard Schölkopf +3
We build a multi-output generative model for quasar spectra and the properties of their black hole engines, based on a Gaussian process latent-variable model. This model treats eve…
Bridging the Gap to Real-World Object-Centric Learning
Maximilian Seitzer, Max Horn, Andrii Zadaianchuk +8
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decompos…
Function Classes for Identifiable Nonlinear Independent Component Analysis
Simon Buchholz, Michel Besserve, Bernhard Schölkopf
Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning. When such models reflect the ground truth factors and the mechanisms ma…
Flow Annealed Importance Sampling Bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +2
Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for…
Structural Causal 3D Reconstruction
Weiyang Liu, Zhen Liu, Liam Paull +2
This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inheren…
Probing the Robustness of Independent Mechanism Analysis for Representation Learning
Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper +2
One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases. A recently prop…
Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions
Hamza Keurti, Hsiao-Ru Pan, Michel Besserve +2
How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations c…
Discrete Key-Value Bottleneck
Frederik Träuble, Anirudh Goyal, Nasim Rahaman +4
Deep neural networks perform well on classification tasks where data streams are i.i.d. and labeled data is abundant. Challenges emerge with non-stationary training data streams su…
Probable Domain Generalization via Quantile Risk Minimization
Cian Eastwood, Alexander Robey, Shashank Singh +4
Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To ach…
Assaying Out-Of-Distribution Generalization in Transfer Learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler +9
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance acr…
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet +1
Important problems in causal inference, economics, and, more generally, robust machine learning can be expressed as conditional moment restrictions, but estimation becomes challeng…