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09/13/2023
Investigating the Impact of Action Representations in Policy Gradient Algorithms
Reinforcement learning (RL) is a versatile framework for learning to sol...
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09/06/2023
Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks
Atmospheric retrievals (AR) of exoplanets typically rely on a combinatio...
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08/28/2023
Data-Efficient Online Learning of Ball Placement in Robot Table Tennis
We present an implementation of an online optimization algorithm for hit...
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08/20/2023
SE(3) Equivariant Augmented Coupling Flows
Coupling normalizing flows allow for fast sampling and density evaluatio...
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08/15/2023
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
Experimentation on real robots is demanding in terms of time and costs. ...
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07/28/2023
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
Learning policies from previously recorded data is a promising direction...
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07/19/2023
Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features
To avoid failures on out-of-distribution data, recent works have sought ...
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07/05/2023
A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions
A long-lasting goal of robotics research is to operate robots safely, wh...
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06/16/2023
Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80
Robotic applications require the integration of various modalities, enco...
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06/14/2023
The ELM Neuron: an Efficient and Expressive Cortical Neuron Model Can Solve Long-Horizon Tasks
Traditional large-scale neuroscience models and machine learning utilize...
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06/12/2023
Controlling Text-to-Image Diffusion by Orthogonal Finetuning
Large text-to-image diffusion models have impressive capabilities in gen...
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06/09/2023
Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimators
We study causal effect estimation from a mixture of observational and in...
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06/09/2023
Can Large Language Models Infer Causation from Correlation?
Causal inference is one of the hallmarks of human intelligence. While th...
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06/06/2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Selecting hyperparameters in deep learning greatly impacts its effective...
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06/04/2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
We study the problem of learning causal representations from unknown, la...
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06/01/2023
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
A prominent challenge of offline reinforcement learning (RL) is the issu...
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06/01/2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions
We study causal representation learning, the task of inferring latent ca...
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05/29/2023
Membership Inference Attacks against Language Models via Neighbourhood Comparison
Membership Inference attacks (MIAs) aim to predict whether a data sample...
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05/26/2023
Causal Component Analysis
Independent Component Analysis (ICA) aims to recover independent latent ...
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05/26/2023
Flow Matching for Scalable Simulation-Based Inference
Neural posterior estimation methods based on discrete normalizing flows ...
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05/23/2023
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Transformer models bring propelling advances in various NLP tasks, thus ...
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05/23/2023
Provably Learning Object-Centric Representations
Learning structured representations of the visual world in terms of obje...
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05/19/2023
A Measure-Theoretic Axiomatisation of Causality
Causality is a central concept in a wide range of research areas, yet th...
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05/18/2023
Estimation Beyond Data Reweighting: Kernel Method of Moments
Moment restrictions and their conditional counterparts emerge in many ar...
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05/16/2023
The Hessian perspective into the Nature of Convolutional Neural Networks
While Convolutional Neural Networks (CNNs) have long been investigated a...
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05/09/2023
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
With the recent advances in natural language processing (NLP), a vast nu...
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04/17/2023
Leveraging sparse and shared feature activations for disentangled representation learning
Recovering the latent factors of variation of high dimensional data has ...
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04/16/2023
Out-of-Variable Generalization
The ability of an agent to perform well in new and unseen environments i...
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03/16/2023
Dataflow graphs as complete causal graphs
Component-based development is one of the core principles behind modern ...
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03/11/2023
Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap
The neural collapse (NC) phenomenon describes an underlying geometric sy...
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03/03/2023
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning
Reinforcement learning has shown great potential in solving complex task...
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02/21/2023
Posterior Annealing: Fast Calibrated Uncertainty for Regression
Bayesian deep learning approaches that allow uncertainty estimation for ...
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02/10/2023
On the Interventional Kullback-Leibler Divergence
Modern machine learning approaches excel in static settings where a larg...
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02/07/2023
Robustness Implies Fairness in Casual Algorithmic Recourse
Algorithmic recourse aims to disclose the inner workings of the black-bo...
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01/31/2023
The passive symmetries of machine learning
Any representation of data involves arbitrary investigator choices. Beca...
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01/27/2023
Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
The recent surge in popularity of diffusion models for image generation ...
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01/20/2023
Multi armed bandits and quantum channel oracles
Multi armed bandits are one of the theoretical pillars of reinforcement ...
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01/12/2023
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning
Recent years have seen a surge of interest in learning high-level causal...
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12/20/2022
Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing
Generated texts from large pretrained language models have been shown to...
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12/14/2022
Evaluating vaccine allocation strategies using simulation-assisted causal modelling
Early on during a pandemic, vaccine availability is limited, requiring p...
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12/13/2022
On the Relationship Between Explanation and Prediction: A Causal View
Explainability has become a central requirement for the development, dep...
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11/16/2022
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Deep learning techniques for gravitational-wave parameter estimation hav...
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11/07/2022
FED-CD: Federated Causal Discovery from Interventional and Observational Data
Causal discovery, the inference of causal relations from data, is a core...
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10/31/2022
Iterative Teaching by Data Hallucination
We consider the problem of iterative machine teaching, where a teacher s...
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10/29/2022
Spectral Representation Learning for Conditional Moment Models
Many problems in causal inference and economics can be formulated in the...
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10/21/2022
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
We have recently witnessed a number of impressive results on hard mathem...
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10/13/2022
On the Identifiability and Estimation of Causal Location-Scale Noise Models
We study the class of location-scale or heteroscedastic noise models (LS...
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10/12/2022
AIMY: An Open-source Table Tennis Ball Launcher for Versatile and High-fidelity Trajectory Generation
To approach the level of advanced human players in table tennis with rob...
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10/11/2022
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
We combine amortized neural posterior estimation with importance samplin...
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10/04/2022