Bernhard Schölkopf

research

∙ 09/13/2023

Investigating the Impact of Action Representations in Policy Gradient Algorithms

Reinforcement learning (RL) is a versatile framework for learning to sol...

0 Jan Schneider, et al. ∙

research

∙ 09/06/2023

Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks

Atmospheric retrievals (AR) of exoplanets typically rely on a combinatio...

0 Timothy D. Gebhard, et al. ∙

research

∙ 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...

0 Philip Tobuschat, et al. ∙

research

∙ 08/20/2023

SE(3) Equivariant Augmented Coupling Flows

Coupling normalizing flows allow for fast sampling and density evaluatio...

0 Laurence I. Midgley, et al. ∙

research

∙ 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. ...

0 Nico Gürtler, et al. ∙

research

∙ 07/28/2023

Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

Learning policies from previously recorded data is a promising direction...

0 Nico Gürtler, et al. ∙

research

∙ 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 ...

0 Cian Eastwood, et al. ∙

research

∙ 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...

0 Simon Guist, et al. ∙

research

∙ 06/16/2023

Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80

Robotic applications require the integration of various modalities, enco...

0 Vincent Berenz, et al. ∙

research

∙ 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...

0 Aaron Spieler, et al. ∙

research

∙ 06/12/2023

Controlling Text-to-Image Diffusion by Orthogonal Finetuning

Large text-to-image diffusion models have impressive capabilities in gen...

0 Zeju Qiu, et al. ∙

research

∙ 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...

0 Klaus-Rudolf Kladny, et al. ∙

research

∙ 06/09/2023

Can Large Language Models Infer Causation from Correlation?

Causal inference is one of the hallmarks of human intelligence. While th...

0 Zhijing Jin, et al. ∙

research

∙ 06/06/2023

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Selecting hyperparameters in deep learning greatly impacts its effective...

8 Alexander Immer, et al. ∙

research

∙ 06/04/2023

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing

We study the problem of learning causal representations from unknown, la...

0 Simon Buchholz, et al. ∙

research

∙ 06/01/2023

Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding

A prominent challenge of offline reinforcement learning (RL) is the issu...

0 Alizée Pace, et al. ∙

research

∙ 06/01/2023

Nonparametric Identifiability of Causal Representations from Unknown Interventions

We study causal representation learning, the task of inferring latent ca...

0 Julius von Kügelgen, et al. ∙

research

∙ 05/29/2023

Membership Inference Attacks against Language Models via Neighbourhood Comparison

Membership Inference attacks (MIAs) aim to predict whether a data sample...

0 Justus Mattern, et al. ∙

research

∙ 05/26/2023

Causal Component Analysis

Independent Component Analysis (ICA) aims to recover independent latent ...

0 Wendong Liang, et al. ∙

research

∙ 05/26/2023

Flow Matching for Scalable Simulation-Based Inference

Neural posterior estimation methods based on discrete normalizing flows ...

0 Maximilian Dax, et al. ∙

research

∙ 05/23/2023

All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations

Transformer models bring propelling advances in various NLP tasks, thus ...

2 Yuxin Ren, et al. ∙

research

∙ 05/23/2023

Provably Learning Object-Centric Representations

Learning structured representations of the visual world in terms of obje...

0 Jack Brady, et al. ∙

research

∙ 05/19/2023

A Measure-Theoretic Axiomatisation of Causality

Causality is a central concept in a wide range of research areas, yet th...

0 Junhyung Park, et al. ∙

research

∙ 05/18/2023

Estimation Beyond Data Reweighting: Kernel Method of Moments

Moment restrictions and their conditional counterparts emerge in many ar...

0 Heiner Kremer, et al. ∙

research

∙ 05/16/2023

The Hessian perspective into the Nature of Convolutional Neural Networks

While Convolutional Neural Networks (CNNs) have long been investigated a...

0 Sidak Pal Singh, et al. ∙

research

∙ 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...

0 Fernando González, et al. ∙

research

∙ 04/17/2023

Leveraging sparse and shared feature activations for disentangled representation learning

Recovering the latent factors of variation of high dimensional data has ...

0 Marco Fumero, et al. ∙

research

∙ 04/16/2023

Out-of-Variable Generalization

The ability of an agent to perform well in new and unseen environments i...

0 Siyuan Guo, et al. ∙

research

∙ 03/16/2023

Dataflow graphs as complete causal graphs

Component-based development is one of the core principles behind modern ...

0 Andrei Paleyes, et al. ∙

research

∙ 03/11/2023

Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap

The neural collapse (NC) phenomenon describes an underlying geometric sy...

0 Weiyang Liu, et al. ∙

research

∙ 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...

1 Simon Guist, et al. ∙

research

∙ 02/21/2023

Posterior Annealing: Fast Calibrated Uncertainty for Regression

Bayesian deep learning approaches that allow uncertainty estimation for ...

0 Uddeshya Upadhyay, et al. ∙

research

∙ 02/10/2023

On the Interventional Kullback-Leibler Divergence

Modern machine learning approaches excel in static settings where a larg...

0 Jonas Wildberger, et al. ∙

research

∙ 02/07/2023

Robustness Implies Fairness in Casual Algorithmic Recourse

Algorithmic recourse aims to disclose the inner workings of the black-bo...

0 Ahmad-Reza Ehyaei, et al. ∙

research

∙ 01/31/2023

The passive symmetries of machine learning

Any representation of data involves arbitrary investigator choices. Beca...

0 Soledad Villar, et al. ∙

research

∙ 01/27/2023

Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion

The recent surge in popularity of diffusion models for image generation ...

0 Flavio Schneider, et al. ∙

research

∙ 01/20/2023

Multi armed bandits and quantum channel oracles

Multi armed bandits are one of the theoretical pillars of reinforcement ...

0 Simon Buchholz, et al. ∙

research

∙ 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...

7 Yuejiang Liu, et al. ∙

research

∙ 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...

0 Justus Mattern, et al. ∙

research

∙ 12/14/2022

Evaluating vaccine allocation strategies using simulation-assisted causal modelling

Early on during a pandemic, vaccine availability is limited, requiring p...

9 Armin Kekić, et al. ∙

research

∙ 12/13/2022

On the Relationship Between Explanation and Prediction: A Causal View

Explainability has become a central requirement for the development, dep...

0 Amir-Hossein Karimi, et al. ∙

research

∙ 11/16/2022

Adapting to noise distribution shifts in flow-based gravitational-wave inference

Deep learning techniques for gravitational-wave parameter estimation hav...

0 Jonas Wildberger, et al. ∙

research

∙ 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...

0 Amin Abyaneh, et al. ∙

research

∙ 10/31/2022

Iterative Teaching by Data Hallucination

We consider the problem of iterative machine teaching, where a teacher s...

0 Zeju Qiu, et al. ∙

research

∙ 10/29/2022

Spectral Representation Learning for Conditional Moment Models

Many problems in causal inference and economics can be formulated in the...

6 Ziyu Wang, et al. ∙

research

∙ 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...

0 Alessandro Stolfo, et al. ∙

research

∙ 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...

0 Alexander Immer, et al. ∙

research

∙ 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...

0 Alexander Dittrich, et al. ∙

research

∙ 10/11/2022

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

We combine amortized neural posterior estimation with importance samplin...

0 Maximilian Dax, et al. ∙

research

∙ 10/04/2022

When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment

AI systems are becoming increasingly intertwined with human life. In ord...

8 Zhijing Jin, et al. ∙

Bernhard Schölkopf

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