Bernhard Schölkopf

82

Papers

1,239

Total Citations

Papers (82)

Avoiding Discrimination through Causal Reasoning

NeurIPS 2017arXiv

AdaGAN: Boosting Generative Models

NeurIPS 2017arXiv

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

NeurIPS 2017arXiv

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Consistent Kernel Mean Estimation for Functions of Random Variables

NeurIPS 2016arXiv

Do-PFN: In-Context Learning for Causal Effect Estimation

Identifying Policy Gradient Subspaces

The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks.

Out-of-Variable Generalisation for Discriminative Models

Generative Intervention Models for Causal Perturbation Modeling

Preference Elicitation for Offline Reinforcement Learning

The Directionality of Optimization Trajectories in Neural Networks

Detecting and Identifying Selection Structure in Sequential Data

GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs

Structural Causal 3D Reconstruction

Pairwise Similarity Learning is SimPLE

Towards Total Recall in Industrial Anomaly Detection

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

Towards Principled Disentanglement for Domain Generalization

Robustness of Nonlinear Representation Learning

Provable Privacy with Non-Private Pre-Processing

Geometry-Aware Instrumental Variable Regression

Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?

Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis

Direct Advantage Estimation

Embrace the Gap: VAEs Perform Independent Mechanism Analysis

Amortized Inference for Causal Structure Learning

AutoML Two-Sample Test

Function Classes for Identifiable Nonlinear Independent Component Analysis

Probable Domain Generalization via Quantile Risk Minimization

Exploring the Latent Space of Autoencoders with Interventional Assays

Interventions, Where and How? Experimental Design for Causal Models at Scale

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

Flow Matching for Scalable Simulation-Based Inference

Spuriosity Didn’t Kill the Classifier: Using Invariant Predictions to Harness Spurious Features

Leveraging sparse and shared feature activations for disentangled representation learning

A Measure-Theoretic Axiomatisation of Causality

CLadder: Assessing Causal Reasoning in Language Models

Causal Component Analysis

Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing

Nonparametric Identifiability of Causal Representations from Unknown Interventions

SE(3) Equivariant Augmented Coupling Flows

Controlling Text-to-Image Diffusion by Orthogonal Finetuning

Towards a Learning Theory of Cause-Effect Inference

Removing systematic errors for exoplanet search via latent causes

The Arrow of Time in Multivariate Time Series

Domain Adaptation with Conditional Transferable Components

Differentially Private Database Release via Kernel Mean Embeddings

Detecting non-causal artifacts in multivariate linear regression models

On Matching Pursuit and Coordinate Descent

Learning Independent Causal Mechanisms

Tempered Adversarial Networks

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Kernel Mean Matching for Content Addressability of GANs

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

First-Order Adversarial Vulnerability of Neural Networks and Input Dimension

Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness

Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models

Informative Features for Model Comparison

On the Fairness of Disentangled Representations

Perceiving the arrow of time in autoregressive motion

Kernel Stein Tests for Multiple Model Comparison

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Selecting causal brain features with a single conditional independence test per feature

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Causal analysis of Covid-19 Spread in Germany

Learning Kernel Tests Without Data Splitting

Relative gradient optimization of the Jacobian term in unsupervised deep learning

Backward-Compatible Prediction Updates: A Probabilistic Approach

Regret Bounds for Gaussian-Process Optimization in Large Domains

Dynamic Inference with Neural Interpreters

The Inductive Bias of Quantum Kernels

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Iterative Teaching by Label Synthesis

Causal Influence Detection for Improving Efficiency in Reinforcement Learning

DiBS: Differentiable Bayesian Structure Learning

NeurIPS 2021arXiv

Independent mechanism analysis, a new concept?

Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization

Assaying Out-Of-Distribution Generalization in Transfer Learning

Neural Attentive Circuits