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Found 9 papers, 4 papers with code
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
In this work, we relate these two approaches and study how to learn human-interpretable concepts from data.
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general.
Flow Matching for Scalable Simulation-Based Inference
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging.
Causal Component Analysis
As a corollary, this interventional perspective also leads to new identifiability results for nonlinear ICA -- a special case of CauCA with an empty graph -- requiring strictly fewer datasets than previous results.
Multi-Armed Bandits and Quantum Channel Oracles
Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition.
Function Classes for Identifiable Nonlinear Independent Component Analysis
Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings.
AutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
The Inductive Bias of Quantum Kernels
Quantum computers offer the possibility to efficiently compute inner products of exponentially large density operators that are classically hard to compute.
Assaying Large-scale Testing Models to Interpret COVID-19 Case Numbers
Large-scale testing is considered key to assess the state of the current COVID-19 pandemic.
Applications Populations and Evolution
