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Elke Kirschbaum

5 accepted papers

2025

QA-Calibration of Language Model Confidence Scores

ICLR 2025poster

To use generative question-and-answering (QA) systems for decision-making and in any critical application, these systems need to provide well-calibrated confidence scores that reflect the correctness of their answers. Existing calibration methods aim to ensure that the confidence score is, *on avera…

Cited by 0SourcePDFScholar
2022

Causal Inference Through the Structural Causal Marginal Problem

ICML 2022spotlight

We introduce an approach to counterfactual inference based on merging information from multiple datasets. We consider a causal reformulation of the statistical marginal problem: given a collection of marginal structural causal models (SCMs) over distinct but overlapping sets of variables, determine…

2022

Obtaining Causal Information by Merging Datasets with MAXENT

AISTATS 2022poster

The investigation of the question "which treatment has a causal effect on a target variable?" is of particular relevance in a large number of scientific disciplines. This challenging task becomes even more difficult if not all treatment variables were or even can not be observed jointly with the tar…

Cited by 11SourcePDFScholar
2019

LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos

ICLR 2019poster

Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatio-temporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that fa…

2017

Sparse convolutional coding for neuronal assembly detection

NeurIPS 2017poster

Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing. Although Hebb's original proposal dates back many decades, the detection…