24 citations · 46 across the 11 of their papers we have counts for
11 papers
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…
EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
Chung-Yiu Yau, Dawei Li, Athanasios Glentis +3
Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning training mainly due to stochas…
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks
Valentyn Boreiko, Alexander Panfilov, Vaclav Voracek +2
A plethora of jailbreaking attacks have been proposed to obtain harmful responses from safety-tuned LLMs. These methods largely succeed in coercing the target output in their origi…
How Much Can We Forget about Data Contamination?
Sebastian Bordt, Suraj Srinivas, Valentyn Boreiko +1
The leakage of benchmark data into the training data has emerged as a significant challenge for evaluating the capabilities of large language models (LLMs). In this work, we challe…
Identification of Fine-grained Systematic Errors via Controlled Scene Generation
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
Many safety-critical applications, especially in autonomous driving, require reliable object detectors. They can be very effectively assisted by a method to search for and identify…
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models
Indu Ilanchezian, Valentyn Boreiko, Laura Kühlewein +5
Counterfactual reasoning is often used in clinical settings to explain decisions or weigh alternatives. Therefore, for imaging based specialties such as ophthalmology, it would be…
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
The identification and removal of systematic errors in object detectors can be a prerequisite for their deployment in safety-critical applications like automated driving and roboti…
Identification of Systematic Errors of Image Classifiers on Rare Subgroups
Jan Hendrik Metzen, Robin Hutmacher, N. Grace Hua +2
Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under…
Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko +1
Benchmark performance of deep learning classifiers alone is not a reliable predictor for the performance of a deployed model. In particular, if the image classifier has picked up s…
Diffusion Visual Counterfactual Explanations
Maximilian Augustin, Valentyn Boreiko, Francesco Croce +1
Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are 'small' but 'realistic' semantic changes of the image c…
Sparse Visual Counterfactual Explanations in Image Space
Valentyn Boreiko, Maximilian Augustin, Francesco Croce +2
Visual counterfactual explanations (VCEs) in image space are an important tool to understand decisions of image classifiers as they show under which changes of the image the decisi…