14 accepted papers
Writing is a foundational skill for educational, professional, and civic participation, yet access to frequent and timely writing feedback remains deeply unequal. Teachers face significant workload constraints, particularly in large classes, and many learners lack alternative sources of individualiz
While diffusion models have advanced anomaly detection, their reliance on multi-step noise schedules introduces significant computational complexity. In this paper, we demonstrate that the generative capability of diffusion is not required for tabular one-class anomaly detection. We revisit core pri…
This position paper argues that effective tutoring requires **corrective friction**: surfacing misconceptions and challenging them supportively to drive conceptual change. Yet preference-aligned LLMs can trade **epistemic rigor** for agreeableness. We identify a **Reasoning-Sycophancy Paradox**: mod…
Large-language models (LLMs) and chatbot agents are known to provide wrong outputs at times, and it was recently found that this can never be fully prevented. Hence, uncertainty quantification plays a crucial role, aiming to quantify the level of ambiguity in either one overall number or two numbers…
Effectively explaining decisions of black-box machine learning models is critical to responsible deployment of AI systems that rely on them. Recognizing their importance, the field of explainable AI (XAI) provides several techniques to generate these explanations. Yet, there is relatively little emp…
Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggregates the models trained locally by clients without necessitating access to local…
As robots become increasingly prevalent amidst diverse environments, their ability to adapt to novel scenarios and objects is essential. Advances in modern object detection have also paved the way for robots to identify interaction entities within their immediate vicinity. One drawback is that the r…
Contrastively trained encoders have recently been proven to invert the data-generating process: they encode each input, e.g., an image, into the true latent vector that generated the image (Zimmermann et al., 2021). However, real-world observations often have inherent ambiguities. For instance, imag…
Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not onl…
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing. When no human concept labels are available, concept discovery methods search trained embedding spaces for interpretable concepts like object shape or color that can provide post-ho…
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies. To assess the attribution quality across different attribution techniques, the most popular among these evaluation strategies in the image domain use pixel pert…
"Proxy-based Deep Metric Learning (DML) learns deep metric spaces by embedding images and class representatives (proxies) close to one another during training, as commonly measured by the angle between them. However, this disregards the embedding norm, which can carry additional beneficial context s…
Successful and meaningful human-robot interaction requires robots to have knowledge about the interaction context - e.g., which objects should be interacted with. Unfortunately, the corpora of interactive objects is - for all practical purposes - infinite. This fact hinders the deployment of robots…
As human gaze provides information on our cognitive states, actions, and intentions, gaze-based interaction has the potential to enable a fluent and natural human-robot collaboration. In this work, we focus on reliable gaze estimation in remote eye tracking based on calibration-free methods. Althoug…