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Alec Helbling

5 accepted papers

2026

LORE: Jointly Learning The Intrinsic Dimensionality and Relative Similarity Structure from Ordinal Data

ICLR 2026poster

Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem. We introduce LORE (Low Rank Ordinal Embedding), a scalable framework that jointly learns both the intrinsic dimensionality and an ordinal embedding fr…

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2025

ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features

ICML 2025oral

Do the rich representations of multi-modal diffusion transformers (DiTs) exhibit unique properties that enhance their interpretability? We introduce ConceptAttention, a novel method that leverages the expressive power of DiT attention layers to generate high-quality saliency maps that precisely loca…

2025

LLM Attributor: Interactive Visual Attribution for LLM Generation

AAAI 2025technical

While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM ATTRIBUTOR, a Python library that provides…

2025

RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering

IJCAI 2025

Differentiable rendering techniques like Gaussian Splatting and Neural Radiance Fields have become powerful tools for generating high-fidelity models of 3D objects and scenes. Their ability to produce both physically plausible and differentiable models of scenes are key ingredient needed to produce

2025

TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models

AAAI 2025technical

Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present TRANSFORMER EXPLAINER, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer con…