9 citations · 25 across the 22 of their papers we have counts for
24 papers
Evaluating AI Models' Capability to Automate Voice Phishing Attacks
Fred Heiding, Claudio Mayrink Verdun, Simon Lermen +5
Voice phishing (vishing) attacks have traditionally been limited by the need for human operators. The rapid emergence of high-quality AI voice synthesis and large language models (…
KV Cache Compression Through the Lens of Transform Coding
Hannah Laus, Claudio Mayrink Verdun, Hao Wang +2
The key-value (KV) cache stores information from past tokens and is a major memory bottleneck in long-context inference. Existing quantization methods address this bottleneck by re…
An Instrument to Evaluate Governance Proposals: AI Policy Analysis at Scale
Paulo Carvao, Claudio Mayrink Verdun, Isabel Adler +1
This paper introduces a policy analysis framework for systematic, transparent assessment of AI governance proposals in an evolving and contested regulatory landscape. AI policy deb…
Reading Between the Dots: Decoding Hidden Computation across Filler Tokens
Kaley Brauer, Claudio Mayrink Verdun, Samuel Marks
Frontier LLMs can perform multi-step reasoning over content-free filler tokens like dots or counting sequences, producing correct answers with no visible chain-of-thought (CoT). Th…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability
Usha Bhalla, Alex Oesterling, Claudio Mayrink Verdun +2
Translating the internal representations and computations of models into concepts that humans can understand is a key goal of interpretability. While recent dictionary learning met…
ProofCompass: Enhancing Specialized Provers with LLM Guidance
Nicolas Wischermann, Claudio Mayrink Verdun, Gabriel Poesia +1
Language models have become increasingly powerful tools for formal mathematical reasoning. However, most existing approaches rely exclusively on either large general-purpose models…
Inference-Time Reward Hacking in Large Language Models
Hadi Khalaf, Claudio Mayrink Verdun, Alex Oesterling +2
A common paradigm to improve the performance of large language models is optimizing for a reward model. Reward models assign a numerical score to an LLM's output that indicates, fo…
HeavyWater and SimplexWater: Distortion-Free LLM Watermarks for Low-Entropy Next-Token Predictions
Dor Tsur, Carol Xuan Long, Claudio Mayrink Verdun +5
Large language model (LLM) watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate b…
Multi-Group Proportional Representation for Text-to-Image Models
Sangwon Jung, Alex Oesterling, Claudio Mayrink Verdun +3
Text-to-image (T2I) generative models can create vivid, realistic images from textual descriptions. As these models proliferate, they expose new concerns about their ability to rep…
Optimized Couplings for Watermarking Large Language Models
Dor Tsur, Carol Xuan Long, Claudio Mayrink Verdun +3
Large-language models (LLMs) are now able to produce text that is, in many cases, seemingly indistinguishable from human-generated content. This has fueled the development of water…
Soft Best-of-n Sampling for Model Alignment
Claudio Mayrink Verdun, Alex Oesterling, Himabindu Lakkaraju +1
Best-of- (BoN) sampling is a practical approach for aligning language model outputs with human preferences without expensive fine-tuning. BoN sampling is performed by generating…
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection
Mariia Seleznova, Hung-Hsu Chou, Claudio Mayrink Verdun +1
We introduce GradPCA, an Out-of-Distribution (OOD) detection method that exploits the low-rank structure of neural network gradients induced by Neural Tangent Kernel (NTK) alignmen…
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Adam Karvonen, Benjamin Wright, Can Rager +6
What latent features are encoded in language model (LM) representations? Recent work on training sparse autoencoders (SAEs) to disentangle interpretable features in LM representati…
Non-Asymptotic Uncertainty Quantification in High-Dimensional Learning
Frederik Hoppe, Claudio Mayrink Verdun, Hannah Laus +2
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional regression or learning problems to increase the confidence of a given predictor. We devel…
With or Without Replacement? Improving Confidence in Fourier Imaging
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer +2
Over the last few years, debiased estimators have been proposed in order to establish rigorous confidence intervals for high-dimensional problems in machine learning and data scien…
Multi-Group Proportional Representation in Retrieval
Alex Oesterling, Claudio Mayrink Verdun, Carol Xuan Long +5
Image search and retrieval tasks can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current approaches to mitigate these representationa…
Uncertainty quantification for learned ISTA
Frederik Hoppe, Claudio Mayrink Verdun, Felix Krahmer +2
Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretabil…
Uncertainty quantification for sparse Fourier recovery
Frederik Hoppe, Felix Krahmer, Claudio Mayrink Verdun +2
One of the most prominent methods for uncertainty quantification in high-dimen-sional statistics is the desparsified LASSO that relies on unconstrained -minimization. The m…
Get rid of your constraints and reparametrize: A study in NNLS and implicit bias
Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun +2
Over the past years, there has been significant interest in understanding the implicit bias of gradient descent optimization and its connection to the generalization properties of…
A Scalable Second Order Method for Ill-Conditioned Matrix Completion from Few Samples
Christian Kümmerle, Claudio Mayrink Verdun
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as an iteratively reweighted least squares (IRLS) algorithm, a saddle-escaping smoothing Ne…
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be tackled using -regularization as in th…
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method
Christian Kümmerle, Claudio M. Verdun
We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least squares (IRLS) algorithm and a saddle-escaping smoo…
Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squares
Christian Kümmerle, Claudio Mayrink Verdun
We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of completing or denoising low-rank matrices that are structured, e.g., that possess a Hankel…