78 citations · 253 across the 56 of their papers we have counts for
12 papers · 1 filter
Normative Alignment of Recommender Systems via Internal Label Shift
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen +4
We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions…
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow +4
We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deploym…
Latent Diffusion for Missing Data
Alberte Heering Estad, Ignacio Peis, Jes Frellsen
Diffusion models have emerged as powerful generative approaches for missing-data imputation, yet most existing methods operate directly in data space and degrade when training data…
Towards More General Control of Diffusion Models Using Jeffrey Guidance
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2
A key strength of diffusion models lies in their flexibility, since their outputs can be controlled at sampling time through guidance. However, beyond simple cases such as conditio…
Order-Agnostic Autoregressive Modelling with Missing Data
Ignacio Peis, Pablo M. Olmos, Jes Frellsen
Order-Agnostic autoregressive models have demonstrated strong performance in deep generative modeling, yet their use in settings with incomplete data remains largely unexplored. In…
Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings
Paul Jeha, Anastasiia Sedova, Louis Béthune +4
For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization…
Tracing Uncertainty in Language Model "Reasoning"
Nils Grünefeld, Bertram Højer, Philipp Mondorf +5
Language model (LM) "reasoning", commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics underlying this process remain…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
Noise-Response Calibration: A Causal Intervention Protocol for LLM-Judges
Maxim Khomiakov, Jes Frellsen
Large language models (LLMs) are increasingly used as automated judges and synthetic labelers, especially in low-label settings. Yet these systems are stochastic and often overconf…
An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
Nils Grünefeld, Jes Frellsen, Christian Hardmeier
Existing methods for quantifying predictive uncertainty in neural networks are either computationally intractable for large language models or require access to training data that…
Fast Sampling for Flows and Diffusions with Lazy and Point Mass Stochastic Interpolants
Gabriel Damsholt, Jes Frellsen, Susanne Ditlevsen
Stochastic interpolants unify flows and diffusions, popular generative modeling frameworks. A primary hyperparameter in these methods is the interpolation schedule that determines…
Supervised Guidance Training for Infinite-Dimensional Diffusion Models
Elizabeth L. Baker, Alexander Denker, Jes Frellsen
Score-based diffusion models have recently been extended to infinite-dimensional function spaces, with uses such as inverse problems arising from partial differential equations. In…