79 citations · 261 across the 26 of their papers we have counts for
27 papers
Stepwise Variational Inference with Vine Copulas
Elisabeth Griesbauer, Leiv Rønneberg, Arnoldo Frigessi +2
We propose stepwise variational inference (VI) with vine copulas: a universal VI procedure that combines vine copulas with a novel stepwise estimation procedure of the variational…
Beyond Accuracy: Decomposing the Reasoning Efficiency of LLMs
Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya +1
As reasoning LLMs increasingly trade tokens for accuracy through deliberation, search, and self-correction, a single accuracy score can no longer tell whether those tokens buy usef…
CogniLoad: A Synthetic Natural Language Reasoning Benchmark With Tunable Length, Intrinsic Difficulty, and Distractor Density
Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya +1
Current benchmarks for long-context reasoning in Large Language Models (LLMs) often blur critical factors like intrinsic task complexity, distractor interference, and task length.…
TVineSynth: A Truncated C-Vine Copula Generator of Synthetic Tabular Data to Balance Privacy and Utility
Elisabeth Griesbauer, Claudia Czado, Arnoldo Frigessi +1
We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to d…
BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-Task Large Language Models
Simen Eide, Arnoldo Frigessi
This paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel method for finetuning multi-task Large Language Models (LLMs). Current finetuning approaches, such as…
Rank-based Bayesian clustering via covariate-informed Mallows mixtures
Emilie Eliseussen, Arnoldo Frigessi, Valeria Vitelli
Data in the form of rankings, ratings, pair comparisons or clicks are frequently collected in diverse fields, from marketing to politics, to understand assessors' individual prefer…