1 citations · 2 across the 26 of their papers we have counts for
13 papers · 1 filter
FlowBudget: Training-Free Sampling-Budget Estimation for Flow Matching Models
Zahra Dehghani, Pablo Piantanida, Mohammadhadi Shateri
Sampling from continuous flow-based generators such as flow matching models requires numerical ODE or SDE integration, making inference cost depend on the number of function evalua…
Source-Free Class Relearning: Diagnosing Forgetting in Class Unlearning
Zahra Dehghani, Pablo Piantanida, Mohammadhadi Shateri
Class unlearning aims to remove a model's ability to recognize designated forget classes while preserving performance on retain classes. However, low forget accuracy after unlearni…
Continuous Adversarial MeanFlow Transfer
Yara Bahram, Zahra Dehghani, Mélodie Desbos +3
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costl…
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
Philippe Formont, Maxime Darrin, Ismail Ben Ayed +1
Recent reasoning-based large language models have shown strong performance on tasks with verifiable outcomes, but their use in de novo molecular generation remains limited by the l…
Happiness as a Measure of Fairness
Georg Pichler, Marco Romanelli, Pablo Piantanida
In this paper, we propose a novel fairness framework grounded in the concept of happiness, a measure of the utility each group gains fromdecisionoutcomes. Bycapturingfairness throu…
Learning Task-Agnostic Representations through Multi-Teacher Distillation
Philippe Formont, Maxime Darrin, Banafsheh Karimian +5
Casting complex inputs into tractable representations is a critical step across various fields. Diverse embedding models emerge from differences in architectures, loss functions, i…
Statistical Deficiency for Task Inclusion Estimation
Loïc Fosse, Frédéric Béchet, Benoît Favre +5
Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any t…
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEE
Florence Regol, Joud Chataoui, Bertrand Charpentier +3
Machine learning models can solve complex tasks but often require significant computational resources during inference. This has led to the development of various post-training com…
Beyond the Norms: Detecting Prediction Errors in Regression Models
Andres Altieri, Marco Romanelli, Georg Pichler +2
This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling err…
When is an Embedding Model More Promising than Another?
Maxime Darrin, Philippe Formont, Ismail Ben Ayed +2
Embedders play a central role in machine learning, projecting any object into numerical representations that can, in turn, be leveraged to perform various downstream tasks. The eva…
A Strong Baseline for Molecular Few-Shot Learning
Philippe Formont, Hugo Jeannin, Pablo Piantanida +1
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies. We rev…
Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese, Marco Romanelli, Pablo Piantanida
This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them various avenues to introduce noise into th…
On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine Learning
Roberta Hansen, Matias Vera, Lautaro Estienne +2
This paper deals with the convergence analysis of the SUCPA (Semi Unsupervised Calibration through Prior Adaptation) algorithm, defined from a first-order non-linear difference equ…