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Bilevel learning of the Group Lasso structure
Jordan Frecon, Saverio Salzo, Massimiliano Pontil
Regression with group-sparsity penalty plays acentral role in high-dimensional predictionproblems.
Multi-Object Hallucination in Vision Language Models
There are, however, two key limitations with these setups as shown by a case study in Figure 1.
MALT Powers Up Adversarial Attacks
Almost Linearity T argeting, based on medium-scale almost linearity assumptions.
f475bdd151d8b5fa01215aeda925e75c-Paper-Conference.pdf
Weconsider the pool-based activelearning problem, where only asubset ofthe training data is labeled, and the goal is to query a batch of unlabeled samples to be labeled so as to maximally improve model performance.
