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Found 5 papers, 1 papers with code
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation
To address this challenge, we first establish a generalization bound for the adversarial target loss, which consists of (i) terms related to the loss on the data, and (ii) a measure of worst-case domain divergence.
Leveraging Importance Weights in Subset Selection
In such a setting, an algorithm can sample examples one at a time but, in order to limit overhead costs, is only able to update its state (i. e. further train model weights) once a large enough batch of examples is selected.
Adversarial Robustness is at Odds with Lazy Training
Recent works show that adversarial examples exist for random neural networks [Daniely and Schacham, 2020] and that these examples can be found using a single step of gradient ascent [Bubeck et al., 2021].
OWA aggregation of multi-criteria with mixed uncertain fuzzy satisfactions
We apply the Ordered Weighted Averaging (OWA) operator in multi-criteria decision-making.
Thompson Sampling for a Fatigue-aware Online Recommendation System
In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period.
