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Found 8 papers, 3 papers with code
Gandalf the Red: Adaptive Security for LLMs
Current evaluations of defenses against prompt attacks in large language model (LLM) applications often overlook two critical factors: the dynamic nature of adversarial behavior and the usability penalties imposed on legitimate users by restrictive defenses.
Recommendations on test datasets for evaluating AI solutions in pathology
The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures.
GeNet: Deep Representations for Metagenomics
We introduce GeNet, a method for shotgun metagenomic classification from raw DNA sequences that exploits the known hierarchical structure between labels for training.
Learning Independent Causal Mechanisms
The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization.
Avoiding Discrimination through Causal Reasoning
Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.
Discriminative k-shot learning using probabilistic models
The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples.
Causal Discovery Using Proxy Variables
In this paper, we develop a framework to estimate the cause-effect relation between two static entities $x$ and $y$: for instance, an art masterpiece $x$ and its fraudulent copy $y$.
Invariant Models for Causal Transfer Learning
We focus on the problem of Domain Generalization, in which no examples from the test task are observed.
