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Found 9 papers, 0 papers with code
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions
The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable concepts.
Towards learning to explain with concept bottleneck models: mitigating information leakage
Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint.
Feature Importance for Time Series Data: Improving KernelSHAP
Feature importance techniques have enjoyed widespread attention in the explainable AI literature as a means of determining how trained machine learning models make their predictions.
Reductive MDPs: A Perspective Beyond Temporal Horizons
Solving general Markov decision processes (MDPs) is a computationally hard problem.
Asynchronous Collaborative Learning Across Data Silos
Our proposed approach enhances conventional federated learning techniques to make them suitable for this asynchronous training in this intra-organisation, cross-silo setting.
SURF: Improving classifiers in production by learning from busy and noisy end users
We show that conventional crowdsourcing algorithms struggle in this user feedback setting, and present a new algorithm, SURF, that can cope with this non-response ambiguity.
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it.
On the Importance of Opponent Modeling in Auction Markets
The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions.
Hierarchical quantum classifiers
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state.
Quantum Physics
