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Found 10 papers, 4 papers with code
Guaranteed confidence-band enclosures for PDE surrogates
The result are conformal-like coverage guaranteed prediction sets for functional surrogate models.
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
To generate accurate and reliable predictions, modern AI systems need to combine data from multiple modalities, such as text, images, audio, spreadsheets, and time series.
Robust Confidence Intervals in Stereo Matching using Possibility Theory
To the best of our knowledge, this is the first method creating disparity confidence intervals based on the cost volume.
Learning Sets of Probabilities Through Ensemble Methods
A possible approach to obtain set-valued predictions is to learn for each query instance a probability set (a. k. a.
Skeptical binary inferences in multi-label problems with sets of probabilities
In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors.
Multi-label Chaining with Imprecise Probabilities
We present two different strategies to extend the classical multi-label chaining approach to handle imprecise probability estimates.
Copula-based conformal prediction for Multi-Target Regression
There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
Epistemic Uncertainty Sampling
In this paper, we advocate a distinction between two different types of uncertainty, referred to as epistemic and aleatoric, in the context of active learning.
Reasons and Means to Model Preferences as Incomplete
Much has been written however on different models of preferences.
