Multi-label learning from single positive labels
Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …
Compared to the standard multi-class case (where each image has only one label), it is …
Theoretically grounded loss functions and algorithms for score-based multi-class abstention
Learning with abstention is a key scenario where the learner can abstain from making a
prediction at some cost. In this paper, we analyze the score-based formulation of learning …
prediction at some cost. In this paper, we analyze the score-based formulation of learning …
Predictor-rejector multi-class abstention: Theoretical analysis and algorithms
We study the key framework of learning with abstention in the multi-class classification
setting. In this setting, the learner can choose to abstain from making a prediction with some …
setting. In this setting, the learner can choose to abstain from making a prediction with some …
Aggregation models in ensemble learning: A large-scale comparison
In this work we present a large-scale comparison of 21 learning and aggregation methods
proposed in the ensemble learning, social choice theory (SCT), information fusion and …
proposed in the ensemble learning, social choice theory (SCT), information fusion and …
Differentiable top-k classification learning
The top-k classification accuracy is one of the core metrics in machine learning. Here, k is
conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training objectives …
conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training objectives …
Improving expert predictions with conformal prediction
Automated decision support systems promise to help human experts solve multiclass
classification tasks more efficiently and accurately. However, existing systems typically …
classification tasks more efficiently and accurately. However, existing systems typically …
Pl@ ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution
This paper presents a novel image dataset with high intrinsic ambiguity and a longtailed
distribution built from the database of Pl@ ntNet citizen observatory. It consists of 306,146 …
distribution built from the database of Pl@ ntNet citizen observatory. It consists of 306,146 …
Rank-based decomposable losses in machine learning: A survey
Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …
differentiate individual losses versus aggregate losses. The individual loss measures the …
Improving screening processes via calibrated subset selection
L Wang, T Joachims… - … Conference on Machine …, 2022 - proceedings.mlr.press
Many selection processes such as finding patients qualifying for a medical trial or retrieval
pipelines in search engines consist of multiple stages, where an initial screening stage …
pipelines in search engines consist of multiple stages, where an initial screening stage …
Conformal prediction sets improve human decision making
In response to everyday queries, humans explicitly signal uncertainty and offer alternative
answers when they are unsure. Machine learning models that output calibrated prediction …
answers when they are unsure. Machine learning models that output calibrated prediction …