An overview of machine teaching
In this paper we try to organize machine teaching as a coherent set of ideas. Each idea is
presented as varying along a dimension. The collection of dimensions then form the …
presented as varying along a dimension. The collection of dimensions then form the …
Prior knowledge elicitation: The past, present, and future
Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
Interactive machine teaching: a human-centered approach to building machine-learned models
Modern systems can augment people's capabilities by using machine-learned models to
surface intelligent behaviors. Unfortunately, building these models remains challenging and …
surface intelligent behaviors. Unfortunately, building these models remains challenging and …
Binary classification with confidence difference
Recently, learning with soft labels has been shown to achieve better performance than
learning with hard labels in terms of model generalization, calibration, and robustness …
learning with hard labels in terms of model generalization, calibration, and robustness …
A survey on cost types, interaction schemes, and annotator performance models in selection algorithms for active learning in classification
Pool-based active learning (AL) aims to optimize the annotation process (ie, labeling) as the
acquisition of annotations is often time-consuming and therefore expensive. For this …
acquisition of annotations is often time-consuming and therefore expensive. For this …
Active learning with simple questions
K Vasilis, M Mingchen… - The Thirty Seventh Annual …, 2024 - proceedings.mlr.press
We consider an active learning setting where a learner is presented with a pool $ S $ of $ n
$ unlabeled examples belonging to a domain $\mathcal X $ and asks queries to find the …
$ unlabeled examples belonging to a domain $\mathcal X $ and asks queries to find the …
Strong average-case lower bounds from non-trivial derandomization
We prove that for all constants a, NQP= NTIME [n polylog (n)] cannot be (1/2+ 2− log an)-
approximated by 2log an-size ACC 0∘ THR circuits (ACC 0 circuits with a bottom layer of …
approximated by 2log an-size ACC 0∘ THR circuits (ACC 0 circuits with a bottom layer of …
Pointwise binary classification with pairwise confidence comparisons
To alleviate the data requirement for training effective binary classifiers in binary
classification, many weakly supervised learning settings have been proposed. Among them …
classification, many weakly supervised learning settings have been proposed. Among them …
Near-optimal linear decision trees for k-SUM and related problems
We construct near-optimal linear decision trees for a variety of decision problems in
combinatorics and discrete geometry. For example, for any constant k, we construct linear …
combinatorics and discrete geometry. For example, for any constant k, we construct linear …
Noise-tolerant interactive learning using pairwise comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and
pairwise comparison oracles, where the comparison oracle answers which one in the given …
pairwise comparison oracles, where the comparison oracle answers which one in the given …