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Can AI help in ideation? A theory-based model for idea screening in crowdsourcing contests
Crowdsourcing generates up to thousands of ideas per contest. The selection of best ideas
is costly because of the limited number, objectivity, and attention of experts. Using a data set …
is costly because of the limited number, objectivity, and attention of experts. Using a data set …
Pal: Pluralistic alignment framework for learning from heterogeneous preferences
Large foundation models pretrained on raw web-scale data are not readily deployable
without additional step of extensive alignment to human preferences. Such alignment is …
without additional step of extensive alignment to human preferences. Such alignment is …
Deep embedding learning with discriminative sampling policy
Deep embedding learning aims to learn a distance metric for effective similarity
measurement, which has achieved promising performance in various tasks. As the vast …
measurement, which has achieved promising performance in various tasks. As the vast …
Metric learning from limited pairwise preference comparisons
We study metric learning from preference comparisons under the ideal point model, in which
a user prefers an item over another if it is closer to their latent ideal item. These items are …
a user prefers an item over another if it is closer to their latent ideal item. These items are …
Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning
Distance metric learning (DML) aims to find a suitable measure to compute a distance
between instances. Facilitated by side information, the learned metric can often improve the …
between instances. Facilitated by side information, the learned metric can often improve the …
Strongly truthful interactive regret minimization
When faced with a database containing millions of tuples, an end user might be only
interested in finding his/her (close to) favorite tuple in the database. Recently, a regret …
interested in finding his/her (close to) favorite tuple in the database. Recently, a regret …
Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning
We introduce a new type of query mechanism for collecting human feedback, called the
perceptual adjustment query (PAQ). Being both informative and cognitively lightweight, the …
perceptual adjustment query (PAQ). Being both informative and cognitively lightweight, the …
[PDF][PDF] One for All: Simultaneous Metric and Preference Learning over Multiple Users.
This paper investigates simultaneous preference and metric learning from a crowd of
respondents. A set of items represented by d-dimensional feature vectors and paired …
respondents. A set of items represented by d-dimensional feature vectors and paired …
Simultaneous preference and metric learning from paired comparisons
A popular model of preference in the context of recommendation systems is the so-called
ideal point model. In this model, a user is represented as a vector u together with a collection …
ideal point model. In this model, a user is represented as a vector u together with a collection …
[PDF][PDF] Deep metric learning: The generalization analysis and an adaptive algorithm.
As an effective way to learn a distance metric between pairs of samples, deep metric
learning (DML) has drawn significant attention in recent years. The key idea of DML is to …
learning (DML) has drawn significant attention in recent years. The key idea of DML is to …