285 research outputs found
From Common to Special: When Multi-Attribute Learning Meets Personalized Opinions
Visual attributes, which refer to human-labeled semantic annotations, have
gained increasing popularity in a wide range of real world applications.
Generally, the existing attribute learning methods fall into two categories:
one focuses on learning user-specific labels separately for different
attributes, while the other one focuses on learning crowd-sourced global labels
jointly for multiple attributes. However, both categories ignore the joint
effect of the two mentioned factors: the personal diversity with respect to the
global consensus; and the intrinsic correlation among multiple attributes. To
overcome this challenge, we propose a novel model to learn user-specific
predictors across multiple attributes. In our proposed model, the diversity of
personalized opinions and the intrinsic relationship among multiple attributes
are unified in a common-to-special manner. To this end, we adopt a
three-component decomposition. Specifically, our model integrates a common
cognition factor, an attribute-specific bias factor and a user-specific bias
factor. Meanwhile Lasso and group Lasso penalties are adopted to leverage
efficient feature selection. Furthermore, theoretical analysis is conducted to
show that our proposed method could reach reasonable performance. Eventually,
the empirical study carried out in this paper demonstrates the effectiveness of
our proposed method
Learning Personalized Attribute Preference via Multi-task AUC Optimization
Traditionally, most of the existing attribute learning methods are trained
based on the consensus of annotations aggregated from a limited number of
annotators. However, the consensus might fail in settings, especially when a
wide spectrum of annotators with different interests and comprehension about
the attribute words are involved. In this paper, we develop a novel multi-task
method to understand and predict personalized attribute annotations. Regarding
the attribute preference learning for each annotator as a specific task, we
first propose a multi-level task parameter decomposition to capture the
evolution from a highly popular opinion of the mass to highly personalized
choices that are special for each person. Meanwhile, for personalized learning
methods, ranking prediction is much more important than accurate
classification. This motivates us to employ an Area Under ROC Curve (AUC) based
loss function to improve our model. On top of the AUC-based loss, we propose an
efficient method to evaluate the loss and gradients. Theoretically, we propose
a novel closed-form solution for one of our non-convex subproblem, which leads
to provable convergence behaviors. Furthermore, we also provide a
generalization bound to guarantee a reasonable performance. Finally, empirical
analysis consistently speaks to the efficacy of our proposed method.Comment: AAAI2019 ora
Chiral-Flux-Phase-Based Topological Superconductivity in Kagome Systems with Mixed Edge Chiralities
Recent studies have attracted intense attention on the quasi-2D kagome
superconductors ( K, Rb, and Cs) where the
unexpected chiral flux phase (CFP) associates with the spontaneous
time-reversal symmetry breaking in charge density wave (CDW) states. Here,
commencing from the 2-by-2 CDW phases, we bridge the gap between topological
superconductivity (TSC) and time-reversal asymmetric CFP in kagome systems.
Several chiral TSC states featuring distinct Chern numbers emerge for an s-wave
or a d-wave superconducting pairing symmetry. Importantly, these CFP-based TSC
phases possess unique gapless edge modes with mixed chiralities (i.e., both
positive and negative chiralities), but with the net chiralities consistent
with the Bogoliubov-de Gennes Chern numbers. We further study the transport
properties of a two-terminal junction, using Chern insulator or normal metal
leads via atomic Green's function method with Landauer-B\"uttiker formalism. In
both cases, the normal electron tunneling and the crossed Andreev reflection
oscillate as the chemical potential changes, but together contribute to plateau
transmissions (1 and 3/2, respectively). These behaviors can be regarded as the
signature of a topological superconductor hosting edge states with mixed
chiralities.Comment: 6 pages, 4 figure
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Traditional machine learning follows a close-set assumption that the training
and test set share the same label space. While in many practical scenarios, it
is inevitable that some test samples belong to unknown classes (open-set). To
fix this issue, Open-Set Recognition (OSR), whose goal is to make correct
predictions on both close-set samples and open-set samples, has attracted
rising attention. In this direction, the vast majority of literature focuses on
the pattern of open-set samples. However, how to evaluate model performance in
this challenging task is still unsolved. In this paper, a systematic analysis
reveals that most existing metrics are essentially inconsistent with the
aforementioned goal of OSR: (1) For metrics extended from close-set
classification, such as Open-set F-score, Youden's index, and Normalized
Accuracy, a poor open-set prediction can escape from a low performance score
with a superior close-set prediction. (2) Novelty detection AUC, which measures
the ranking performance between close-set and open-set samples, ignores the
close-set performance. To fix these issues, we propose a novel metric named
OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise
formulation that evaluates open-set performance and close-set performance in a
coupling manner. Further analysis shows that OpenAUC is free from the
aforementioned inconsistency properties. Finally, an end-to-end learning method
is proposed to minimize the OpenAUC risk, and the experimental results on
popular benchmark datasets speak to its effectiveness
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method
in recommendation systems (RS), closing the gap between metric learning and
Collaborative Filtering. Following the convention of RS, existing methods
exploit unique user representation in their model design. This paper focuses on
a challenging scenario where a user has multiple categories of interests. Under
this setting, we argue that the unique user representation might induce
preference bias, especially when the item category distribution is imbalanced.
To address this issue, we propose a novel method called
\textit{Diversity-Promoting Collaborative Metric Learning} (DPCML), with the
hope of considering the commonly ignored minority interest of the user. The key
idea behind DPCML is to include a multiple set of representations for each user
in the system. Based on this embedding paradigm, user preference toward an item
is aggregated from different embeddings by taking the minimum item-user
distance among the user embedding set. Furthermore, we observe that the
diversity of the embeddings for the same user also plays an essential role in
the model. To this end, we propose a \textit{diversity control regularization}
term to accommodate the multi-vector representation strategy better.
Theoretically, we show that DPCML could generalize well to unseen test data by
tackling the challenge of the annoying operation that comes from the minimum
value. Experiments over a range of benchmark datasets speak to the efficacy of
DPCML
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