This paper introduces a novel approach for click-through rate (CTR)
prediction within industrial recommender systems, addressing the inherent
challenges of numerical imbalance and geometric asymmetry. These challenges
stem from imbalanced datasets, where positive (click) instances occur less
frequently than negatives (non-clicks), and geometrically asymmetric
distributions, where positive samples exhibit visually coherent patterns while
negatives demonstrate greater diversity. To address these challenges, we have
used a deep neural network classifier that uses the polyhedral conic functions.
This classifier is similar to the one-class classifiers in spirit and it
returns compact polyhedral acceptance regions to separate the positive class
samples from the negative samples that have diverse distributions. Extensive
experiments have been conducted to test the proposed approach using
state-of-the-art (SOTA) CTR prediction models on four public datasets, namely
Criteo, Avazu, MovieLens and Frappe. The experimental evaluations highlight the
superiority of our proposed approach over Binary Cross Entropy (BCE) Loss,
which is widely used in CTR prediction tasks.

本文介绍了一种新颖的工业推荐系统中点击率（CTR）预测方法，解决了数值不平衡和几何不对称的固有挑战。

多面体锥形分类器用于 CTR 预测

Polyhedral Conic Classifier for CTR Prediction

Partial label learning (PLL) is a weakly-supervised learning paradigm where
each training instance is paired with a set of candidate labels (partial
label), one of which is the true label. Noisy PLL (NPLL) relaxes this
constraint by allowing some partial labels to not contain the true label,
enhancing the practicality of the problem. Our work centers on NPLL and
presents a minimalistic framework called SARI that initially assigns
pseudo-labels to images by exploiting the noisy partial labels through a
weighted nearest neighbour algorithm. These pseudo-label and image pairs are
then used to train a deep neural network classifier with label smoothing and
standard regularization techniques. The classifier's features and predictions
are subsequently employed to refine and enhance the accuracy of pseudo-labels.
SARI combines the strengths of Average Based Strategies (in pseudo labelling)
and Identification Based Strategies (in classifier training) from the
literature. We perform thorough experiments on seven datasets and compare SARI
against nine NPLL and PLL methods from the prior art. SARI achieves
state-of-the-art results in almost all studied settings, obtaining substantial
gains in fine-grained classification and extreme noise settings.

SARI is a minimalistic framework for noisy partial label learning that combines average-based and identification-based strategies, achieving state-of-the-art results in fine-grained classification and extreme noise settings.

简化的平均与稳健的基于嘈杂部分标签的识别

SARI: Simplistic Average and Robust Identification based Noisy Partial  Label Learning

Deep Neural Network (DNN) classifiers are known to be vulnerable to Trojan or
backdoor attacks, where the classifier is manipulated such that it
misclassifies any input containing an attacker-determined Trojan trigger.
Backdoors compromise a model's integrity, thereby posing a severe threat to the
landscape of DNN-based classification. While multiple defenses against such
attacks exist for classifiers in the image domain, there have been limited
efforts to protect classifiers in the text domain.
We present Trojan-Miner (T-Miner) -- a defense framework for Trojan attacks
on DNN-based text classifiers. T-Miner employs a sequence-to-sequence
(seq-2-seq) generative model that probes the suspicious classifier and learns
to produce text sequences that are likely to contain the Trojan trigger.
T-Miner then analyzes the text produced by the generative model to determine if
they contain trigger phrases, and correspondingly, whether the tested
classifier has a backdoor. T-Miner requires no access to the training dataset
or clean inputs of the suspicious classifier, and instead uses synthetically
crafted "nonsensical" text inputs to train the generative model. We extensively
evaluate T-Miner on 1100 model instances spanning 3 ubiquitous DNN model
architectures, 5 different classification tasks, and a variety of trigger
phrases. We show that T-Miner detects Trojan and clean models with a 98.75%
overall accuracy, while achieving low false positives on clean models. We also
show that T-Miner is robust against a variety of targeted, advanced attacks
from an adaptive attacker.

本论文介绍了一种基于 seq-2-seq 生成模型的 T-Miner 防御框架，用于保护基于 DNN 的文本分类器免受特洛伊攻击的威胁，该框架无需访问训练数据集或干净的输入。经过广泛的评估，T-Miner 检测到特洛伊和干净模型的总体准确性为 98.75％，同时对干净模型的假阳性较低，且能够抵抗来自适应攻击者的各种有针对性和高级进攻。