191 research outputs found
Differentially Private Regression for Discrete-Time Survival Analysis
In survival analysis, regression models are used to understand the effects of
explanatory variables (e.g., age, sex, weight, etc.) to the survival
probability. However, for sensitive survival data such as medical data, there
are serious concerns about the privacy of individuals in the data set when
medical data is used to fit the regression models. The closest work addressing
such privacy concerns is the work on Cox regression which linearly projects the
original data to a lower dimensional space. However, the weakness of this
approach is that there is no formal privacy guarantee for such projection. In
this work, we aim to propose solutions for the regression problem in survival
analysis with the protection of differential privacy which is a golden standard
of privacy protection in data privacy research. To this end, we extend the
Output Perturbation and Objective Perturbation approaches which are originally
proposed to protect differential privacy for the Empirical Risk Minimization
(ERM) problems. In addition, we also propose a novel sampling approach based on
the Markov Chain Monte Carlo (MCMC) method to practically guarantee
differential privacy with better accuracy. We show that our proposed approaches
achieve good accuracy as compared to the non-private results while guaranteeing
differential privacy for individuals in the private data set.Comment: 19 pages, CIKM1
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
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Submodular memetic approximation for multiobjective parallel test paper generation
Parallel test paper generation is a biobjective distributed resource optimization problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified assessment criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in both of the collective objective functions. In this paper, we propose a submodular memetic approximation algorithm for solving this problem. The proposed algorithm is an adaptive memetic algorithm (MA), which exploits the submodular property of the collective objective functions to design greedy-based approximation algorithms for enhancing steps of the multiobjective MA. Synergizing the intensification of submodular local search mechanism with the diversification of the population-based submodular crossover operator, our algorithm can jointly optimize the total quality maximization objective and the fairness quality maximization objective. Our MA can achieve provable near-optimal solutions in a huge search space of large datasets in efficient polynomial runtime. Performance results on various datasets have shown that our algorithm has drastically outperformed the current techniques in terms of paper quality and runtime efficiency
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