• Publications
  • Influence
YouTube-8M: A Large-Scale Video Classification Benchmark
TLDR
YouTube-8M is introduced, the largest multi-label video classification dataset, composed of ~8 million videos (500K hours of video), annotated with a vocabulary of 4800 visual entities, and various (modest) classification models are trained on the dataset. Expand
Local Low-Rank Matrix Approximation
TLDR
A new matrix approximation model is proposed where it is assumed that the matrix is locally of low-rank, leading to a representation of the observed matrix as a weighted sum ofLow-rank matrices, and improvements in prediction accuracy over classical approaches for recommendation tasks. Expand
Local collaborative ranking
TLDR
The experiments indicate that the combination of a mixture of local low-rank matrices each of which was trained to minimize a ranking loss outperforms many of the currently used state-of-the-art recommendation systems. Expand
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
TLDR
The proposed N-GCN model improves state-of-the-art baselines on all of the challenging node classification tasks the authors consider: Cora, Citeseer, Pubmed, and PPI, and has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, and resilience to adversarial input perturbations. Expand
A Comparative Study of Collaborative Filtering Algorithms
TLDR
This paper conducts a study comparing several collaborative ltering techniques, both classic and recent state-of-the-art, in a variety of experimental contexts to identify what algorithms work well and in what conditions. Expand
LLORMA: Local Low-Rank Matrix Approximation
TLDR
This paper proposes, analyzes, and experiment with two procedures, one parallel and the other global, for constructing local matrix approximations, which approximate the observed matrix as a weighted sum of low-rank matrices. Expand
Learning multiple-question decision trees for cold-start recommendation
TLDR
A novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split is proposed, which outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts. Expand
PREA: personalized recommendation algorithms toolkit
TLDR
This paper describes an open-source toolkit implementing many recommendation algorithms as well as popular evaluation metrics, and in contrast to other packages, this toolkit implements recent state-of-the-art algorithms as to most classic algorithms. Expand
The 2nd YouTube-8M Large-Scale Video Understanding Challenge
TLDR
This paper briefly introduces the YouTube-8M dataset and challenge task, followed by participants statistics and result analysis, and summarizes proposed ideas by participants, including architectures, temporal aggregation methods, ensembling and distillation, data augmentation, and more. Expand
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
TLDR
A set of five ethical concerns in the particular case of auditing commercial facial processing technology are demonstrated, highlighting additional design considerations and ethical tensions the auditor needs to be aware of so as not to exacerbate or complement the harms propagated by the audited system. Expand
...
1
2
3
4
5
...