Search Results for author:
Found 11 papers, 7 papers with code
Sequential Variational Autoencoders for Collaborative Filtering
We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network.
Ranked #1 on
Recommendation Systems
on MovieLens 1M
(nDCG@100 metric)
How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements
We investigate a growing body of work that seeks to improve recommender systems through the use of review text.
Off-policy Bandits with Deficient Support
Learning effective contextual-bandit policies from past actions of a deployed system is highly desirable in many settings (e. g. voice assistants, recommendation, search), since it enables the reuse of large amounts of log data.
SVP-CF: Selection via Proxy for Collaborative Filtering Data
As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance -- masking performance deficiencies in algorithms or altering the relative performance of algorithms, as compared to models trained on the complete dataset.
ECLARE: Extreme Classification with Label Graph Correlations
This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
On Sampling Collaborative Filtering Datasets
We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms.
Infinite Recommendation Networks: A Data-Centric Approach
We leverage the Neural Tangent Kernel and its equivalence to training infinitely-wide neural networks to devise $\infty$-AE: an autoencoder with infinitely-wide bottleneck layers.
Ranked #1 on
Recommendation Systems
on Douban
(AUC metric)
Data Distillation: A Survey
The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets.
Farzi Data: Autoregressive Data Distillation
We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure.
Off-Policy Evaluation for Large Action Spaces via Policy Convolution
To address these challenges, we introduce the Policy Convolution (PC) family of estimators.
