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Found 11 papers, 7 papers with code
Off-Policy Evaluation for Large Action Spaces via Policy Convolution
To address these challenges, we introduce the Policy Convolution (PC) family of estimators.
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.
Data Distillation: A Survey
The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets.
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)
On Sampling Collaborative Filtering Datasets
We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms.
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.
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.
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.
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.
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 #2 on
Recommendation Systems
on MovieLens 1M
(nDCG@100 metric)
