Search Results for author: Noveen Sachdeva

Found 8 papers, 7 papers with code

Data Distillation: A Survey

1 code implementation11 Jan 2023 Noveen Sachdeva, Julian McAuley

The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets.

Recommendation Systems

Infinite Recommendation Networks: A Data-Centric Approach

5 code implementations3 Jun 2022 Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian McAuley

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)

Information Retrieval Recommendation Systems

On Sampling Collaborative Filtering Datasets

1 code implementation13 Jan 2022 Noveen Sachdeva, Carole-Jean Wu, Julian McAuley

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms.

Collaborative Filtering Recommendation Systems

ECLARE: Extreme Classification with Label Graph Correlations

1 code implementation31 Jul 2021 Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, Manik Varma

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.

Classification Extreme Multi-Label Classification +6

SVP-CF: Selection via Proxy for Collaborative Filtering Data

no code implementations11 Jul 2021 Noveen Sachdeva, Carole-Jean Wu, Julian McAuley

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.

Collaborative Filtering Recommendation Systems

Off-policy Bandits with Deficient Support

1 code implementation16 Jun 2020 Noveen Sachdeva, Yi Su, Thorsten Joachims

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

1 code implementation25 May 2020 Noveen Sachdeva, Julian McAuley

We investigate a growing body of work that seeks to improve recommender systems through the use of review text.

Recommendation Systems

Sequential Variational Autoencoders for Collaborative Filtering

1 code implementation25 Nov 2018 Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, Vikram Pudi

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)

Recommendation Systems

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