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We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure.
A value for n-person games
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Dataset distillation using neural feature regression
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Accelerating deep learning by focusing on the biggest losers
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Discrepancy, coresets, and sketches in machine learning
Zohar Karnin and Edo Liberty · 2019
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Learning from less data: A unified data subset selection and active learning framework for computer vision
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
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Svp-cf: Selection via proxy for collaborative filtering data
Noveen Sachdeva, Carole-Jean Wu, and Julian McAuley · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Submodularity in machine learning and artificial intelligence
Jeff Bilmes · 2022
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
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Sequential variational autoencoders for collaborative filtering
Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, and Vikram Pudi · 2019
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang · 2019
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An empirical study of example forgetting during deep neural network learning
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Selection via proxy: Efficient data selection for deep learning
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On sampled metrics for item recommendation
Walid Krichene and Steffen Rendle · 2020
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Problems with shapley-value-based explanations as feature importance measures
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2022
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One-pass diversified sampling with application to terabyte-scale genomic sequence streams
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Privacy for free: How does dataset condensation help privacy?
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An empirical analysis of compute-optimal large language model training
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High-resolution image synthesis with latent diffusion models
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Gapformer: Fast autoregressive transformers meet rnns for personalized adaptive cruise control
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Beyond neural scaling laws: beating power law scaling via data pruning
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Scaling vision transformers
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Filter-enhanced mlp is all you need for sequential recommendation
Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji-Rong Wen · 2022
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Semdedup: Data-efficient learning at web-scale through semantic deduplication
Amro Abbas, Kushal Tirumala, Dániel Simig, Surya Ganguli, and Ari S Morcos · 2023
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