Ashish Khetan

Orcid: 0000-0002-8856-1595

According to our database1, Ashish Khetan authored at least 20 papers between 2016 and 2023.

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Bibliography

2023
Representation Projection Invariance Mitigates Representation Collapse.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022
Improving language models fine-tuning with representation consistency targets.
CoRR, 2022

Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
PacGAN: The Power of Two Samples in Generative Adversarial Networks.
IEEE J. Sel. Areas Inf. Theory, 2020

TabTransformer: Tabular Data Modeling Using Contextual Embeddings.
CoRR, 2020

PruneNet: Channel Pruning via Global Importance.
CoRR, 2020

schuBERT: Optimizing Elements of BERT.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019
Spectrum Estimation from a Few Entries.
J. Mach. Learn. Res., 2019

Robust conditional GANs under missing or uncertain labels.
CoRR, 2019

DARC: Differentiable ARchitecture Compression.
CoRR, 2019

2018
Generalized Rank-Breaking: Computational and Statistical Tradeoffs.
J. Mach. Learn. Res., 2018

Number of Connected Components in a Graph: Estimation via Counting Patterns.
CoRR, 2018

Robustness of conditional GANs to noisy labels.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning From Noisy Singly-labeled Data.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Matrix Norm Estimation from a Few Entries.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Data-driven Rank Breaking for Efficient Rank Aggregation.
J. Mach. Learn. Res., 2016

Reliable Crowdsourcing under the Generalized Dawid-Skene Model.
CoRR, 2016

Achieving budget-optimality with adaptive schemes in crowdsourcing.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Computational and Statistical Tradeoffs in Learning to Rank.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016


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