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Jordan T. Ash
Jordan T. Ash
Microsoft Research NYC
Verified email at princeton.edu - Homepage
Title
Cited by
Cited by
Year
Deep batch active learning by diverse, uncertain gradient lower bounds
JT Ash, C Zhang, A Krishnamurthy, J Langford, A Agarwal
International Conference on Learning Representations, 2020
6722020
On warm-starting neural network training
JT Ash, RP Adams
Neural Information Processing Systems, 2020
148*2020
Learning deep resnet blocks sequentially using boosting theory
F Huang, JT Ash, J Langford, R Schapire
International Conference on Machine Learning, 2017
1222017
Understanding contrastive learning requires incorporating inductive biases
N Saunshi, JT Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ...
International Conference on Machine Learning, 19250-19286, 2022
992022
Transformers learn shortcuts to automata
B Liu, JT Ash, S Goel, A Krishnamurthy, C Zhang
International Conference on Learning Representations, 2023
902023
Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology
JT Ash, G Darnell, D Munro, B Engelhardt
Nature Communications, 458711, 2021
632021
Automated particle picking for low-contrast macromolecules in cryo-electron microscopy
R Langlois, J Pallesen, JT Ash, DN Ho, JL Rubinstein, J Frank
Journal of structural biology 186 (1), 1-7, 2014
612014
Gone Fishing: Neural Active Learning with Fisher Embeddings
JT Ash, S Goel, A Krishnamurthy, S Kakade
Neural Information Processing Systems, 2021
522021
Investigating the Role of Negatives in Contrastive Representation Learning
JT Ash, S Goel, A Krishnamurthy, D Misra
Artificial Intelligence and Statistics, 2022
442022
A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation
T Xue, A Beatson, M Chiaramonte, G Roeder, JT Ash, Y Menguc, ...
Soft matter 16 (32), 7524-7534, 2020
412020
End-to-end training of deep probabilistic CCA on paired biomedical observations
G Gundersen, B Dumitrascu, JT Ash, BE Engelhardt
Uncertainty in Artificial Intelligence, 2020
252020
Learning Composable Energy Surrogates for PDE Order Reduction
A Beatson, JT Ash, G Roeder, T Xie, RP Adams
Neural Information Processing Systems, 2020
182020
Exposing Attention Glitches with Flip-Flop Language Modeling
B Liu, JT Ash, S Goel, A Krishnamurthy, C Zhang
Neural Information Processing Systems, 2023
152023
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
P Sharma, JT Ash, D Misra
International Conference on Learning Representations, 2024
102024
Anti-Concentrated Confidence Bonuses for Scalable Exploration
JT Ash, C Zhang, S Goel, A Krishnamurthy, S Kakade
International Conference on Learning Representations, 2022
92022
Unsupervised domain adaptation using approximate label matching
JT Ash, RE Schapire, BE Engelhardt
ICML workshop on implicit generative models, 2017
82017
Scratchable devices: user-friendly programming for household appliances
J Ash, M Babes, G Cohen, S Jalal, S Lichtenberg, M Littman, V Marivate, ...
Human-Computer Interaction. Towards Mobile and Intelligent Interaction …, 2011
82011
Streaming Active Learning with Deep Neural Networks
A Saran, S Yousefi, A Krishnamurthy, J Langford, JT Ash
International Conference On Machine Learning, 2023
62023
An experimental design framework for label-efficient supervised finetuning of large language models
G Bhatt, Y Chen, AM Das, J Zhang, ST Truong, S Mussmann, Y Zhu, ...
arXiv preprint arXiv:2401.06692, 2024
22024
Neural Active Learning on Heteroskedastic Distributions
S Khosla, CK Whye, JT Ash, C Zhang, K Kawaguchi, A Lamb
arXiv preprint arXiv:2211.00928, 2022
12022
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Articles 1–20