Jordan T. Ash

According to our database, Jordan T. Ash authored at least 20 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models.

Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin G. Jamieson, Jordan T. Ash, Robert D. Nowak

CoRR, 2024

2023

The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction.

Pratyusha Sharma, Jordan T. Ash, Dipendra Misra

CoRR, 2023

Exposing Attention Glitches with Flip-Flop Language Modeling.

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Streaming Active Learning with Deep Neural Networks.

Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash

Proceedings of the International Conference on Machine Learning, 2023

Transformers Learn Shortcuts to Automata.

Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Understanding and Improving Neural Active Learning on Heteroskedastic Distributions.

Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

2022

Neural Active Learning on Heteroskedastic Distributions.

Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb

CoRR, 2022

Eigen Memory Trees.

Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad

CoRR, 2022

Understanding Contrastive Learning Requires Incorporating Inductive Biases.

Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy

Proceedings of the International Conference on Machine Learning, 2022

Anti-Concentrated Confidence Bonuses For Scalable Exploration.

Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

Proceedings of the Tenth International Conference on Learning Representations, 2022

Investigating the Role of Negatives in Contrastive Representation Learning.

Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Gone Fishing: Neural Active Learning with Fisher Embeddings.

Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learning Composable Energy Surrogates for PDE Order Reduction.

Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Warm-Starting Neural Network Training.

Jordan T. Ash, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds.

Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

On the Difficulty of Warm-Starting Neural Network Training.

Jordan T. Ash, Ryan P. Adams

CoRR, 2019

End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations.

Gregory W. Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018

Learning Deep ResNet Blocks Sequentially using Boosting Theory.

Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire

Proceedings of the 35th International Conference on Machine Learning, 2018

2016

Multi-Source Domain Adaptation Using Approximate Label Matching.

Jordan T. Ash, Robert E. Schapire

CoRR, 2016

2011

Scratchable Devices: User-Friendly Programming for Household Appliances.

Jordan T. Ash, Monica Babes, Gal Cohen, Sameen Jalal, Sam Lichtenberg, Michael L. Littman, Vukosi Marivate, Phillip Quiza, Blase Ur, Emily Zhang

Proceedings of the Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments, 2011