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 (HCI (3), 2011)

Multi-Source Domain Adaptation Using Approximate Label Matching. - Jordan T. Ash; Robert E. Schapire (CoRR, 2016)

Learning Deep ResNet Blocks Sequentially using Boosting Theory. - Furong Huang; Jordan T. Ash; John Langford 001; Robert E. Schapire (ICML, 2018)

End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations. - Gregory W. Gundersen; Bianca Dumitrascu; Jordan T. Ash; Barbara E. Engelhardt (UAI, 2019)

On the Difficulty of Warm-Starting Neural Network Training. - Jordan T. Ash; Ryan P. Adams (CoRR, 2019)

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. - Jordan T. Ash; Chicheng Zhang; Akshay Krishnamurthy; John Langford 001; Alekh Agarwal (ICLR, 2020)

On Warm-Starting Neural Network Training. - Jordan T. Ash; Ryan P. Adams (NeurIPS, 2020)

Learning Composable Energy Surrogates for PDE Order Reduction. - Alex Beatson; Jordan T. Ash; Geoffrey Roeder; Tianju Xue; Ryan P. Adams (NeurIPS, 2020)

Gone Fishing: Neural Active Learning with Fisher Embeddings. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (NeurIPS, 2021)

Investigating the Role of Negatives in Contrastive Representation Learning. - Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Dipendra Misra (AISTATS, 2022)

Anti-Concentrated Confidence Bonuses For Scalable Exploration. - Jordan T. Ash; Cyril Zhang; Surbhi Goel; Akshay Krishnamurthy; Sham M. Kakade (ICLR, 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 (ICML, 2022)

Eigen Memory Trees. - Mark Rucker; Jordan T. Ash; John Langford 001; Paul Mineiro; Ida Momennejad (CoRR, 2022)

Neural Active Learning on Heteroskedastic Distributions. - Savya Khosla; Chew Kin Whye; Jordan T. Ash; Cyril Zhang; Kenji Kawaguchi; Alex Lamb (CoRR, 2022)

Understanding and Improving Neural Active Learning on Heteroskedastic Distributions. - Savya Khosla; Chew Kin Whye; Jordan T. Ash; Cyril Zhang; Kenji Kawaguchi; Alex Lamb (ECAI, 2023)

Transformers Learn Shortcuts to Automata. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (ICLR, 2023)

Streaming Active Learning with Deep Neural Networks. - Akanksha Saran; Safoora Yousefi; Akshay Krishnamurthy; John Langford 001; Jordan T. Ash (ICML, 2023)

Exposing Attention Glitches with Flip-Flop Language Modeling. - Bingbin Liu; Jordan T. Ash; Surbhi Goel; Akshay Krishnamurthy; Cyril Zhang (NeurIPS, 2023)

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. - Gantavya Bhatt; Yifang Chen 001; Arnav Mohanty Das; Jifan Zhang; Sang T. Truong; Stephen Mussmann; Yinglun Zhu; Jeff A. Bilmes; Simon S. Du; Kevin G. Jamieson; Jordan T. Ash; Robert D. Nowak (ACL (Findings), 2024)

The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction. - Pratyusha Sharma; Jordan T. Ash; Dipendra Misra (ICLR, 2024)

A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning. - Arthur Juliani; Jordan T. Ash (NeurIPS, 2024)

Self-Improvement in Language Models: The Sharpening Mechanism. - Audrey Huang; Adam Block; Dylan J. Foster; Dhruv Rohatgi; Cyril Zhang; Max Simchowitz; Jordan T. Ash; Akshay Krishnamurthy (ICLR, 2025)

On the Query Complexity of Verifier-Assisted Language Generation. - Edoardo Botta; Yuchen Li; Aashay Mehta; Jordan T. Ash; Cyril Zhang; Andrej Risteski (CoRR, 2025)

Generative Modeling of Individual Behavior at Scale. - Nabil Omi; Lucas Caccia; Anurag Sarkar; Jordan T. Ash; Siddhartha Sen 001 (CoRR, 2025)