Jordan Ash

Principal Researcher

출판물

자료 보기:

- Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
  
  Sanae Lotfi, Lucas Caccia, Alessandro Sordoni, Jordan Ash, Miroslav Dudík
  
  March 2026
  
  Publication
- The Coverage Principle: How Pre-Training Enables Post-Training
  
  Fan Chen, Audrey Huang, Noah Golowich, Sadhika Malladi, Adam Block, Jordan T. Ash, Akshay Krishnamurthy, Akshay Krishnamurthy, Dylan J. Foster
  
  ICLR 2026 | October 2025
  
  Publication
- Representation-Based Exploration for Language Models: From Test-Time to Post-Training
  
  Jens Tuyls, Dylan Foster, Akshay Krishnamurthy, Jordan Ash
  
  ICLR 2026 | October 2025
  
  DOI Publication
- On the Query Complexity of Verifier-Assisted Language Generation
  
  Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan Ash, Cyril Zhang, Andrej Risteski
  
  ICML 2025 | February 2025
  
  DOI Publication Publication
- A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
  
  Arthur Juliani, Jordan Ash
  
  NeurIPS 2024 | May 2024
  
  DOI Publication Publication
- The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
  
  Pratyusha Sharma, Jordan Ash, Dipendra Misra
  
  ICLR 2024 | December 2023
  
  DOI
- Exposing Attention Glitches with Flip-Flop Language Modeling
  
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
  
  NeurIPS 2023 | October 2023
  
  Publication
- Transformers Learn Shortcuts to Automata
  
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
  
  ICLR | May 2023
  
  oral presentation
  
  Publication
- Streaming Active Learning with Deep Neural Networks
  
  Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
  
  ICML 2023 | March 2023
  
  Publication
- Understanding Contrastive Learning Requires Incorporating Inductive Biases
  
  Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
  
  ICML 2022 | July 2022
  
  Publication
- Anti-Concentrated Confidence Bonuses for Scalable Exploration
  
  Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
  
  ICLR 2022 | April 2022
  
  Publication Publication
- Investigating the Role of Negatives in Contrastive Representation Learning
  
  Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
  
  2022 International Conference on Artificial Intelligence and Statistics | February 2022
  
  Publication
- Gone Fishing: Neural Active Learning with Fisher Embeddings
  
  Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
  
  NeurIPS 2021 | December 2021
  
  Publication
- Learning Composable Energy Surrogates for PDE Order Reduction
  
  Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
  
  NeurIPS 2020 | December 2020
  
  Publication
- On Warm-Starting Neural Network Training
  
  Jordan Ash, Ryan P. Adams
  
  NeurIPS 2020 | December 2020
  
  Publication

- Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
  
  Sanae Lotfi, Lucas Caccia, Alessandro Sordoni, Jordan Ash, Miroslav Dudík
  
  March 2026
  
  Publication
- The Coverage Principle: How Pre-Training Enables Post-Training
  
  Fan Chen, Audrey Huang, Noah Golowich, Sadhika Malladi, Adam Block, Jordan T. Ash, Akshay Krishnamurthy, Akshay Krishnamurthy, Dylan J. Foster
  
  ICLR 2026 | October 2025
  
  Publication
- Representation-Based Exploration for Language Models: From Test-Time to Post-Training
  
  Jens Tuyls, Dylan Foster, Akshay Krishnamurthy, Jordan Ash
  
  ICLR 2026 | October 2025
  
  DOI Publication
- On the Query Complexity of Verifier-Assisted Language Generation
  
  Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan Ash, Cyril Zhang, Andrej Risteski
  
  ICML 2025 | February 2025
  
  DOI Publication Publication
- A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
  
  Arthur Juliani, Jordan Ash
  
  NeurIPS 2024 | May 2024
  
  DOI Publication Publication
- The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
  
  Pratyusha Sharma, Jordan Ash, Dipendra Misra
  
  ICLR 2024 | December 2023
  
  DOI
- Exposing Attention Glitches with Flip-Flop Language Modeling
  
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
  
  NeurIPS 2023 | October 2023
  
  Publication
- Transformers Learn Shortcuts to Automata
  
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
  
  ICLR | May 2023
  
  oral presentation
  
  Publication
- Streaming Active Learning with Deep Neural Networks
  
  Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
  
  ICML 2023 | March 2023
  
  Publication
- Understanding Contrastive Learning Requires Incorporating Inductive Biases
  
  Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
  
  ICML 2022 | July 2022
  
  Publication
- Anti-Concentrated Confidence Bonuses for Scalable Exploration
  
  Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
  
  ICLR 2022 | April 2022
  
  Publication Publication
- Investigating the Role of Negatives in Contrastive Representation Learning
  
  Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
  
  2022 International Conference on Artificial Intelligence and Statistics | February 2022
  
  Publication
- Gone Fishing: Neural Active Learning with Fisher Embeddings
  
  Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
  
  NeurIPS 2021 | December 2021
  
  Publication
- Learning Composable Energy Surrogates for PDE Order Reduction
  
  Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
  
  NeurIPS 2020 | December 2020
  
  Publication
- On Warm-Starting Neural Network Training
  
  Jordan Ash, Ryan P. Adams
  
  NeurIPS 2020 | December 2020
  
  Publication
- On the Query Complexity of Verifier-Assisted Language Generation
  
  Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan Ash, Cyril Zhang, Andrej Risteski
  
  ICML 2025 | February 2025
  
  DOI Publication Publication

- Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
  
  Sanae Lotfi, Lucas Caccia, Alessandro Sordoni, Jordan Ash, Miroslav Dudík
  
  March 2026
  
  Publication
- The Coverage Principle: How Pre-Training Enables Post-Training
  
  Fan Chen, Audrey Huang, Noah Golowich, Sadhika Malladi, Adam Block, Jordan T. Ash, Akshay Krishnamurthy, Akshay Krishnamurthy, Dylan J. Foster
  
  ICLR 2026 | October 2025
  
  Publication
- Representation-Based Exploration for Language Models: From Test-Time to Post-Training
  
  Jens Tuyls, Dylan Foster, Akshay Krishnamurthy, Jordan Ash
  
  ICLR 2026 | October 2025
  
  DOI Publication
- On the Query Complexity of Verifier-Assisted Language Generation
  
  Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan Ash, Cyril Zhang, Andrej Risteski
  
  ICML 2025 | February 2025
  
  DOI Publication Publication
- A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
  
  Arthur Juliani, Jordan Ash
  
  NeurIPS 2024 | May 2024
  
  DOI Publication Publication
- The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
  
  Pratyusha Sharma, Jordan Ash, Dipendra Misra
  
  ICLR 2024 | December 2023
  
  DOI
- Exposing Attention Glitches with Flip-Flop Language Modeling
  
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
  
  NeurIPS 2023 | October 2023
  
  Publication
- Transformers Learn Shortcuts to Automata
  
  Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
  
  ICLR | May 2023
  
  oral presentation
  
  Publication
- Streaming Active Learning with Deep Neural Networks
  
  Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan Ash
  
  ICML 2023 | March 2023
  
  Publication
- Understanding Contrastive Learning Requires Incorporating Inductive Biases
  
  Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy
  
  ICML 2022 | July 2022
  
  Publication
- Anti-Concentrated Confidence Bonuses for Scalable Exploration
  
  Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
  
  ICLR 2022 | April 2022
  
  Publication Publication
- Investigating the Role of Negatives in Contrastive Representation Learning
  
  Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
  
  2022 International Conference on Artificial Intelligence and Statistics | February 2022
  
  Publication
- Gone Fishing: Neural Active Learning with Fisher Embeddings
  
  Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
  
  NeurIPS 2021 | December 2021
  
  Publication
- Learning Composable Energy Surrogates for PDE Order Reduction
  
  Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
  
  NeurIPS 2020 | December 2020
  
  Publication
- On Warm-Starting Neural Network Training
  
  Jordan Ash, Ryan P. Adams
  
  NeurIPS 2020 | December 2020
  
  Publication

Jordan Ash

출판물

Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts

The Coverage Principle: How Pre-Training Enables Post-Training

Representation-Based Exploration for Language Models: From Test-Time to Post-Training

On the Query Complexity of Verifier-Assisted Language Generation

A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

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

Exposing Attention Glitches with Flip-Flop Language Modeling

Transformers Learn Shortcuts to Automata

Streaming Active Learning with Deep Neural Networks

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Anti-Concentrated Confidence Bonuses for Scalable Exploration

Investigating the Role of Negatives in Contrastive Representation Learning

Gone Fishing: Neural Active Learning with Fisher Embeddings

Learning Composable Energy Surrogates for PDE Order Reduction

On Warm-Starting Neural Network Training

Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts

The Coverage Principle: How Pre-Training Enables Post-Training

Representation-Based Exploration for Language Models: From Test-Time to Post-Training

On the Query Complexity of Verifier-Assisted Language Generation

A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

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

Exposing Attention Glitches with Flip-Flop Language Modeling

Transformers Learn Shortcuts to Automata

Streaming Active Learning with Deep Neural Networks

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Anti-Concentrated Confidence Bonuses for Scalable Exploration

Investigating the Role of Negatives in Contrastive Representation Learning

Gone Fishing: Neural Active Learning with Fisher Embeddings

Learning Composable Energy Surrogates for PDE Order Reduction

On Warm-Starting Neural Network Training

On the Query Complexity of Verifier-Assisted Language Generation

Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts

The Coverage Principle: How Pre-Training Enables Post-Training

Representation-Based Exploration for Language Models: From Test-Time to Post-Training

On the Query Complexity of Verifier-Assisted Language Generation

A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

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

Exposing Attention Glitches with Flip-Flop Language Modeling

Transformers Learn Shortcuts to Automata

Streaming Active Learning with Deep Neural Networks

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Anti-Concentrated Confidence Bonuses for Scalable Exploration

Investigating the Role of Negatives in Contrastive Representation Learning

Gone Fishing: Neural Active Learning with Fisher Embeddings

Learning Composable Energy Surrogates for PDE Order Reduction

On Warm-Starting Neural Network Training