Jordan T. Ash - RankMe

44 papers A* 14A 3Journal 24Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts. CoRR Sanae Lotfi, Lucas Caccia, Alessandro Sordoni, Jordan T. Ash, Miroslav Dudik
2025	J	`jnl`	Generative Modeling of Individual Behavior at Scale. CoRR Nabil Omi, Lucas Caccia, Anurag Sarkar, Jordan T. Ash, Siddhartha Sen
2025	A*	`conf`	On the Query Complexity of Verifier-Assisted Language Generation. ICML Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan T. Ash, Cyril Zhang, Andrej Risteski
2025	J	`jnl`	On the Query Complexity of Verifier-Assisted Language Generation. CoRR Edoardo Botta, Yuchen Li, Aashay Mehta, Jordan T. Ash, Cyril Zhang, Andrej Risteski
2025	J	`jnl`	Representation-Based Exploration for Language Models: From Test-Time to Post-Training. CoRR Jens Tuyls, Dylan J. Foster, Akshay Krishnamurthy, Jordan T. Ash
2025	A*	`conf`	Self-Improvement in Language Models: The Sharpening Mechanism. ICLR Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy
2025	J	`jnl`	The Coverage Principle: How Pre-Training Enables Post-Training. CoRR Fan Chen, Audrey Huang, Noah Golowich, Sadhika Malladi, Adam Block, Jordan T. Ash, Akshay Krishnamurthy, Dylan J. Foster
2025	J	`jnl`	Towards Active Synthetic Data Generation for Finetuning Language Models. CoRR Samuel Kessler, Menglin Xia, Daniel Madrigal Díaz, Dongge Han, Helia Heshemi, Saravan Rajmohan, Victor Rühle, Jordan T. Ash
2024	A*	`conf`	A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning. NeurIPS Arthur Juliani, Jordan T. Ash
2024	J	`jnl`	A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning. CoRR Arthur Juliani, Jordan T. Ash
2024	—	`conf`	An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. ACL (Findings) Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak
2024	J	`jnl`	An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. CoRR Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak
2024	J	`jnl`	Self-Improvement in Language Models: The Sharpening Mechanism. CoRR Audrey Huang, Adam Block, Dylan J. Foster, Dhruv Rohatgi, Cyril Zhang, Max Simchowitz, Jordan T. Ash, Akshay Krishnamurthy
2024	A*	`conf`	The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction. ICLR Pratyusha Sharma, Jordan T. Ash, Dipendra Misra
2023	A*	`conf`	Exposing Attention Glitches with Flip-Flop Language Modeling. NeurIPS Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
2023	J	`jnl`	Exposing Attention Glitches with Flip-Flop Language Modeling. CoRR Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
2023	A*	`conf`	Streaming Active Learning with Deep Neural Networks. ICML Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
2023	J	`jnl`	Streaming Active Learning with Deep Neural Networks. CoRR Akanksha Saran, Safoora Yousefi, Akshay Krishnamurthy, John Langford, Jordan T. Ash
2023	J	`jnl`	The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction. CoRR Pratyusha Sharma, Jordan T. Ash, Dipendra Misra
2023	A*	`conf`	Transformers Learn Shortcuts to Automata. ICLR Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
2023	A	`conf`	Understanding and Improving Neural Active Learning on Heteroskedastic Distributions. ECAI Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb
2022	A*	`conf`	Anti-Concentrated Confidence Bonuses For Scalable Exploration. ICLR Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
2022	J	`jnl`	Eigen Memory Trees. CoRR Mark Rucker, Jordan T. Ash, John Langford, Paul Mineiro, Ida Momennejad
2022	A	`conf`	Investigating the Role of Negatives in Contrastive Representation Learning. AISTATS Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2022	J	`jnl`	Neural Active Learning on Heteroskedastic Distributions. CoRR Savya Khosla, Chew Kin Whye, Jordan T. Ash, Cyril Zhang, Kenji Kawaguchi, Alex Lamb
2022	J	`jnl`	Transformers Learn Shortcuts to Automata. CoRR Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang
2022	A*	`conf`	Understanding Contrastive Learning Requires Incorporating Inductive Biases. ICML Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
2022	J	`jnl`	Understanding Contrastive Learning Requires Incorporating Inductive Biases. CoRR Nikunj Saunshi, Jordan T. Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham M. Kakade, Akshay Krishnamurthy
2021	J	`jnl`	Anti-Concentrated Confidence Bonuses for Scalable Exploration. CoRR Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
2021	A*	`conf`	Gone Fishing: Neural Active Learning with Fisher Embeddings. NeurIPS Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
2021	J	`jnl`	Gone Fishing: Neural Active Learning with Fisher Embeddings. CoRR Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade
2021	J	`jnl`	Investigating the Role of Negatives in Contrastive Representation Learning. CoRR Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Dipendra Misra
2020	A*	`conf`	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. ICLR Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2020	A*	`conf`	Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
2020	J	`jnl`	Learning Composable Energy Surrogates for PDE Order Reduction. CoRR Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
2020	A*	`conf`	On Warm-Starting Neural Network Training. NeurIPS Jordan T. Ash, Ryan P. Adams
2020	—	`—`	Towards Flexible Active And Online Learning With Neural Networks. Jordan T. Ash
2019	J	`jnl`	Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. CoRR Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, Alekh Agarwal
2019	A	`conf`	End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations. UAI Gregory W. Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt
2019	J	`jnl`	On the Difficulty of Warm-Starting Neural Network Training. CoRR Jordan T. Ash, Ryan P. Adams
2018	A*	`conf`	Learning Deep ResNet Blocks Sequentially using Boosting Theory. ICML Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
2017	J	`jnl`	Learning Deep ResNet Blocks Sequentially using Boosting Theory. CoRR Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire
2016	J	`jnl`	Multi-Source Domain Adaptation Using Approximate Label Matching. CoRR Jordan T. Ash, Robert E. Schapire
2011	—	`conf`	Scratchable Devices: User-Friendly Programming for Household Appliances. HCI (3) Jordan T. Ash, Monica Babes, Gal Cohen, Sameen Jalal, Sam Lichtenberg, Michael L. Littman, Vukosi Marivate, Phillip Quiza, Blase Ur, Emily Zhang