James Wexler

According to our database, James Wexler authored at least 20 papers between 2018 and 2024.

Collaborative distances:

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

2024

ConstitutionalExperts: Training a Mixture of Principle-based Prompts.

Savvas Petridis, Ben Wedin, Ann Yuan, James Wexler, Nithum Thain

CoRR, 2024

Automatic Histograms: Leveraging Language Models for Text Dataset Exploration.

Emily Reif, Crystal Qian, James Wexler, Minsuk Kahng

CoRR, 2024

LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models.

Minsuk Kahng, Ian Tenney, Mahima Pushkarna, Michael Xieyang Liu, James Wexler, Emily Reif, Krystal Kallarackal, Minsuk Chang, Michael Terry, Lucas Dixon

CoRR, 2024

Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration.

Crystal Qian, James Wexler

Proceedings of the 29th International Conference on Intelligent User Interfaces, 2024

ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles.

Savvas Petridis, Benjamin D. Wedin, James Wexler, Mahima Pushkarna, Aaron Donsbach, Nitesh Goyal, Carrie J. Cai, Michael Terry

Proceedings of the 29th International Conference on Intelligent User Interfaces, 2024

2023

From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey.

Narges Ashtari, Ryan Mullins, Crystal Qian, James Wexler, Ian Tenney, Mahima Pushkarna

Proceedings of the 2023 ACM Designing Interactive Systems Conference, 2023

2021

Analyzing a Caching Model.

Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas

CoRR, 2021

Best of both worlds: local and global explanations with human-understandable concepts.

Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, Been Kim

CoRR, 2021

2020

The What-If Tool: Interactive Probing of Machine Learning Models.

James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda B. Viégas, Jimbo Wilson

IEEE Trans. Vis. Comput. Graph., 2020

Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial.

James Wexler, Mahima Pushkarna, Sara Robinson, Tolga Bolukbasi, Andrew Zaldivar

Proceedings of the FAT* '20: Conference on Fairness, 2020

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models.

Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020

2019

The Bach Doodle: Approachable music composition with machine learning at scale.

Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts, Monica Dinculescu, James Wexler, Leon Hong, Jacob Howcroft

CoRR, 2019

Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks.

Amirata Ghorbani, James Wexler, Been Kim

CoRR, 2019

Towards Automatic Concept-based Explanations.

Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Approachable Music Composition with Machine Learning at Scale.

Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts, Monica Dinculescu, James Wexler, Leon Hong, Jacob Howcroft

Proceedings of the 20th International Society for Music Information Retrieval Conference, 2019

2018

Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow.

Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg

IEEE Trans. Vis. Comput. Graph., 2018

Scalable and accurate deep learning with electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Gregory S. Corrado, Jeffrey Dean

npj Digit. Medicine, 2018

ClinicalVis: Supporting Clinical Task-Focused Design Evaluation.

Marzyeh Ghassemi, Mahima Pushkarna, James Wexler, Jesse Johnson, Paul Varghese

CoRR, 2018

Scalable and accurate deep learning for electronic health records.

Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Peter J. Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E. Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc V. Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg Corrado, Jeff Dean

CoRR, 2018

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres

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