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2020 – today
- 2024
[c11]Savvas Petridis, Ben Wedin, Ann Yuan, James Wexler, Nithum Thain:
ConstitutionalExperts: Training a Mixture of Principle-based Prompts. ACL (Short Papers) 2024: 574-582- [c10]Emily Reif
, Crystal Qian, James Wexler, Minsuk Kahng:
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration. CHI Extended Abstracts 2024: 53:1-53:9 - [c9]Minsuk Kahng, Ian Tenney, Mahima Pushkarna, Michael Xieyang Liu, James Wexler, Emily Reif, Krystal Kallarackal, Minsuk Chang, Michael Terry, Lucas Dixon:
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models. CHI Extended Abstracts 2024: 216:1-216:7 - [c8]Crystal Qian, James Wexler:
Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration. IUI 2024: 370-384 - [c7]Savvas Petridis, Benjamin D. Wedin, James Wexler, Mahima Pushkarna, Aaron Donsbach, Nitesh Goyal, Carrie J. Cai, Michael Terry:
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles. IUI 2024: 853-868 - [i13]Minsuk Kahng, Ian Tenney, Mahima Pushkarna, Michael Xieyang Liu, James Wexler, Emily Reif, Krystal Kallarackal, Minsuk Chang, Michael Terry, Lucas Dixon:
LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Models. CoRR abs/2402.10524 (2024) - [i12]Emily Reif, Crystal Qian, James Wexler, Minsuk Kahng:
Automatic Histograms: Leveraging Language Models for Text Dataset Exploration. CoRR abs/2402.14880 (2024) - [i11]Crystal Qian, James Wexler:
Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration. CoRR abs/2402.18498 (2024) - [i10]Savvas Petridis, Ben Wedin, Ann Yuan, James Wexler, Nithum Thain:
ConstitutionalExperts: Training a Mixture of Principle-based Prompts. CoRR abs/2403.04894 (2024) - 2023
- [c6]Narges Ashtari, Ryan Mullins, Crystal Qian, James Wexler, Ian Tenney, Mahima Pushkarna:
From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey. Conference on Designing Interactive Systems 2023: 2304-2325 - [i9]Savvas Petridis, Ben Wedin, James Wexler, Aaron Donsbach, Mahima Pushkarna, Nitesh Goyal, Carrie J. Cai, Michael Terry:
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles. CoRR abs/2310.15428 (2023) - 2021
- [i8]Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, Been Kim:
Best of both worlds: local and global explanations with human-understandable concepts. CoRR abs/2106.08641 (2021) - [i7]Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas:
Analyzing a Caching Model. CoRR abs/2112.06989 (2021) - 2020
- [j3]James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda B. Viégas, Jimbo Wilson:
The What-If Tool: Interactive Probing of Machine Learning Models. IEEE Trans. Vis. Comput. Graph. 26(1): 56-65 (2020) - [c5]Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan:
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. EMNLP (Demos) 2020: 107-118 - [c4]James Wexler, Mahima Pushkarna, Sara Robinson, Tolga Bolukbasi, Andrew Zaldivar:
Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial. FAT* 2020: 705 - [i6]Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, Ann Yuan:
The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models. CoRR abs/2008.05122 (2020)
2010 – 2019
- 2019
- [c3]Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts, Monica Dinculescu, James Wexler, Leon Hong, Jacob Howcroft:
Approachable Music Composition with Machine Learning at Scale. ISMIR 2019: 793-800 - [c2]Amirata Ghorbani, James Wexler, James Y. Zou, Been Kim:
Towards Automatic Concept-based Explanations. NeurIPS 2019: 9273-9282 - [i5]Amirata Ghorbani, James Wexler, Been Kim:
Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks. CoRR abs/1902.03129 (2019) - [i4]James Wexler, Mahima Pushkarna, Tolga Bolukbasi, Martin Wattenberg, Fernanda B. Viégas, Jimbo Wilson:
The What-If Tool: Interactive Probing of Machine Learning Models. CoRR abs/1907.04135 (2019) - [i3]Cheng-Zhi Anna Huang, Curtis Hawthorne, Adam Roberts, Monica Dinculescu, James Wexler, Leon Hong, Jacob Howcroft:
The Bach Doodle: Approachable music composition with machine learning at scale. CoRR abs/1907.06637 (2019) - 2018
- [j2]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:
Scalable and accurate deep learning with electronic health records. npj Digit. Medicine 1 (2018) - [j1]Kanit Wongsuphasawat, Daniel Smilkov, James Wexler, Jimbo Wilson, Dandelion Mané, Doug Fritz, Dilip Krishnan, Fernanda B. Viégas, Martin Wattenberg:
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. IEEE Trans. Vis. Comput. Graph. 24(1): 1-12 (2018) - [c1]Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres:
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). ICML 2018: 2673-2682 - [i2]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:
Scalable and accurate deep learning for electronic health records. CoRR abs/1801.07860 (2018) - [i1]Marzyeh Ghassemi, Mahima Pushkarna, James Wexler, Jesse Johnson, Paul Varghese:
ClinicalVis: Supporting Clinical Task-Focused Design Evaluation. CoRR abs/1810.05798 (2018)
Coauthor Index
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last updated on 2024-10-12 23:02 CEST by the dblp team
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