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Chandan Singh
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Publications
- 2024
- [i27]Yanda Chen, Chandan Singh, Xiaodong Liu, Simiao Zuo, Bin Yu, He He, Jianfeng Gao:
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning. CoRR abs/2401.13986 (2024) - [i26]Chandan Singh, Jeevana Priya Inala, Michel Galley, Rich Caruana, Jianfeng Gao:
Rethinking Interpretability in the Era of Large Language Models. CoRR abs/2402.01761 (2024) - [i25]Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang, Jianfeng Gao:
Learning a Decision Tree Algorithm with Transformers. CoRR abs/2402.03774 (2024) - [i24]Zelalem Gero, Chandan Singh, Yiqing Xie, Sheng Zhang, Tristan Naumann, Jianfeng Gao, Hoifung Poon:
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries. CoRR abs/2403.01002 (2024) - 2023
- [c23]Chandan Singh, John X. Morris, Jyoti Aneja, Alexander M. Rush, Jianfeng Gao:
Explaining Data Patterns in Natural Language with Language Models. BlackboxNLP@EMNLP 2023: 31-55 - [c22]Chandan Singh, John X. Morris, Alexander M. Rush, Jianfeng Gao, Yuntian Deng:
Tree Prompting: Efficient Task Adaptation without Fine-Tuning. EMNLP 2023: 6253-6267 - [i23]Chandan Singh, Aliyah R. Hsu, Richard Antonello, Shailee Jain, Alexander G. Huth, Bin Yu, Jianfeng Gao:
Explaining black box text modules in natural language with language models. CoRR abs/2305.09863 (2023) - [i22]Zelalem Gero, Chandan Singh, Hao Cheng, Tristan Naumann, Michel Galley, Jianfeng Gao, Hoifung Poon:
Self-Verification Improves Few-Shot Clinical Information Extraction. CoRR abs/2306.00024 (2023) - [i21]John X. Morris, Chandan Singh, Alexander M. Rush, Jianfeng Gao, Yuntian Deng:
Tree Prompting: Efficient Task Adaptation without Fine-Tuning. CoRR abs/2310.14034 (2023) - [i20]Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao:
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs. CoRR abs/2311.02262 (2023) - 2022
- [j52]James Duncan, Rush Kapoor, Abhineet Agarwal, Chandan Singh, Bin Yu:
VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS. J. Open Source Softw. 7(69): 3895 (2022) - [c20]Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu:
Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. ICML 2022: 111-135 - [i19]Yan Shuo Tan, Chandan Singh, Keyan Nasseri, Abhineet Agarwal, Bin Yu:
Fast Interpretable Greedy-Tree Sums (FIGS). CoRR abs/2201.11931 (2022) - [i18]Abhineet Agarwal, Yan Shuo Tan, Omer Ronen, Chandan Singh, Bin Yu:
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods. CoRR abs/2202.00858 (2022) - [i17]Keyan Nasseri, Chandan Singh, James Duncan, Aaron Kornblith, Bin Yu:
Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data. CoRR abs/2205.15135 (2022) - [i15]Chandan Singh, Jianfeng Gao:
Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models. CoRR abs/2209.11799 (2022) - [i14]Chandan Singh, John X. Morris, Jyoti Aneja, Alexander M. Rush, Jianfeng Gao:
Explaining Patterns in Data with Language Models via Interpretable Autoprompting. CoRR abs/2210.01848 (2022) - 2021
- [j49]Chandan Singh, Keyan Nasseri, Yan Shuo Tan, Tiffany M. Tang, Bin Yu:
imodels: a python package for fitting interpretable models. J. Open Source Softw. 6(61): 3192 (2021) - [c16]Wooseok Ha, Chandan Singh, François Lanusse, Srigokul Upadhyayula, Bin Yu:
Adaptive wavelet distillation from neural networks through interpretations. NeurIPS 2021: 20669-20682 - [i12]Wooseok Ha, Chandan Singh, François Lanusse, Eli Song, Song Dang, Kangmin He, Srigokul Upadhyayula, Bin Yu:
Adaptive wavelet distillation from neural networks through interpretations. CoRR abs/2107.09145 (2021) - [i11]Chandan Singh, Wooseok Ha, Bin Yu:
Interpreting and improving deep-learning models with reality checks. CoRR abs/2108.06847 (2021) - 2020
- [c15]Chandan Singh, Wooseok Ha, Bin Yu:
Interpreting and Improving Deep-Learning Models with Reality Checks. xxAI@ICML 2020: 229-254 - [c14]Laura Rieger, Chandan Singh, W. James Murdoch, Bin Yu:
Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge. ICML 2020: 8116-8126 - [i10]Chandan Singh, Wooseok Ha, François Lanusse, Vanessa Böhm, Jia Liu, Bin Yu:
Transformation Importance with Applications to Cosmology. CoRR abs/2003.01926 (2020) - [i9]Nick Altieri, Rebecca L. Barter, James Duncan, Raaz Dwivedi, Karl Kumbier, Xiao Li, Robert Netzorg, Briton Park, Chandan Singh, Yan Shuo Tan, Tiffany M. Tang, Yu Wang, Bin Yu:
Curating a COVID-19 data repository and forecasting county-level death counts in the United States. CoRR abs/2005.07882 (2020) - [i8]Raaz Dwivedi, Chandan Singh, Bin Yu, Martin J. Wainwright:
Revisiting complexity and the bias-variance tradeoff. CoRR abs/2006.10189 (2020) - 2019
- [c13]Chandan Singh, W. James Murdoch, Bin Yu:
Hierarchical interpretations for neural network predictions. ICLR (Poster) 2019 - [i7]W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu:
Interpretable machine learning: definitions, methods, and applications. CoRR abs/1901.04592 (2019) - [i6]Summer Devlin, Chandan Singh, W. James Murdoch, Bin Yu:
Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees. CoRR abs/1905.07631 (2019) - [i5]Laura Rieger, Chandan Singh, W. James Murdoch, Bin Yu:
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge. CoRR abs/1909.13584 (2019) - 2018
- [i4]Chandan Singh, W. James Murdoch, Bin Yu:
Hierarchical interpretations for neural network predictions. CoRR abs/1806.05337 (2018) - 2017
- [i2]Chandan Singh, Beilun Wang, Yanjun Qi:
A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs. CoRR abs/1709.04090 (2017)
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last updated on 2024-04-14 01:09 CEST by the dblp team
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