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2020 – today
- 2024
[i21]Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly:
Which Pretrain Samples to Rehearse when Finetuning Pretrained Models? CoRR abs/2402.08096 (2024)- [i20]Peter Schafhalter, Shun Liao, Yanqi Zhou, Chih-Kuan Yeh, Arun Kandoor, James Laudon:
Scalable Multi-Domain Adaptation of Language Models using Modular Experts. CoRR abs/2410.10181 (2024) - 2023
- [j2]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Faith-Shap: The Faithful Shapley Interaction Index. J. Mach. Learn. Res. 24: 94:1-94:42 (2023) - [c19]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alex Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. ACL (Findings) 2023: 8003-8017 - [c18]Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. ICLR 2023 - [c17]Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani:
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS 2023 - [c16]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Sample based Explanations via Generalized Representers. NeurIPS 2023 - [i19]Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister:
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes. CoRR abs/2305.02301 (2023) - [i18]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Sample based Explanations via Generalized Representers. CoRR abs/2310.18526 (2023) - [i17]Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani:
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR abs/2312.06134 (2023) - 2022
- [c15]Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. AISTATS 2022: 1485-1502 - [c14]Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Language Data Influence. NeurIPS 2022 - [i16]Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar:
First is Better Than Last for Training Data Influence. CoRR abs/2202.11844 (2022) - [i15]Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, Pradeep Ravikumar:
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. CoRR abs/2202.11919 (2022) - [i14]Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar:
Human-Centered Concept Explanations for Neural Networks. CoRR abs/2202.12451 (2022) - [i13]Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar:
Faith-Shap: The Faithful Shapley Interaction Index. CoRR abs/2203.00870 (2022) - [i12]Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR abs/2208.14966 (2022) - 2021
- [c13]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [p1]Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar:
Human-Centered Concept Explanations for Neural Networks. Neuro-Symbolic Artificial Intelligence 2021: 337-352 - 2020
- [c12]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. ICLR 2020 - [c11]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar:
On Completeness-aware Concept-Based Explanations in Deep Neural Networks. NeurIPS 2020 - [i11]Biswajit Paria, Chih-Kuan Yeh, Ian En-Hsu Yen, Ning Xu, Pradeep Ravikumar, Barnabás Póczos:
Minimizing FLOPs to Learn Efficient Sparse Representations. CoRR abs/2004.05665 (2020) - [i10]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. CoRR abs/2006.00442 (2020)
2010 – 2019
- 2019
- [c10]Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu:
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching. ICLR (Poster) 2019 - [c9]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
On the (In)fidelity and Sensitivity of Explanations. NeurIPS 2019: 10965-10976 - [i9]Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, Pradeep Ravikumar:
How Sensitive are Sensitivity-Based Explanations? CoRR abs/1901.09392 (2019) - [i8]Chih-Kuan Yeh, Been Kim, Sercan Ömer Arik, Chun-Liang Li, Pradeep Ravikumar, Tomas Pfister:
On Concept-Based Explanations in Deep Neural Networks. CoRR abs/1910.07969 (2019) - 2018
- [j1]Chih-Kuan Yeh, Cheng-Yu Hsieh
, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. IEEE Trans. Games 10(4): 365-377 (2018) - [c8]Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang:
Order-Free RNN With Visual Attention for Multi-Label Classification. AAAI 2018: 6714-6721 - [c7]Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang:
Multi-Label Zero-Shot Learning With Structured Knowledge Graphs. CVPR 2018: 1576-1585 - [c6]Hong-Min Chu, Chih-Kuan Yeh, Yu-Chiang Frank Wang:
Deep Generative Models for Weakly-Supervised Multi-Label Classification. ECCV (2) 2018: 409-425 - [c5]Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. NeurIPS 2018: 9311-9321 - [i7]Chih-Kuan Yeh, Joon Sik Kim, Ian En-Hsu Yen, Pradeep Ravikumar:
Representer Point Selection for Explaining Deep Neural Networks. CoRR abs/1811.09720 (2018) - [i6]Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu:
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching. CoRR abs/1812.09323 (2018) - 2017
- [c4]Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank Wang:
Learning Deep Latent Space for Multi-Label Classification. AAAI 2017: 2838-2844 - [i5]Chih-Kuan Yeh, Yao-Hung Hubert Tsai, Yu-Chiang Frank Wang:
Generative-Discriminative Variational Model for Visual Recognition. CoRR abs/1706.02295 (2017) - [i4]Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, Yu-Chiang Frank Wang:
Learning Deep Latent Spaces for Multi-Label Classification. CoRR abs/1707.00418 (2017) - [i3]Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang:
Order-Free RNN with Visual Attention for Multi-Label Classification. CoRR abs/1707.05495 (2017) - [i2]Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh, Yu-Chiang Frank Wang:
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs. CoRR abs/1711.06526 (2017) - 2016
- [c3]Chih-Kuan Yeh, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. ECAI 2016: 1362-1369 - [c2]Te-Li Wang, Chih-Kuan Yeh, Ho-Lin Chen:
An Improved Tax Scheme for Selfish Routing. ISAAC 2016: 61:1-61:12 - [c1]Chih-Kuan Yeh, Wei-Chieh Wu, Yu-Chiang Frank Wang:
Learning graph fusion for query and database specific image retrieval. MMSP 2016: 1-5 - [i1]Chih-Kuan Yeh, Hsuan-Tien Lin:
Automatic Bridge Bidding Using Deep Reinforcement Learning. CoRR abs/1607.03290 (2016)
Coauthor Index
aka: Pradeep Kumar Ravikumar
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