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The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making.
Big data: The management revolution
Andrew McAfee and Erik Brynjolfsson · 2012
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Analyza: Exploring data with conversation
Kedar Dhamdhere, Kevin McCurley, Mukund Sundararajan, Qiqi Yan, and Ralfi Nahmias · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Data2vis: Automatic generation of data visualizations using sequence-to-sequence recurrent neural networks
Victor C. Dibia and Çagatay Demiralp · 2018
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Elements of an automatic data scientist
Luc De Raedt, Hendrik Blockeel, Samuel Kolb, Stefano Teso, and Gust Verbruggen · 2018
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Juice: A large scale distantly supervised dataset for open domain context-based code generation
Rajas Agashe, Srini Iyer, and Luke Zettlemoyer · 2019
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How can AI automate end-to-end data science?
Charu C. Aggarwal, Djallel Bouneffouf, Horst Samulowitz, Beat Buesser, Thanh Hoang, Udayan Khurana, Sijia Liu, Tejaswini Pedapati, Parikshit Ram, Ambrish Rawat, Martin Wistuba, and Alexander G. Gray · 2019
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What ai-driven decision making looks like
Eric Colson · 2019
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Transfer anomaly detection by inferring latent domain representations
Atsutoshi Kumagai, Tomoharu Iwata, and Yasuhiro Fujiwara · 2019
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The Automatic Statistician , page 161–173
Christian Steinruecken, Emma Smith, David Janz, James Lloyd, and Zoubin Ghahramani · 2019
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On the nature and types of anomalies: a review of deviations in data
Ralph Foorthuis · 2020
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Timeseries anomaly detection using temporal hierarchical one-class network
Lifeng Shen, Zhuocong Li, and James Kwok · 2020
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Neurosymbolic programming
Swarat Chaudhuri, Kevin Ellis, Oleksandr Polozov, Rishabh Singh, Armando Solar-Lezama, and Yisong Yue · 2021
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BI-REC: Guided data analysis for conversational business intelligence
Venkata Vamsikrishna Meduri, Abdul Quamar, Chuan Lei, Vasilis Efthymiou, and Fatma Ozcan · 2021
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A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert Müller · 2021
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Interpretable program synthesis
Tianyi Zhang, Zhiyang Chen, Yuanli Zhu, Priyan Vaithilingam, Xinyu Wang, and Elena L. Glassman · 2021
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Why becoming a data-driven organization is so hard
Randy Bean · 2022
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Training and evaluating a Jupyter notebook data science assistant
Shubham Chandel, Colin B. Clement, Guillermo Serrato, and Neel Sundaresan · 2022
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Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida I. Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen tau Yih, Luke Zettlemoyer, and Mike Lewis · 2022
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Beyond generating code: Evaluating GPT on a data visualization course, 2023
Zhutian Chen, Chenyang Zhang, Qianwen Wang, Jakob Troidl, Simon Warchol, Johanna Beyer, Nils Gehlenborg, and Hanspeter Pfister · 2023
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Liying Cheng, Xingxuan Li, and Lidong Bing · 2023
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LIDA: A tool for automatic generation of grammar-agnostic visualizations and infographics using large language models
Victor Dibia · 2023
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AutoGPT, 2023
Significant Gravitas · 2023
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How do analysts understand and verify ai-assisted data analyses?
Ken Gu, Ruoxi Shang, Tim Althoff, Chenglong Wang, and Steven M. Drucker · 2023
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Execution-based evaluation for data science code generation models
Junjie Huang, Chenglong Wang, Jipeng Zhang, Cong Yan, Haotian Cui, Jeevana Priya Inala, Colin Clement, and Nan Duan · 2022
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DS-1000: A natural and reliable benchmark for data science code generation
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen tau Yih, Daniel Fried, Sida Wang, and Tao Yu · 2022
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Competition-level code generation with AlphaCode
Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom, Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de, Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey, Cherepanov, James Molloy, Daniel Jaymin Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de, Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
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A unified survey on anomaly, novelty, open-set, and out of-distribution detection: Solutions and future challenges
Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2022
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Execution-based evaluation for open-domain code generation
Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou · 2022
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OpenOOD: Benchmarking generalized out-of-distribution detection
Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, WENXUAN PENG, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, and Ziwei Liu · 2022
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Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, Weishi Wang, Md. Rizwan Parvez, and Shafiq R. Joty · 2023
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Demonstration of InsightPilot: An LLM-empowered automated data exploration system
Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, and Dongmei Zhang · 2023
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Paula Maddigan and Teo Susnjak · 2023
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Demystifying GPT self-repair for code generation, 2023
Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama · 2023
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HuggingGPT: Solving AI tasks with ChatGPT and its friends in Hugging Face
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang · 2023
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Reflexion: Language agents with verbal reinforcement learning, 2023
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
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Llama 2: Open foundation and fine-tuned chat models
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Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim · 2023
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Natural language to code generation in interactive data science notebooks
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Oleksandr Polozov, and Charles Sutton · 2023
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