A Complete Bibliography of the ACM/IMS Journal of Data Science

Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA

Tel: +1 801 581 5254 FAX: +1 801 581 4148

       E-mail: beebe@math.utah.edu, beebe@acm.org,
                  beebe@computer.org (Internet)
     WWW URL: https://www.math.utah.edu/~beebe/

20 August 2024 Version 1.02

Title word cross-reference

ACM [1]. ACM/ [1]. Activation [9]. AI [12]. Analytics [5]. Anytime [11]. Anytime-valid [11]. Augmentation [3].

Bandits [2, 11]. Based [5]. Batched [2]. Belief [4]. Beyond [5].

Component [9]. Contextual [11].

Data [1, 3, 5, 6]. Differentiable [4]. Differential [10]. DNBP [4].

Early [9]. Editorial [1]. Efficiency [6]. Equations [10].

Fusion [3].

Identification [6]. IMS [1]. Inaugural [1]. Incorporating [8]. Inference [3, 11]. Informed [10]. Interpolation [7]. Issue [1].

REFERENCES 2

Journal [1]. Language [8]. Learning [12, 10, 7]. Linear [7]. Loop [8]. Low [9]. Low-Rank [9]. Machine [12]. Management [5]. Missing [6]. Mitigating [12]. ML [5]. ML-Based [5]. Models [8]. Necessity [12]. Networks [9]. Neural [2, 10]. Nonignorable [6]. Nonparametric [4]. off [11]. off-policy [11]. Operator [10]. Optimistic [7]. Parameters [9]. Partial [10]. Physics [10]. Physics-Informed [10]. policy [11]. Principal [9]. Prompting [8]. Propagation [4]. Rank [9]. Rates [7]. Record [3]. Reduce [9]. Regression [7]. Regularization [7]. Risk [12]. Science [1]. Semiparametric [6]. Shadow [6]. Structure [9]. Supervision [8]. Theory [12, 6, 7]. Training [9]. Unifying [7]. Utilizing [9]. valid [11]. Variable [6]. via [3]. Weak [8].

References

Bradic:2024:AIJ

[1] Jelena Bradic, Stratos Idreos, and John Lafferty. ACM/ IMS journal of data science: Inaugural issue editorial. ACM/IMS Journal of Data Science, 1(1):1:1–1:??, March 2024.

Gu:2024:BNB

[2] Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab Mirrokni, and Dongruo Zhou. Batched neural bandits. ACM/IMS Journal of Data Science, 1(1):1:1–1:??, March 2024.

Heidari:2024:RFI

[3] Alireza Heidari, George Michalopoulos, Ihab F. Ilyas, and Theodoros Rekatsinas. Record fusion via inference and data augmentation. ACM/ IMS Journal of Data Science, 1(1):2:1–2:??, March 2024.

Opipari:2024:DDN

[4] Anthony Opipari, Jana Pavlasek, Chao Chen, Shoutian Wang, Karthik Desingh, and Odest Chadwicke Jenkins. DNBP: Differentiable nonparametric belief propagation. ACM/IMS Journal of Data Science, 1(1):3:1– 3:??, March 2024.

Kang:2024:DMM

[5] Daniel Kang, John Guibas, Peter Bailis, Tatsunori Hashimoto, Yi Sun, and Matei Zaharia. Data management for ML-based analytics and beyond. ACM/IMS Journal of Data Science, 1(1):4:1–4:??, March 2024.

Miao:2024:ISE

[6] Wang Miao, Lan Liu, Yilin Li, Eric J. Tchetgen Tchetgen, and Zhi Geng. Identification and semiparametric efficiency theory of nonignorable missing data with a shadow variable. ACM/IMS Journal of Data Science, 1(2):5:1– 5:??, June 2024.

Zhou:2024:ORU

[7] Lijia Zhou, Frederic Koehler, Danica J. Sutherland, and Nathan Srebro. Optimistic rates: a unifying theory for interpolation learning and regularization in linear regression. ACM/IMS Journal of Data Science, 1(2):6:1– 6:??, June 2024.

Smith:2024:LML

[8] Ryan Smith, Jason A. Fries, Braden Hancock, and Stephen H. Bach. Language models in the loop: Incorporating prompting into weak supervision. ACM/IMS Journal of Data Science, 1(2):7:1–7:??, June 2024.

Waleffe:2024:PCN

[9] Roger Waleffe and Theodoros Rekatsinas. Principal component networks: Utilizing low-rank activation structure to reduce parameters early in training. ACM/IMS Journal of Data Science, 1(2):8:1–8:??, June 2024.

Li:2024:PIN

[10] Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, and Anima Anandkumar. Physicsinformed neural operator for learning partial differential equations. ACM/ IMS Journal of Data Science, 1(3):9:1–9:??, September 2024.

Waudby-Smith:2024:AVP

[11] Ian Waudby-Smith, Lili Wu, Aaditya Ramdas, Nikos Karampatziakis, and Paul Mineiro. Anytime-valid off-policy inference for contextual bandits. ACM/IMS Journal of Data Science, 1(3):10:1–10:??, September 2024.

Belkin:2024:NML

[12] Mikhail Belkin. The necessity of machine learning theory in mitigating AI risk. ACM/IMS Journal of Data Science, 1(3):11:1–11:??, September 2024.