How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations. - Martina A. Rau; Blake Mason; Robert D. Nowak (EDM, 2016)

Learning Low-Dimensional Metrics. - Blake Mason; Lalit Jain; Robert D. Nowak (NIPS, 2017)

For Teaching Perceptual Fluency, Machines Beat Human Experts. - Ayon Sen; Purav Patel; Martina A. Rau; Blake Mason; Robert Nowak 001; Timothy T. Rogers; Jerry Zhu (CogSci, 2018)

Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice. - Ayon Sen; Purav Patel; Martina A. Rau; Blake Mason; Robert Nowak 001; Timothy T. Rogers; Xiaojin Zhu 001 (EDM, 2018)

Learning Nearest Neighbor Graphs from Noisy Distance Samples. - Blake Mason; Ardhendu Tripathy; Robert D. Nowak (NeurIPS, 2019)

Cognitive Task Analysis for Implicit Knowledge About Visual Representations With Similarity Learning Methods. - Blake Mason; Martina A. Rau; Robert D. Nowak (Cogn. Sci., 2019)

Finding All $ epsilon$-Good Arms in Stochastic Bandits. - Blake Mason; Lalit Jain; Ardhendu Tripathy; Robert Nowak 001 (NeurIPS, 2020)

Finding All ε-Good Arms in Stochastic Bandits. - Blake Mason; Lalit Jain; Ardhendu Tripathy; Robert D. Nowak (CoRR, 2020)

Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits. - Kwang-Sung Jun; Lalit Jain; Houssam Nassif; Blake Mason (ICML, 2021)

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. - Julian Katz-Samuels; Blake Mason; Kevin G. Jamieson; Robert Nowak 001 (NeurIPS, 2021)

Nearest neighbor search under uncertainty. - Blake Mason; Ardhendu Tripathy; Robert Nowak 001 (UAI, 2021)

An Experimental Design Approach for Regret Minimization in Logistic Bandits. - Blake Mason; Kwang-Sung Jun; Lalit Jain (AAAI, 2022)

Nearly Optimal Algorithms for Level Set Estimation. - Blake Mason; Lalit Jain; Subhojyoti Mukherjee; Romain Camilleri; Kevin G. Jamieson; Robert D. Nowak (AISTATS, 2022)

NFT-K: Non-Fungible Tangent Kernels. - Sina Alemohammad; Hossein Babaei; C. J. Barberan; Naiming Liu; Lorenzo Luzi; Blake Mason; Richard G. Baraniuk (ICASSP, 2022)

One for All: Simultaneous Metric and Preference Learning over Multiple Users. - Gregory Canal; Blake Mason; Ramya Korlakai Vinayak; Robert Nowak 001 (NeurIPS, 2022)

Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. - Jasper Tan; Blake Mason; Hamid Javadi; Richard G. Baraniuk (NeurIPS, 2022)

Benign Overparameterization in Membership Inference with Early Stopping. - Jasper Tan; Daniel LeJeune; Blake Mason; Hamid Javadi; Richard G. Baraniuk (CoRR, 2022)

Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering. - Tavish McDonald; Brian Tsan; Amar Saini; Juanita Ordonez; Luis Gutierrez; Phan Nguyen; Blake Mason; Brenda Ng (SDU@AAAI, 2023)

A Blessing of Dimensionality in Membership Inference through Regularization. - Jasper Tan; Daniel LeJeune; Blake Mason; Hamid Javadi; Richard G. Baraniuk (AISTATS, 2023)

Experimental Designs for Heteroskedastic Variance. - Justin Weltz; Tanner Fiez; Alexander Volfovsky; Eric Laber; Blake Mason; Houssam Nassif; Lalit Jain (NeurIPS, 2023)

Effective Adaptive Exploration of Prices and Promotions in Choice-Based Demand Models. - Lalit Jain; Zhaoqi Li; Erfan Loghmani; Blake Mason; Hema Yoganarasimhan (Mark. Sci., 2024)

Metric Learning in an RKHS. - Gokcan Tatli; Yi Chen; Blake Mason; Robert D. Nowak; Ramya Korlakai Vinayak (UAI, 2025)