Peer Group Metadata-Informed LSTM Ensembles for Insider Threat Detection. - Jason Matterer; Daniel LeJeune (FLAIRS, 2018)

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. - Amirali Aghazadeh; Ryan Spring; Daniel LeJeune; Gautam Dasarathy; Anshumali Shrivastava; Richard G. Baraniuk (ICML, 2018)

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. - Daniel LeJeune; Reinhard Heckel; Richard G. Baraniuk (AISTATS, 2019)

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. - Daniel LeJeune; Richard G. Baraniuk; Reinhard Heckel (CoRR, 2019)

Thresholding Graph Bandits with GrAPL. - Daniel LeJeune; Gautam Dasarathy; Richard G. Baraniuk (AISTATS, 2020)

The Implicit Regularization of Ordinary Least Squares Ensembles. - Daniel LeJeune; Hamid Javadi; Richard G. Baraniuk (AISTATS, 2020)

Minipatch Learning as Implicit Ridge-Like Regularization. - Tianyi Yao; Daniel LeJeune; Hamid Javadi; Richard G. Baraniuk; Genevera I. Allen (BigComp, 2021)

Wearing A Mask: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels. - Sina Alemohammad; Hossein Babaei; Randall Balestriero; Matt Y. Cheung; Ahmed Imtiaz Humayun; Daniel LeJeune; Naiming Liu; Lorenzo Luzi; Jasper Tan; Zichao Wang 001; Richard G. Baraniuk (ICASSP, 2021)

The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization. - Daniel LeJeune; Hamid Javadi; Richard G. Baraniuk (NeurIPS, 2021)

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

Extreme Compressed Sensing of Poisson Rates From Multiple Measurements. - Pavan K. Kota; Daniel LeJeune; Rebekah A. Drezek; Richard G. Baraniuk (IEEE Trans. Signal Process., 2022)

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

WIRE: Wavelet Implicit Neural Representations. - Vishwanath Saragadam; Daniel LeJeune; Jasper Tan; Guha Balakrishnan; Ashok Veeraraghavan; Richard G. Baraniuk (CVPR, 2023)

An Adaptive Tangent Feature Perspective of Neural Networks. - Daniel LeJeune; Sina Alemohammad (CPAL, 2024)

Titan: Bringing the Deep Image Prior to Implicit Representations. - Lorenzo Luzi; Daniel LeJeune; Ali Siahkoohi; Sina Alemohammad; Vishwanath Saragadam; Hossein Babaei; Naiming Liu; Zichao Wang 001; Richard G. Baraniuk (ICASSP, 2024)

Self-Consuming Generative Models Go MAD. - Sina Alemohammad; Josue Casco-Rodriguez; Lorenzo Luzi; Ahmed Imtiaz Humayun; Hossein Babaei; Daniel LeJeune; Ali Siahkoohi; Richard G. Baraniuk (ICLR, 2024)

Asymptotically Free Sketched Ridge Ensembles: Risks, Cross-Validation, and Tuning. - Pratik Patil; Daniel LeJeune (ICLR, 2024)

Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems. - Michal Derezinski; Daniel LeJeune; Deanna Needell; Elizaveta Rebrova (CoRR, 2024)

RandALO: Out-of-sample risk estimation in no time flat. - Parth Nobel; Daniel LeJeune; Emmanuel J. Candès (CoRR, 2024)

Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization. - Daniel LeJeune; Jiayu Liu; Reinhard Heckel (J. Mach. Learn. Res., 2024)

The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression. - Yehuda Dar; Daniel LeJeune; Richard G. Baraniuk (SIAM J. Math. Data Sci., 2024)

Asymptotics of the Sketched Pseudoinverse. - Daniel LeJeune; Pratik Patil; Hamid Javadi; Richard G. Baraniuk; Ryan J. Tibshirani (SIAM J. Math. Data Sci., 2024)