98 citations · 365 across the 61 of their papers we have counts for
16 papers · 1 filter
Locally Adaptive and Differentiable Regression
Mingxuan Han, Varun Shankar, Jeff M Phillips +1
Over-parameterized models like deep nets and random forests have become very popular in machine learning. However, the natural goals of continuity and differentiability, common in…
Linear Distance Metric Learning with Noisy Labels
Meysam Alishahi, Anna Little, Jeff M. Phillips
In linear distance metric learning, we are given data in one Euclidean metric space and the goal is to find an appropriate linear map to another Euclidean metric space which respec…
Batch Multi-Fidelity Active Learning with Budget Constraints
Shibo Li, Jeff M. Phillips, Xin Yu +2
Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for the…
Practical and Configurable Network Traffic Classification Using Probabilistic Machine Learning
Jiahui Chen, Joe Breen, Jeff M. Phillips +1
Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classifi…
A Deterministic Streaming Sketch for Ridge Regression
Benwei Shi, Jeff M. Phillips
We provide a deterministic space-efficient algorithm for estimating ridge regression. For data points with features and a large enough regularization parameter, we provide…
Constrained Non-Affine Alignment of Embeddings
Yuwei Wang, Yan Zheng, Yanqing Peng +7
Embeddings are one of the fundamental building blocks for data analysis tasks. Embeddings are already essential tools for large language models and image analysis, and their use is…