354 citations · 369 across the 6 of their papers we have counts for
6 papers
Happiness Maximizing Sets under Group Fairness Constraints (Technical Report)
Jiping Zheng, Yuan Ma, Wei Ma +2
Finding a happiness maximizing set (HMS) from a database, i.e., selecting a small subset of tuples that preserves the best score with respect to any nonnegative linear utility func…
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning
Xiaoyang Wang, Bo Li, Yibo Zhang +2
Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these Au…
Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
Matthew T. Dearing, Xiaoyan Wang
Many interesting datasets ubiquitous in machine learning and deep learning can be described via graphs. As the scale and complexity of graph-structured datasets increase, such as i…
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
Sourabh Kulkarni, Kinjal Divesh Shah, Nimar Arora +9
We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and eva…
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsist…
Lipschitz Learning for Signal Recovery
Hong Jiang, Jong-Hoon Ahn, Xiaoyang Wang
We consider the recovery of signals from their observations, which are samples of a transform of the signals rather than the signals themselves, by using machine learning (ML). We…