171 citations · 237 across the 13 of their papers we have counts for
6 papers · 1 filter
Approximate Partition Selection for Big-Data Workloads using Summary Statistics
Kexin Rong, Yao Lu, Peter Bailis +2
Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling…
Leveraging Organizational Resources to Adapt Models to New Data Modalities
Sahaana Suri, Raghuveer Chanda, Neslihan Bulut +7
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e.g., a new v…
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
Daniel Kang, Ankit Mathur, Teja Veeramacheneni +2
While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researc…
Chromatic Learning for Sparse Datasets
Vladimir Feinberg, Peter Bailis
Learning over sparse, high-dimensional data frequently necessitates the use of specialized methods such as the hashing trick. In this work, we design a highly scalable alternative…
Model Assertions for Monitoring and Improving ML Models
Daniel Kang, Deepti Raghavan, Peter Bailis +1
ML models are increasingly deployed in settings with real world interactions such as vehicles, but unfortunately, these models can fail in systematic ways. To prevent errors, ML en…
Storyboard: Optimizing Precomputed Summaries for Aggregation
Edward Gan, Peter Bailis, Moses Charikar
An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggre…