Calvin McCarter

Machine Learning Researcher

Publications

You can visit my Google Scholar page for my full list of publications.



preprints

Towards Backwards-Compatible Data with Confounded Domain Adaptation
C McCarter.
[arXiv preprint]

Adaptive Block Floating-Point for Analog Deep Learning Hardware
A Basumallik, D Bunandar, N Dronen, N Harris, L Levkova, C McCarter, L Nair, D Walter, D Widemann.
[arXiv preprint]

Validation of a transcriptome-based assay for classifying cancers of unknown primary origin
J Michuda, A Breschi, J Kapilivsky, K Manghnani, C McCarter, AJ Hockenberry, B Mineo, C Igartua, JT Dudley, MC Stumpe, N Beaubier, M Shirazi, R Jones, E Morency, K Blackwell, J Guinney, KA Beauchamp, T Taxter.
[medrXiv preprint]

peer-reviewed papers

If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare.
R Hanson, D Martin, C McCarter, J Paulson.
The Astrophysical Journal (APJ), 2021.
[paper] [arXiv preprint] [Grabby Aliens website]

Real-world Evidence of Diagnostic Testing and Treatment Patterns in US Breast Cancer Patients with Implications for Treatment Biomarkers from RNA-sequencing Data.
LE Fernandes, CG Epstein, AM Bobe, JSK Bell, MC Stumpe, ME Salazar, AA Salahudeen, A Ruth, C McCarter, BD Leibowitz, M Kase, C Igartua, R Huether, A Hafez, N Beaubier, MD Axelson, MD Pegram, SL Sammons, JA OShaughnessy, GA Palmer.
Clinical Breast Cancer, 2020.
[medRxiv preprint] [paper]

Learning Gene Networks Underlying Clinical Phenotypes Using SNP Perturbation.
C McCarter, J Howrylak, S Kim.
PLOS Computational Biology, 2020.
[paper] [bioRxiv preprint]

Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models.
C McCarter, S Kim.
International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
[pdf] [supplement] [poster]

On Sparse Gaussian Chain Graph Models.
C McCarter, S Kim.
Advances in Neural Information Processing Systems (NeurIPS), 2014.
[pdf] [poster]

Active Learning with Partially Featured Data.
S Moon, C McCarter, YH Kuo.
Proceedings of the 23rd International Conference on World Wide Web (WWW), 2014.

Prediction of Glycan Motifs Using Quantitative Analysis of Multi-lectin Binding.
C McCarter, D Kletter, H Tang, K Partyka, Y Ma, S Singh, J Yadav, M Bern, BB Haab.
Proteomics Clinical Applications, 2013.

Simulation-based Signal Selection for State Restoration in Silicon Debug.
D Chatterjee, C McCarter, V Bertacco.
Proceedings of the International Conference on Computer-Aided Design (ICCAD), 2011.

patents

Systems and methods for multi-label cancer classification.
J Michuda, KA Beauchamp, J Kapilivsky, C McCarter, N Beaubier, MC Stumpe, C Igartua, JSK Bell, T Taxter, R Pelossof
US Patent App. 17/150,992. Publication date: 2021/5/13.

selected workshop papers

Lookups are not (yet) all you need for deep learning inference
C McCarter, N Dronen.
Sparsity in Neural Networks Workshop 2022.
[arXiv preprint] [paper] [poster]

Transcriptome background tissue correction in metastatic cancers using a correlated composition admixture model. C McCarter, B Leibowitz, J Michuda, JSK Bell, C Igartua, KP White.
Proceedings of the Annual Meeting of the American Association for Cancer Research 2020.
[abstract]

Penalised additive least squares models for high dimensional nonparametric regression and function selection.
K. Kandasamy, C. McCarter. Large-Scale Kernel Learning Workshop @ ICML, 2015.
[pdf]