Calvin McCarter

Machine Learning Researcher


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


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.


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.

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