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ORCIDRico Angell
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2020 – today
- 2024
[c10]Rico Angell, Andrew McCallum:
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. ICML 2024- [i10]Rico Angell:
Polynomial Precision Dependence Solutions to Alignment Research Center Matrix Completion Problems. CoRR abs/2401.03999 (2024) - [i9]Adam Karvonen, Benjamin Wright, Can Rager, Rico Angell, Jannik Brinkmann, Logan Smith, Claudio Mayrink Verdun, David Bau, Samuel Marks:
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models. CoRR abs/2408.00113 (2024) - 2023
- [i8]Rico Angell, Andrew McCallum:
Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching. CoRR abs/2312.11801 (2023) - 2022
- [c9]Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum:
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization. EMNLP 2022: 2171-2194 - [c8]Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum:
Interactive Correlation Clustering with Existential Cluster Constraints. ICML 2022: 703-716 - [c7]Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum:
Entity Linking via Explicit Mention-Mention Coreference Modeling. NAACL-HLT 2022: 4644-4658 - [i7]Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum:
Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization. CoRR abs/2210.12579 (2022) - 2021
- [c6]Sunil Mohan, Rico Angell, Nicholas Monath, Andrew McCallum:
Low resource recognition and linking of biomedical concepts from a large ontology. BCB 2021: 54:1-54:10 - [c5]Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum:
Clustering-based Inference for Biomedical Entity Linking. NAACL-HLT 2021: 2598-2608 - [i6]Sunil Mohan, Rico Angell, Nicholas Monath, Andrew McCallum:
Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology. CoRR abs/2101.10587 (2021) - [i5]Arthur Feeney, Rishabh Gupta, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari, Tengfei Ma:
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction. CoRR abs/2105.13975 (2021) - [i4]Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum:
Entity Linking and Discovery via Arborescence-based Supervised Clustering. CoRR abs/2109.01242 (2021) - 2020
- [i3]Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum:
Clustering-based Inference for Zero-Shot Biomedical Entity Linking. CoRR abs/2010.11253 (2020) - [i2]Brittany Johnson, Jesse Bartola, Rico Angell, Katherine A. Keith, Sam Witty, Stephen J. Giguere, Yuriy Brun:
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models. CoRR abs/2012.09951 (2020)
2010 – 2019
- 2018
- [c4]Rico Angell, Daniel Sheldon:
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes. NeurIPS 2018: 8998-9007 - [c3]Rico Angell, Brittany Johnson, Yuriy Brun, Alexandra Meliou
:
Themis: automatically testing software for discrimination. ESEC/SIGSOFT FSE 2018: 871-875 - 2017
- [c2]Rico Angell, Grant Schoenebeck:
Don't Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization. WINE 2017: 16-29 - 2016
- [i1]Rico Angell, Grant Schoenebeck:
Don't Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization. CoRR abs/1609.06520 (2016) - 2015
- [c1]Rico Angell, Ben Oztalay, Andrew DeOrio:
A Topological Approach to Hardware Bug Triage. MTV 2015: 20-25
Coauthor Index
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