Papers by Brian Namee
A Rationale-Centric Framework for Human-in-the-loop Machine Learning (2022.acl-long)
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| Challenge: | Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-district tests, especially for few-shot learning scenarios. |
| Approach: | They propose a rational-centric framework with human-in-the-loop to exploit spurious associations and bias models towards generally applicable underlying distributions. |
| Outcome: | The proposed framework leads to significant prediction benefits on in-distribution and out-of-district tests, compared to state-of the-art benchmarks. |