James Zou, Yongchan Kwon, Weixin Liang, Kevin Jiang
We lifted 7 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| opendataval/opendataval | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy | Ran | opendataval/opendataval/opendataval/metrics.py code served (permissive licence) · get_code("09b21e96f9f1145c") |
| get_name | Ran | opendataval/opendataval/opendataval/util.py code served (permissive licence) · get_code("e4f775a9a6a730ec") |
| load_mediator_output | Ran | opendataval/opendataval/opendataval/util.py code served (permissive licence) · get_code("ac153ea032d1ea3a") |
| neg_l2 | Ran | opendataval/opendataval/opendataval/metrics.py code served (permissive licence) · get_code("0e01cada769eb741") |
| neg_mse | Ran | opendataval/opendataval/opendataval/metrics.py code served (permissive licence) · get_code("d496216f795f6458") |
| set_random_state | Ran | opendataval/opendataval/opendataval/util.py code served (permissive licence) · get_code("504db6072fc44357") |
| to_numpy | Not yet run | opendataval/opendataval/opendataval/model/api.py code served (permissive licence) · get_code("5266d8e9025c49d4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data valuation algorithms have been proposed to quantify data quality, however, there lacks a systemic and standardized benchmarking system for data valuation. In this paper, we introduce OpenDataVal, an easy-to-use and unified benchmark framework that empowers researchers and practitioners to apply and compare various data valuation algorithms. OpenDataVal provides an integrated environment that includes (i) a diverse collection of image, natural language, and tabular datasets, (ii) implementations of eleven different state-of-the-art data valuation algorithms, and (iii) a prediction model API that can import any models in scikit-learn. Furthermore, we propose four downstream machine learning tasks for evaluating the quality of data values. We perform benchmarking analysis using OpenDataVal, quantifying and comparing the efficacy of state-of-the-art data valuation approaches. We find that no single algorithm performs uniformly best across all tasks, and an appropriate algorithm should be employed for a user's downstream task. OpenDataVal is publicly available at https://opendataval.github.io with comprehensive documentation. Furthermore, we provide a leaderboard where researchers can evaluate the effectiveness of their own data valuation algorithms.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.10577")
get_code_for_paper("2306.10577")
have("2306.10577")
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