Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
Authors: Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu, Sashank J. Reddi, Gang Fu, Mohammadhossein Bateni, Sanjiv Kumar
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a suite of synthetic and real-world experiments to empirically validate our theoretical findings. |
| Researcher Affiliation | Industry | 1Google Research. Correspondence to: Michal Lukasik <EMAIL>, Lin Chen <EMAIL>. |
| Pseudocode | No | The paper describes methods and analyses them, but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code or a link to a code repository. |
| Open Datasets | Yes | We consider the UCI Banking dataset composed of information about Bank customers, advertising campaign details, and the success thereof (Moro et al., 2014). Help Steer (Wang et al., 2023b) consists of evaluations of LLM responses across 5 categories: MSLR. We next consider MSLR Web30k, a dataset of users query-document interactions (Qin & Liu, 2013). |
| Dataset Splits | Yes | We train a 3-layer MLP model with hidden dimension 256 and Re LU activation over 8K examples for 50 epochs. We evaluate on held out 2K examples. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'TensorFlow ranking library' but does not specify any software versions for libraries or programming languages. |
| Experiment Setup | Yes | We train a 3-layer MLP model with hidden dimension 256 and Re LU activation over 8K examples for 50 epochs. We train a linear model on numerical features using Adam for 100 epochs. |