TAROT: Targeted Data Selection via Optimal Transport
Authors: Lan Feng, Fan Nie, Yuejiang Liu, Alexandre Alahi
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate TAROT across multiple tasks, including semantic segmentation, motion prediction, and instruction tuning. Results consistently show that TAROT outperforms state-of-the-art methods, highlighting its versatility across various deep learning tasks. |
| Researcher Affiliation | Academia | 1EPFL, Switzerlanzd 2Stanford, USA. Correspondence to: Yuejiang Liu <EMAIL>, Alexandre Alahi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Fixed-Size Selection Algorithm 2 OT-Distance Minimization Selection (OTM) |
| Open Source Code | Yes | Code is available at: https: //github.com/vita-epfl/TAROT. |
| Open Datasets | Yes | Following Park et al. (2023), we evaluate image classification using Res Net-9 classifiers trained on the CIFAR-10 dataset. For motion prediction, we adopt Auto Bots (Girgis et al., 2021), training on the nu Scenes (Caesar et al., 2020) dataset (32k samples) and validating on 9k target samples. The GTA5 dataset (Richter et al., 2016) serves as the candidate dataset, while the Cityscapes (Cordts et al., 2016) training split (2975 samples) is used as the target dataset, with its validation split for evaluation. We use the Uni Traj framework (Feng et al., 2024) for unified training and evaluation across multiple datasets, including Waymo Open Motion (WOMD) (Ettinger et al., 2021), Argoverse 2 (Wilson et al., 2021), nu Scenes (Caesar et al., 2020), and nu Plan (H. Caesar, 2021). We utilize the same candidate dataset, comprising FLAN V2 (Longpre et al., 2023), COT (Wei et al., 2022), DOLLY (Conover et al., 2023) and OPEN ASSISTANT 1 (K opf et al., 2024), with MMLU (Hendrycks et al., 2021b;a) and BBH (Suzgun ets al., 2023) serving as the target tasks for evaluation. |
| Dataset Splits | Yes | The Cityscapes (Cordts et al., 2016) training split (2975 samples) is used as the target dataset, with its validation split for evaluation. The nu Scenes training set (32k samples) serves as the target dataset, while the candidate pool comprises WOMD, Argoverse 2, and nu Plan. From nu Plan, we filter trajectories with a moving distance over 2 meters, yielding 1000k samples. We use the official training splits of WOMD and Argoverse 2, including 2000k samples. The evaluation is conducted on the nu Scenes validation set. We evaluate selection ratios of 5%, 20%, 50% and OTM (Section 3.3). OTM selects approximately 24% of the data. OT-Distance Minimization Selection (OTM): The target dataset Dt is randomly split into k equal subsets. In each fold, 1/k of Dt is used for selection, while the OT distance is evaluated against the remaining (k 1)/k data. ... In our experiments, k = 10 ensures a good match with the target distribution while avoiding overfitting. |
| Hardware Specification | Yes | Table 8: The wall clock runtime (measured as single H100 GPU hours) of TAROT compared with LESS and TSDS on instruction tuning task. |
| Software Dependencies | No | The paper mentions specific models and frameworks used (Deep Lab V3, Res Net50, Auto Bots, Wayformer, LLAMA-3.1-8B, QWEN-2.5-7B) but does not provide specific version numbers for underlying software dependencies like programming languages (e.g., Python), libraries (e.g., PyTorch, TensorFlow), or CUDA versions. |
| Experiment Setup | Yes | Table 3: Training Hyperparameters for Semantic Segmentation Table 4: Experiment Settings for Motion Prediction Table 5: Training Hyperparameters for Instruction Tuning |