Tilted Empirical Risk Minimization
Authors: Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we showcase the flexibility, wide applicability, and competitive performance of the TERM framework through empirical results on a variety of real-world problems such as handling outliers (Section 5.1), ensuring fairness and improving generalization (Section 5.2), and addressing compound issues (Section 5.3). |
| Researcher Affiliation | Collaboration | Tian Li CMU tianli@cmu.edu Ahmad Beirami Facebook AI beirami@fb.com Maziar Sanjabi Facebook AI maziars@fb.com Virginia Smith CMU smithv@cmu.edu |
| Pseudocode | Yes | Algorithm 1: Batch TERM", "Algorithm 2: Stochastic TERM", "Algorithm 3: Batch Non-Hierarchical TERM", "Algorithm 4: Stochastic Non-Hierarchical TERM |
| Open Source Code | Yes | We provide implementation details in Appendix J. All code, datasets, and experiments are publicly available at github.com/litian96/TERM. |
| Open Datasets | Yes | For regression tasks, we use the drug discovery data extracted from (Diakonikolas et al., 2019) which is originally curated from (Olier et al., 2018) and train linear regression models with different losses... For mitigating noise on classification tasks, we use the standard CIFAR-10 data and their standard train/val/test partitions along with a standard inception network (Szegedy et al., 2016)... We use the HIV-1 dataset (Rögnvaldsson, 2013) as in (Namkoong & Duchi, 2017) |
| Dataset Splits | Yes | We randomly split the dataset into 80% training set, 10% validation set, and 10% testing set. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For experiments with positive t (Section 5.2), we tune t P t0.1, 0.5, 1, 5, 10, 50, 100, 200u on the validation set... For experiments regarding focal loss (Lin et al., 2017), we select the class balancing parameter (α in the original focal loss paper) from rangep0.05, 0.95, 0.05q and select the main parameter γ from t0.5, 1, 2, 3, 4, 5u... The initial step-size is set to 0.1 and decayed to 0.01 at epoch 50. The batch size is 100. |