4 accepted papers
Large Language Models are commonly benchmarked on a dataset by evaluating all relevant models on all queries in the test set. This can be wasteful for a practitioner who wants to find the best model to deploy—if a model clearly performs worse than others, there is no need to precisely estimate its p…
When a learner is faced with a new task, but is given very few samples, it must leverage any available side-information. In practice, this often comes in the form of benchmarks, where there is abundant data to evaluate model performance on related tasks. Though task relatedness is difficult to forma…
Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalizatio…
In multi-objective learning (MOL), several possibly competing prediction tasks must be solved jointly by a single model. Achieving good trade-offs may require a model class $\mathcal{G}$ with larger capacity than what is necessary for solving the individual tasks. This, in turn, increases the statis…