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stat.ML2026
Hedging on the Frontier: Learning New Tasks with Few Samples
Tobias Wegel, Federico Di Gennaro, Geelon So +1
When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks i…
stat.ML2025
On the sample complexity of semi-supervised multi-objective learning
Tobias Wegel, Geelon So, Junhyung Park +1
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 $\mathc…
stat.ML2025
Learning Pareto manifolds in high dimensions: How can regularization help?
Tobias Wegel, Filip Kovačević, Alexandru Ţifrea +1
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. F…