3 papers
cs.LG2026
Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity
Zifan Lyu, Chahine Nejma, Tobias Wegel +2
Large Language Models are typically benchmarked by evaluating every model on every test query. For practitioners seeking the best model to deploy, this is often wasteful: if a mode…
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
Learning Pareto manifolds in high dimensions: How can regularization help?
Tobias Wegel, Filip KovaÄeviÄ, Alexandru Å¢ifrea +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…