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Jennifer Sun

2 accepted papers

2026

Evaluating Agentic Optimization on Large Codebases

ICML 2026poster

Large language model (LLM) coding agents increasingly operate at the repository level, motivating benchmarks that evaluate their ability to optimize entire codebases under realistic constraints. Existing code benchmarks largely rely on synthetic tasks, binary correctness signals, or single-objective…

Cited by 0SourceScholar
2020

Learning Differentiable Programs with Admissible Neural Heuristics

NeurIPS 2020poster

We study the problem of learning differentiable functions expressed as programs in a domain-specific language. Such programmatic models can offer benefits such as composability and interpretability; however, learning them requires optimizing over a combinatorial space of program "architectures". We…

Cited by 58SourcePDFScholar