24 papers
AI-Assisted Discovery of Convex Relaxations via Dual Agents
Sungyoon Kim, Mert Pilanci
Recent work shows that LLM agents can improve sharp-constant inequalities by searching for extremal constructions, which yield upper bounds. We address the complementary side: a lo…
Convex Optimization for Alignment and Preference Learning on a Single GPU
Miria Feng, Mert Pilanci
Fine-tuning large language models (LLMs) to align with human preferences has driven the success of systems such as Gemini and ChatGPT. However, approaches like Reinforcement Learni…
Convex Low-resource Accent-Robust Language Detection in Speech Recognition
Miria Feng, William Tan, Mert Pilanci
Globalization and multiculturalism continue to produce increasingly diverse speech varieties. Yet current spoken dialogue systems frequently fail on under-represented dialects and…
Principled Design of Diffusion-based Optimizers for Inverse Problems
Julio Oscanoa, Irmak Sivgin, Cagan Alkan +4
Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference times and cumbersome hyperparam…
Optimizer-Induced Mode Connectivity: From AdamW to Muon
Fangzhao Zhang, Sungyoon Kim, Erica Zhang +2
Mode connectivity has been widely studied, yet the role of the optimizer remains underexplored. We revisit it through optimizer-induced implicit regularization, asking how connecti…
Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration
Erica Zhang, Naomi Sagan, Danny Tse +3
Large language models (LLMs) encode rich semantic knowledge that can be useful for supervised learning, but their outputs are unreliable as statistical priors: they may be noisy, m…