38 citations · 247 across the 108 of their papers we have counts for
11 papers · 1 filter
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…
Unveiling Hidden Convexity in Deep Learning: a Sparse Signal Processing Perspective
Emi Zeger, Mert Pilanci
Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine learning tasks, inc…
Optimal Scalar Quantization for Matrix Multiplication: Closed-Form Density and Phase Transition
Calvin Ang, Sungyoon Kim, Mert Pilanci
We study entrywise scalar quantization of two matrices prior to multiplication. Given and , we quantize entries of and independentl…
ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imaging
Linfeng Ye, Shayan Mohajer Hamidi, Zhixiang Chi +5
Attention-based multiple instance learning (MIL) has emerged as a powerful framework for whole slide image (WSI) diagnosis, leveraging attention to aggregate instance-level feature…
FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
Rajat Vadiraj Dwaraknath, Sungyoon Kim, Mert Pilanci
Sparse sketches such as the sparse Johnson-Lindenstrauss transform are a core primitive in randomized numerical linear algebra because they leverage random sparsity to reduce the a…
An Information-Theoretic Perspective on LLM Tokenizers
Mete Erdogan, Abhiram Gorle, Shubham Chandak +2
Large language model (LLM) tokenizers act as structured compressors: by mapping text to discrete token sequences, they determine token count (and thus compute and context usage) an…
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…