8 citations · 17 across the 18 of their papers we have counts for
19 papers
Uniform Sampling from High-dimensional Spectral Norm Balls
Michael R. Metel
Motivated by an application in machine learning optimization, this paper focuses on the challenges of sampling a matrix uniformly from the unit spectral norm ball. It is proven tha…
Thinking Long, but Short: Stable Sequential Test-Time Scaling for Large Reasoning Models
Michael R. Metel, Yufei Cui, Boxing Chen +1
Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed…
Modified K-means Algorithm with Local Optimality Guarantees
Mingyi Li, Michael R. Metel, Akiko Takeda
The K-means algorithm is one of the most widely studied clustering algorithms in machine learning. While extensive research has focused on its ability to achieve a globally optimal…
Batch-Max: Higher LLM Throughput using Larger Batch Sizes and KV Cache Compression
Michael R. Metel, Boxing Chen, Mehdi Rezagholizadeh
Several works have developed eviction policies to remove key-value (KV) pairs from the KV cache for more efficient inference. The focus has been on compressing the KV cache after t…
Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity
Michael R. Metel, Peng Lu, Boxing Chen +2
We present a simple on the fly method for faster inference of large language models. Unlike other (self-)speculative decoding techniques, our method does not require fine-tuning or…
Mathematical Challenges in Deep Learning
Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev +8
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challe…