18 citations · 27 across the 15 of their papers we have counts for
9 papers · 1 filter
Uncovering the Limits of Proof Sharing for Neural Networks
Kanak Das, Shubham Ugare, Bor-Yuh Evan Chang +3
Robustness verification of neural networks is increasingly important, due to their use in many critical domains. In certain scenarios, proof sharing has been shown to accelerate in…
PagedWeight: Efficient MoE LLM Serving with Dynamic Quality-Aware Weight Quantization
Yuchen Yang, Yifan Zhao, Anisha Dasgupta +1
Mixture-of-Experts (MoE) is a popular class of large language models (LLMs), offering high efficiency and accuracy. However, in KV-cache-intensive serving scenarios, MoEs often exh…
RefineStat: Efficient Exploration for Probabilistic Program Synthesis
Madhav Kanda, Shubham Ugare, Sasa Misailovic
Probabilistic programming offers a powerful framework for modeling uncertainty, yet statistical model discovery in this domain entails navigating an immense search space under stri…
DINGO: Constrained Inference for Diffusion LLMs
Tarun Suresh, Debangshu Banerjee, Shubham Ugare +2
Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffus…
ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
Yuchen Yang, Yifan Zhao, Shubham Ugare +2
Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustness, which provides provable guarantees…
SynCode: LLM Generation with Grammar Augmentation
Shubham Ugare, Tarun Suresh, Hangoo Kang +2
LLMs are widely used in complex AI applications. These applications underscore the need for LLM outputs to adhere to a specific format, for their integration with other components…