1 citations · 1 across the 1 of their papers we have counts for
8 papers
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…
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…
Enforcing Temporal Constraints for LLM Agents
Adharsh Kamath, Sishen Zhang, Calvin Xu +3
LLM-based agents are deployed in safety-critical applications, yet current guardrail systems fail to prevent violations of temporal safety policies, requirements that govern the or…
UTF-8 Plumbing: Byte-level Tokenizers Unavoidably Enable LLMs to Generate Ill-formed UTF-8
Preston Firestone, Shubham Ugare, Gagandeep Singh +1
Subword tokenization segments input text according to a pre-defined vocabulary to feed it into a language model; the language model, in turn, generates a sequence made from this sa…
Is The Watermarking Of LLM-Generated Code Robust?
Tarun Suresh, Shubham Ugare, Gagandeep Singh +1
We present the first in depth study on the robustness of existing watermarking techniques applied to code generated by large language models (LLMs). As LLMs increasingly contribute…
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…