14 citations · 22 across the 7 of their papers we have counts for
9 papers
Same Text, Different Prediction: Serving-Context Nondeterminism in Text Classifiers
Santhosh Kumar Kasa, Siva Rajesh Kasa, Sumit Negi
Deterministic inference is essential for reliable and trustworthy machine learning. Prior studies of text generation have shown that changing factors such as batch size, batch comp…
Traversing the Satisfaction-Diversity Frontier in Text-to-Image Diffusion
Kevin Zhai, Siva Rajesh Kasa, Soumya Roy +2
Text-to-image generation enables users to explore several images generated from the same prompt. For these generated images to be useful, each one must reflect the user's preferenc…
COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation
Yashal Shakti Kanungo, Gyanendra Das, Pooja A +1
Online ads are essential to all businesses and ad headlines are one of their core creative component. Existing methods can generate headlines automatically and also optimize their…
Ad Headline Generation using Self-Critical Masked Language Model
Yashal Shakti Kanungo, Sumit Negi, Aruna Rajan
For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially…
Fast-dLLM++: Fréchet Profile Decoding for Faster Diffusion LLM Inference
Siva Rajesh Kasa, Yasong Dai, Sumit Negi +1
Diffusion large language models promise parallel token generation, yet inference remains bottlenecked by deciding which masked tokens can be safely committed together. Fast-dLLM ad…
DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification
Ziyi Wang, Siva Rajesh Kasa, Ankith M S +6
Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel. In practice, its speedup is often bottleneck…
Rethinking Test Time Scaling for Flow-Matching Generative Models
Qingtao Yu, Changlin Song, Minghao Sun +6
The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While…
The Hidden Cost of Modeling P(X): Vulnerability to Membership Inference Attacks in Generative Text Classifiers
Owais Makroo, Siva Rajesh Kasa, Sumegh Roychowdhury +4
Membership Inference Attacks (MIAs) pose a critical privacy threat by enabling adversaries to determine whether a specific sample was included in a model's training dataset. Despit…
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning
Jiaru Zou, Soumya Roy, Vinay Kumar Verma +6
Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of…