5 citations · 11 across the 11 of their papers we have counts for
14 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…
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…
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…
Generative or Discriminative? Revisiting Text Classification in the Era of Transformers
Siva Rajesh Kasa, Karan Gupta, Sumegh Roychowdhury +7
The comparison between discriminative and generative classifiers has intrigued researchers since Efron's seminal analysis of logistic regression versus discriminant analysis. While…
Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
Qizhang Feng, Siva Rajesh Kasa, Santhosh Kumar Kasa +3
Large Language Models (LLMs) have seen widespread adoption due to their remarkable natural language capabilities. However, when deploying them in real-world settings, it is importa…
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
Siva Rajesh Kasa, Aniket Goel, Karan Gupta +4
Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating predicti…
How Robust are LLMs to In-Context Majority Label Bias?
Karan Gupta, Sumegh Roychowdhury, Siva Rajesh Kasa +4
In the In-Context Learning (ICL) setup, various forms of label biases can manifest. One such manifestation is majority label bias, which arises when the distribution of labeled exa…
Tackling Concept Shift in Text Classification using Entailment-style Modeling
Sumegh Roychowdhury, Karan Gupta, Siva Rajesh Kasa +2
Pre-trained language models (PLMs) have seen tremendous success in text classification (TC) problems in the context of Natural Language Processing (NLP). In many real-world text cl…
Improved Inference of Gaussian Mixture Copula Model for Clustering and Reproducibility Analysis using Automatic Differentiation
Siva Rajesh Kasa, Vaibhav Rajan
Copulas provide a modular parameterization of multivariate distributions that decouples the modeling of marginals from the dependencies between them. Gaussian Mixture Copula Model…
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data
Siva Rajesh Kasa, Vaibhav Rajan
We study two practically important cases of model based clustering using Gaussian Mixture Models: (1) when there is misspecification and (2) on high dimensional data, in the light…
Automatic Differentiation in Mixture Models
Siva Rajesh Kasa, Vaibhav Rajan
In this article, we discuss two specific classes of models - Gaussian Mixture Copula models and Mixture of Factor Analyzers - and the advantages of doing inference with gradient de…
A Test for detecting Structural Breakdowns in Markets using Eigenvalue Decompositions
Malay Bhattacharyya, Siva Rajesh Kasa
Correlations among stock returns during volatile markets differ substantially compared to those from quieter markets. During times of financial crisis, it has been observed that tr…