Zobrazeno 1 - 10
of 374
pro vyhledávání: '"P. Shiva Prasad"'
Autor:
Manggala, Putra, Mastakouri, Atalanti, Kirschbaum, Elke, Kasiviswanathan, Shiva Prasad, Ramdas, Aaditya
To use generative question-and-answering (QA) systems for decision-making and in any critical application, these systems need to provide well-calibrated confidence scores that reflect the correctness of their answers. Existing calibration methods aim
Externí odkaz:
http://arxiv.org/abs/2410.06615
We study the least-square regression problem with a two-layer fully-connected neural network, with ReLU activation function, trained by gradient flow. Our first result is a generalization result, that requires no assumptions on the underlying regress
Externí odkaz:
http://arxiv.org/abs/2410.06191
Autor:
Kadel, Rajan, Mishra, Bhupesh Kumar, Shailendra, Samar, Abid, Samia, Rani, Maneeha, Mahato, Shiva Prasad
GenAI has gained the attention of a myriad of users in almost every profession. Its advancement has had an intense impact on education, significantly disrupting the assessment design and evaluation methodologies. Despite the potential benefits and po
Externí odkaz:
http://arxiv.org/abs/2405.01805
Juvenile Idiopathic Arthritis (JIA) is a widespread and chronic condition that affects children and adolescents worldwide. The person suffering from JIA is characterized by chronic joint inflammation leading to pain, swelling, stiffness, and limited
Externí odkaz:
http://arxiv.org/abs/2401.04117
Conditional independence (CI) tests are widely used in statistical data analysis, e.g., they are the building block of many algorithms for causal graph discovery. The goal of a CI test is to accept or reject the null hypothesis that $X \perp \!\!\! \
Externí odkaz:
http://arxiv.org/abs/2306.06721
Autor:
He, Lie, Kasiviswanathan, Shiva Prasad
In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust learning, causal inference, etc. The sample-averaged gradient of the CS
Externí odkaz:
http://arxiv.org/abs/2304.10613
Autor:
Supongsenla Ao, Shiva Prasad Gouda, Lakshi Saikia, Baskar Gurunathan, Samuel Lalthazuala Rokhum
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract Carbon-based nanodots have garnered recent interest for their simple synthesis and versatile utility, ranging from biomedical to (opto) electronic applications, evolving into a tunable and biocompatible material. Here, for the first time, a
Externí odkaz:
https://doaj.org/article/39d2e21a8f5446718c0b64723a3d9188
We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available. Utilizing the recent developments in diffusion models, w
Externí odkaz:
http://arxiv.org/abs/2302.00860
In this work, we initiate the idea of using denoising diffusion models to learn priors for online decision making problems. Our special focus is on the meta-learning for bandit framework, with the goal of learning a strategy that performs well across
Externí odkaz:
http://arxiv.org/abs/2301.05182
Independence testing is a classical statistical problem that has been extensively studied in the batch setting when one fixes the sample size before collecting data. However, practitioners often prefer procedures that adapt to the complexity of a pro
Externí odkaz:
http://arxiv.org/abs/2212.07383