2 citations · 2 across the 4 of their papers we have counts for
8 papers
Symbolic Density Estimators for Unnormalized Distributions
Vikas Kanaujia, Riyansha Singh, Shashank Sharma +1
Estimating the symbolic or analytical form of probability density functions (PDFs) from observed samples is a fundamental challenge in statistical and computational modelling. This…
FUND: Density Flow for Sampling Unnormalised Distributions
Vikas Kanaujia, Vipul Arora
Efficient sampling from Boltzmann distributions is central to modelling complex physical systems. Markov Chain Monte Carlo (MCMC) methods suffer from critical slowing down, high au…
Testing Sparse Functions over the Reals
Vipul Arora, Arnab Bhattacharyya, Philips George John +1
Over the last three decades, function testing has been extensively studied over Boolean, finite fields, and discrete settings. However, to encode the real-world applications more s…
SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions
Vikas Kanaujia, Vipul Arora
Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling methods, such as Markov Chain Monte…
On Optimal Testing of Linearity
Vipul Arora, Esty Kelman, Uri Meir
Linearity testing has been a focal problem in property testing of functions. We combine different known techniques and observations about linearity testing in order to resolve two…
Outlier Robust Multivariate Polynomial Regression
Vipul Arora, Arnab Bhattacharyya, Mathews Boban +2
We study the problem of robust multivariate polynomial regression: let be an unknown -variate polynomial of degree at most in each variabl…
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASR
Nagarathna Ravi, Thishyan Raj T, Vipul Arora
Confidence estimation of predictions from an End-to-End (E2E) Automatic Speech Recognition (ASR) model benefits ASR's downstream and upstream tasks. Class-probability-based confide…
AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learning
Vikas Kanaujia, Mathias S. Scheurer, Vipul Arora
Deep generative models complement Markov-chain-Monte-Carlo methods for efficiently sampling from high-dimensional distributions. Among these methods, explicit generators, such as N…