16 citations · 16 across the 9 of their papers we have counts for
11 papers
Averaged Annealed Markov Chains Achieve the Algorithmic Threshold for Tensor PCA, Part I: Glauber Dynamics
Aniket Das, Tselil Schramm
In the tensor principal components analysis (tensor PCA) problem, we are asked to recover an unknown vector in from a noisy observation of its -th tensor power, for…
Parallel Sampling from the Ising -Spin Model
Nima Anari, Aniket Das, Alireza Haqi
We study the parallel complexity of sampling from the high-temperature Ising mixed -spin Gibbs measure, a canonical instance of a mean-field spin glass on the hypercube $\{\pm 1…
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Autospeculation
Hengyuan Hu, Aniket Das, Dorsa Sadigh +1
Denoising Diffusion Probabilistic Models (DDPMs) have emerged as powerful tools for generative modeling. However, their sequential computation requirements lead to significant infe…
Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD
Aniket Das, Dheeraj Nagaraj, Soumyabrata Pal +2
We consider the problem of high-dimensional heavy-tailed statistical estimation in the streaming setting, which is much harder than the traditional batch setting due to memory cons…
Near Optimal Heteroscedastic Regression with Symbiotic Learning
Dheeraj Baby, Aniket Das, Dheeraj Nagaraj +1
We consider the problem of heteroscedastic linear regression, where, given samples from $y_i = \langle \mathbf{w}^{*}, \mathbf{x}_i \rangle + ε_i \cdot \l…
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Aniket Das, Dheeraj Nagaraj
Stein Variational Gradient Descent (SVGD) is a popular variational inference algorithm which simulates an interacting particle system to approximately sample from a target distribu…
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
Aniket Das, Dheeraj Nagaraj, Anant Raj
We consider stochastic approximations of sampling algorithms, such as Stochastic Gradient Langevin Dynamics (SGLD) and the Random Batch Method (RBM) for Interacting Particle Dynamc…
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
Aniket Das, Bernhard Schölkopf, Michael Muehlebach
We analyze the convergence rates of stochastic gradient algorithms for smooth finite-sum minimax optimization and show that, for many such algorithms, sampling the data points with…
NeurInt : Learning to Interpolate through Neural ODEs
Avinandan Bose, Aniket Das, Yatin Dandi +1
A wide range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation present in the data distribution.…
Jointly Trained Image and Video Generation using Residual Vectors
Yatin Dandi, Aniket Das, Soumye Singhal +2
In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real…
TorchGAN: A Flexible Framework for GAN Training and Evaluation
Avik Pal, Aniket Das
TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's modular design al…