107 citations · 232 across the 8 of their papers we have counts for
9 papers
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise
Minjia Zhang, Niranjan Uma Naresh, Yuxiong He
In recent years, large pre-trained Transformer-based language models have led to dramatic improvements in many natural language understanding tasks. To train these models with incr…
Provable Inductive Robust PCA via Iterative Hard Thresholding
U. N. Niranjan, Arun Rajkumar, Theja Tulabandhula
The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse compo…
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier
U. N. Niranjan, Arun Rajkumar
We study the problem of ranking a set of items from nonactively chosen pairwise preferences where each item has feature information with it. We propose and characterize a very broa…
Tensor Contractions with Extended BLAS Kernels on CPU and GPU
Yang Shi, U. N. Niranjan, Animashree Anandkumar +1
Tensor contractions constitute a key computational ingredient of numerical multi-linear algebra. However, as the order and dimension of tensors grow, the time and space complexitie…
Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Animashree Anandkumar, Prateek Jain, Yang Shi +1
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components. We propose a novel non-convex iterative algorithm with guaranteed recovery. It alt…
Non-convex Robust PCA
Praneeth Netrapalli, U N Niranjan, Sujay Sanghavi +2
We propose a new method for robust PCA -- the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support. Our method involves alternating be…