89 citations · 208 across the 26 of their papers we have counts for
7 papers · 1 filter
Geometric Median Matching for Robust k-Subset Selection from Noisy Data
Anish Acharya, Sujay Sanghavi, Alexandros G. Dimakis +1
Data pruning -- the combinatorial task of selecting a small and representative subset from a large dataset, is crucial for mitigating the enormous computational costs associated wi…
Geometric Median (GM) Matching for Robust Data Pruning
Anish Acharya, Inderjit S Dhillon, Sujay Sanghavi
Large-scale data collections in the wild, are invariably noisy. Thus developing data pruning strategies that remain robust even in the presence of corruption is critical in practic…
Understanding Contrastive Representation Learning from Positive Unlabeled (PU) Data
Anish Acharya, Li Jing, Bhargav Bhushanam +4
Pretext Invariant Representation Learning (PIRL) followed by Supervised Fine-Tuning (SFT) has become a standard paradigm for learning with limited labels. We extend this approach t…
Positive Unlabeled Contrastive Learning
Anish Acharya, Sujay Sanghavi, Li Jing +4
Self-supervised pretraining on unlabeled data followed by supervised fine-tuning on labeled data is a popular paradigm for learning from limited labeled examples. We extend this pa…
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Anish Acharya, Abolfazl Hashemi, Prateek Jain +3
Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakd…
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization
Abolfazl Hashemi, Anish Acharya, Rudrajit Das +3
In decentralized optimization, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e. averaging over the network) steps. M…
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization
Anish Acharya, Rahul Goel, Angeliki Metallinou +1
Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constrai…