523 citations · 584 across the 20 of their papers we have counts for
10 papers · 1 filter
Improving Selective Classification with Pairwise Queries for Binary Classification
Harsh Vardhan, Sunav Choudhary, Natwar Modani +1
In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which it is not confident. The re…
Learning to Reason in LLMs by Expectation Maximization
Junghyun Lee, Branislav Kveton, Anup Rao +4
Large language models (LLMs) solve reasoning problems by first generating a rationale and then answering. We formalize reasoning as a latent variable model and derive a reward-base…
Memory Savings at What Cost? A Study of Alternatives to Backpropagation
Kunjal Panchal, Sunav Choudhary, Yuriy Brun +1
Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization are increasingly proposed as memory-efficient, backpropagation-free alternatives for large language m…
Sequential Causal Discovery with Noisy Language Model Priors
Prakhar Verma, David Arbour, Sunav Choudhary +3
Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data often arrive in batches, are subjec…
Sparse Linear Bandits with Blocking Constraints
Adit Jain, Soumyabrata Pal, Sunav Choudhary +3
We investigate the high-dimensional sparse linear bandits problem in a data-poor regime where the time horizon is much smaller than the ambient dimension and number of arms. We stu…
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
Kunjal Panchal, Nisarg Parikh, Sunav Choudhary +3
Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using pr…
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning
Hamid Mozaffari, Sunav Choudhary, Amir Houmansadr
Federated learning (FL) is a distributed machine learning paradigm that enables training models on decentralized data. The field of FL security against poisoning attacks is plagued…
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing
Kunjal Panchal, Sunav Choudhary, Nisarg Parikh +2
Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coar…
Federated Learning with Personalization Layers
Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh +1
The emerging paradigm of federated learning strives to enable collaborative training of machine learning models on the network edge without centrally aggregating raw data and hence…
Data-Driven Compression of Convolutional Neural Networks
Ramit Pahwa, Manoj Ghuhan Arivazhagan, Ankur Garg +3
Deploying trained convolutional neural networks (CNNs) to mobile devices is a challenging task because of the simultaneous requirements of the deployed model to be fast, lightweigh…