6 citations · 30 across the 20 of their papers we have counts for
15 papers · 1 filter
Gradient Coreset for Federated Learning
Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh Iyer +1
Federated Learning (FL) is used to learn machine learning models with data that is partitioned across multiple clients, including resource-constrained edge devices. It is therefore…
Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala +2
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a student model may…
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde +6
Tabular data is one of the most commonly used types of data in machine learning. Despite recent advances in neural nets (NNs) for tabular data, there is still an active discussion…
Speeding up NAS with Adaptive Subset Selection
Vishak Prasad C, Colin White, Paarth Jain +2
A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final perfo…
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Ashish Mittal, Durga Sivasubramanian, Rishabh Iyer +2
Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost. Training with a subset of training data could mitigate this proble…
AutoML for Climate Change: A Call to Action
Renbo Tu, Nicholas Roberts, Vishak Prasad +7
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate c…