10 citations · 14 across the 13 of their papers we have counts for
8 papers · 1 filter
A Generalisation Signal Need Not Be a Model-Selection Signal
Aditya Nagarsekar, M P Ashish Bhat, Aadi Nesarkar +5
Model selection in computational biology often relies on validation data drawn from the training regime, even when deployment lies outside it. When validation no longer preserves w…
Matching High-Dimensional Geometric Quantiles for Test-Time Adaptation of Transformers and Convolutional Networks Alike
Sravan Danda, Aditya Challa, Shlok Mehendale +1
Test-time adaptation (TTA) refers to adapting a classifier for the test data when the probability distribution of the test data slightly differs from that of the training data of t…
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications
Shlok Mehendale, Aditya Challa, Rahul Yedida +3
Which principle underpins the design of an effective anomaly detection loss function? The answer lies in the concept of Radon-Nikodým theorem, a fundamental concept in measure theo…
A Granger-Causal Perspective on Gradient Descent with Application to Pruning
Aditya Shah, Aditya Challa, Sravan Danda +2
Stochastic Gradient Descent (SGD) is the main approach to optimizing neural networks. Several generalization properties of deep networks, such as convergence to a flatter minima, a…
Quantile Activation: Correcting a Failure Mode of ML Models
Aditya Challa, Sravan Danda, Laurent Najman +1
Standard ML models fail to infer the context distribution and suitably adapt. For instance, the learning fails when the underlying distribution is actually a mixture of distributio…
A Novel Approach to Regularising 1NN classifier for Improved Generalization
Aditya Challa, Sravan Danda, Laurent Najman
In this paper, we propose a class of non-parametric classifiers, that learn arbitrary boundaries and generalize well. Our approach is based on a novel way to regularize 1NN classif…
QuantProb: Generalizing Probabilities along with Predictions for a Pre-trained Classifier
Aditya Challa, Snehanshu Saha, Soma Dhavala
Quantification of Uncertainty in predictions is a challenging problem. In the classification settings, although deep learning based models generalize well, class probabilities ofte…
Higher-Order Relations Skew Link Prediction in Graphs
Govind Sharma, Aditya Challa, Paarth Gupta +1
The problem of link prediction is of active interest. The main approach to solving the link prediction problem is based on heuristics such as Common Neighbors (CN) -- more number o…