1 citations · 2 across the 7 of their papers we have counts for
7 papers · 1 filter
A Bayesian Information-Theoretic Approach to Data Attribution
Dharmesh Tailor, Nicolò Felicioni, Kamil Ciosek
Training Data Attribution (TDA) seeks to trace model predictions back to influential training examples, enhancing interpretability and safety. We formulate TDA as a Bayesian inform…
Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence
Dharmesh Tailor, Alvaro H. C. Correia, Eric Nalisnick +1
Uncertainty quantification is an important prerequisite for the deployment of deep learning models in safety-critical areas. Yet, this hinges on the uncertainty estimates being use…
On Equivariant Model Selection through the Lens of Uncertainty
Putri A. van der Linden, Alexander Timans, Dharmesh Tailor +1
Equivariant models leverage prior knowledge on symmetries to improve predictive performance, but misspecified architectural constraints can harm it instead. While work has explored…
Learning to Defer to a Population: A Meta-Learning Approach
Dharmesh Tailor, Aditya Patra, Rajeev Verma +2
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert. All existing work on L2D assumes that eac…
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Peter Nickl, Lu Xu, Dharmesh Tailor +2
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory…
Exploiting Inferential Structure in Neural Processes
Dharmesh Tailor, Mohammad Emtiyaz Khan, Eric Nalisnick
Neural Processes (NPs) are appealing due to their ability to perform fast adaptation based on a context set. This set is encoded by a latent variable, which is often assumed to fol…
Learning the optimal state-feedback via supervised imitation learning
Dharmesh Tailor, Dario Izzo
Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from expert agents. By substituting expert demonstrations for optima…