113 citations · 1k across the 66 of their papers we have counts for
9 papers · 1 filter
Balance Regularized Neural Network Models for Causal Effect Estimation
Mehrdad Farajtabar, Andrew Lee, Yuanjian Feng +4
Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce. In this paper, we advocate ba…
Linear Mode Connectivity in Multitask and Continual Learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Dilan Gorur +2
Continual (sequential) training and multitask (simultaneous) training are often attempting to solve the same overall objective: to find a solution that performs well on all conside…
The Effectiveness of Memory Replay in Large Scale Continual Learning
Yogesh Balaji, Mehrdad Farajtabar, Dong Yin +2
We study continual learning in the large scale setting where tasks in the input sequence are not limited to classification, and the outputs can be of high dimension. Among multiple…
A maximum-entropy approach to off-policy evaluation in average-reward MDPs
Nevena Lazic, Dong Yin, Mehrdad Farajtabar +4
This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear…
Understanding the Role of Training Regimes in Continual Learning
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu +1
Catastrophic forgetting affects the training of neural networks, limiting their ability to learn multiple tasks sequentially. From the perspective of the well established plasticit…
Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint
Dong Yin, Mehrdad Farajtabar, Ang Li +2
Neural networks have achieved remarkable success in many cognitive tasks. However, when they are trained sequentially on multiple tasks without access to old data, their performanc…