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Rohan Sukumaran

rohan.sukumaran@mila.quebec
Maîtrise recherche - Université de Montréal
Superviseur⋅e principal⋅e
Golnoosh Farnadi
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Publications

Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh
Juan Ramirez
Rohan Sukumaran
Golnoosh Farnadi
Simon Lacoste-Julien
Jose Gallego-Posada
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or … (voir plus)storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that directly addresses the disparate impact of pruning: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Omega: Optimistic EMA Gradients
Juan Ramirez
Rohan Sukumaran
Quentin Bertrand
Gauthier Gidel
Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training… (voir plus). Although game optimization is fairly well understood in the deterministic setting, some issues persist in the stochastic regime. Recent work has shown that stochastic gradient descent-ascent methods such as the optimistic gradient are highly sensitive to noise or can fail to converge. Although alternative strategies exist, they can be prohibitively expensive. We introduce Omega, a method with optimistic-like updates that mitigates the impact of noise by incorporating an EMA of historic gradients in its update rule. We also explore a variation of this algorithm that incorporates momentum. Although we do not provide convergence guarantees, our experiments on stochastic games show that Omega outperforms the optimistic gradient method when applied to linear players.
2023-07-02
ICML.cc/2023/Workshop/LXAI_Regular_Deadline (présentation orale)
doi.org
openreview.net