Portrait of Juan Ramirez

Juan Ramirez

PhD - Université de Montréal
Supervisor
Simon Lacoste-Julien
Research Topics
Applied Machine Learning
Constraint Programming
Optimization
Trustworthy Machine Learning
Website
Google Scholar
GitHub

Publications

Feasible Learning
Juan Ramirez
Ignacio Hounie
Juan Elenter
Jose Gallego-Posada
Meraj Hashemizadeh
Alejandro Ribeiro
Simon Lacoste-Julien
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bound… (see more)s the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance \emph{on every individual data point}. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
2025-04-23
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
Feasible Learning
Juan Ramirez
Ignacio Hounie
Juan Elenter
Jose Gallego-Posada
Meraj Hashemizadeh
Alejandro Ribeiro
Simon Lacoste-Julien
We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bound… (see more)s the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance *on every individual data point*. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.
2025-01-22
aistats.org/AISTATS/2025/Conference (poster)
openreview.net
openreview.net
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
Motahareh Sohrabi
Juan Ramirez
Tianyue H. Zhang
Simon Lacoste-Julien
Jose Gallego-Posada
Constrained optimization offers a powerful framework to prescribe desired behaviors in neural network models. Typically, constrained problem… (see more)s are solved via their min-max Lagrangian formulations, which exhibit unstable oscillatory dynamics when optimized using gradient descent-ascent. The adoption of constrained optimization techniques in the machine learning community is currently limited by the lack of reliable, general-purpose update schemes for the Lagrange multipliers. This paper proposes the νPI algorithm and contributes an optimization perspective on Lagrange multiplier updates based on PI controllers, extending the work of Stooke, Achiam and Abbeel (2020). We provide theoretical and empirical insights explaining the inability of momentum methods to address the shortcomings of gradient descent-ascent, and contrast this with the empirical success of our proposed νPI controller. Moreover, we prove that νPI generalizes popular momentum methods for single-objective minimization. Our experiments demonstrate that νPI reliably stabilizes the multiplier dynamics and its hyperparameters enjoy robust and predictable behavior.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
proceedings.mlr.press
openreview.net
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 … (see more)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… (see more). 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 (oral)
doi.org
openreview.net
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Jose Gallego-Posada
Juan Ramirez
Akram Erraqabi
Yoshua Bengio
Simon Lacoste-Julien
The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models,… (see more) this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.
openreview.net