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Reza Babanezhad Harikandeh

Independent visiting researcher - Samsung SAIT
Supervisor
Simon Lacoste-Julien
Research Topics
Optimization

Publications

Fast Convergence of Softmax Policy Mirror Ascent
Reza Asad
Reza Babanezhad Harikandeh
Issam Hadj Laradji
Nicolas Roux
Sharan Vaswani
Natural policy gradient (NPG) is a common policy optimization algorithm and can be viewed as mirror ascent in the space of probabilities. Re… (see more)cently, Vaswani et al. (2021) introduced a policy gradient method that corresponds to mirror ascent in the dual space of logits. We refine this algorithm, removing its need for a normalization across actions and analyze the resulting method (referred to as SPMA). For tabular MDPs, we prove that SPMA with a constant step-size matches the linear convergence of NPG and achieves a faster convergence than constant step-size (accelerated) softmax policy gradient. To handle large state-action spaces, we extend SPMA to use a log-linear policy parameterization. Unlike that for NPG, generalizing SPMA to the linear function approximation (FA) setting does not require compatible function approximation. Unlike MDPO, a practical generalization of NPG, SPMA with linear FA only requires solving convex softmax classification problems. We prove that SPMA achieves linear convergence to the neighbourhood of the optimal value function. We extend SPMA to handle non-linear FA and evaluate its empirical performance on the MuJoCo and Atari benchmarks. Our results demonstrate that SPMA consistently achieves similar or better performance compared to MDPO, PPO and TRPO.
2025-01-21
aistats.org/AISTATS/2025/Conference (poster)
proceedings.mlr.press
proceedings.mlr.press
Torque-Aware Momentum
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Gintare Karolina Dziugaite
Razvan Pascanu
Sarath Chandar
Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely … (see more)used in state-of-the-art setups, classical momentum can still struggle with large, misaligned gradients, leading to oscillations. To address this, we propose Torque-Aware Momentum (TAM), which introduces a damping factor based on the angle between the new gradients and previous momentum, stabilizing the update direction during training. Empirical results show that TAM, which can be combined with both SGD and Adam, enhances exploration, handles distribution shifts more effectively, and improves generalization performance across various tasks, including image classification and large language model fine-tuning, when compared to classical momentum-based optimizers.
2024-12-24
ArXiv (preprint)
doi.org
arxiv.org
Fast Convergence of Softmax Policy Mirror Ascent for Bandits & Tabular MDPs
Reza Asad
Reza Babanezhad Harikandeh
Issam Hadj Laradji
Nicolas Roux
Sharan Vaswani
We analyze the convergence of a novel policy gradient algorithm (referred to as SPMA) for multi-armed bandits and tabular Markov decision pr… (see more)ocesses (MDPs). SPMA is an instantiation of mirror ascent and uses the softmax parameterization with a log-sum-exp mirror map. Given access to the exact policy gradients, we prove that SPMA with a constant step-size requires
2024-10-09
NeurIPS.cc/2024/Workshop/OPT (published)
openreview.net
openreview.net
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya
Goncalo Mordido
Aristide Baratin
Reza Babanezhad Harikandeh
Jerry Huang
Simon Lacoste-Julien
Razvan Pascanu
Sarath Chandar
Adaptive gradient-based optimizers, notably Adam, have left their mark in training large-scale deep learning models, offering fast convergen… (see more)ce and robustness to hyperparameter settings. However, they often struggle with generalization, attributed to their tendency to converge to sharp minima in the loss landscape. To address this, we propose a new memory-augmented version of Adam that encourages exploration towards flatter minima by incorporating a buffer of critical momentum terms during training. This buffer prompts the optimizer to overshoot beyond narrow minima, promoting exploration. Through comprehensive analysis in simple settings, we illustrate the efficacy of our approach in increasing exploration and bias towards flatter minima. We empirically demonstrate that it can improve model performance for image classification on ImageNet and CIFAR10/100, language modelling on Penn Treebank, and online learning tasks on TinyImageNet and 5-dataset. Our code is available at https://github.com/chandar-lab/CMOptimizer.
2024-06-08
TMLR (accepted)
doi.org
openreview.net
Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism
Reza Asad
Reza Babanezhad Harikandeh
Issam Hadj Laradji
Nicolas Roux
Sharan Vaswani
2023-10-25
NeurIPS.cc/2023/Workshop/OPT (poster)
openreview.net
openreview.net
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Sharan Vaswani
Amirreza Kazemi
Reza Babanezhad Harikandeh
Nicolas Roux
Actor-critic (AC) methods are widely used in reinforcement learning (RL) and benefit from the flexibility of using any policy gradient metho… (see more)d as the actor and value-based method as the critic. The critic is usually trained by minimizing the TD error, an objective that is potentially decorrelated with the true goal of achieving a high reward with the actor. We address this mismatch by designing a joint objective for training the actor and critic in a decision-aware fashion. We use the proposed objective to design a generic, AC algorithm that can easily handle any function approximation. We explicitly characterize the conditions under which the resulting algorithm guarantees monotonic policy improvement, regardless of the choice of the policy and critic parameterization. Instantiating the generic algorithm results in an actor that involves maximizing a sequence of surrogate functions (similar to TRPO, PPO) and a critic that involves minimizing a closely connected objective. Using simple bandit examples, we provably establish the benefit of the proposed critic objective over the standard squared error. Finally, we empirically demonstrate the benefit of our decision-aware actor-critic framework on simple RL problems.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net
Target-based Surrogates for Stochastic Optimization
Jonathan Wilder Lavington
Sharan Vaswani
Reza Babanezhad Harikandeh
Mark Schmidt
Nicolas Roux
We consider minimizing functions for which it is expensive to compute the gradient. Such functions are prevalent in reinforcement learning, … (see more)imitation learning and bilevel optimization. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a \emph{target space} (e.g. the logits output by a linear model for classification) that can be minimized efficiently. This allows for multiple parameter updates to the model, amortizing the cost of gradient computation. In the full-batch setting, we prove that our surrogate is a global upper-bound on the loss, and can be (locally) minimized using a black-box optimization algorithm. We prove that the resulting majorization-minimization algorithm ensures convergence to a stationary point of the loss. Next, we instantiate our framework in the stochastic setting and propose the
2023-04-23
ICML.cc/2023/Conference (poster)
doi.org
proceedings.mlr.press
SVRG meets AdaGrad: painless variance reduction
Benjamin Dubois-Taine
Sharan Vaswani
Reza Babanezhad Harikandeh
Mark Schmidt
Simon Lacoste-Julien
2022-11-09
Machine Learning (published)
doi.org
arxiv.org
Towards Painless Policy Optimization for Constrained MDPs
Arushi Jain
Sharan Vaswani
Reza Babanezhad Harikandeh
Csaba Szepesvari
Doina Precup
We study policy optimization in an infinite horizon, …
2021-12-31
UAI (published)
doi.org
proceedings.mlr.press
An Analysis of the Adaptation Speed of Causal Models
Rémi Le Priol
Reza Babanezhad Harikandeh
Yoshua Bengio
Simon Lacoste-Julien
Consider a collection of datasets generated by unknown interventions on an unknown structural causal model …
2020-12-31
AISTATS (published)
doi.org
proceedings.mlr.press
To Each Optimizer a Norm, To Each Norm its Generalization
Sharan Vaswani
Reza Babanezhad Harikandeh
Jose Gallego
Aaron Mishkin
Simon Lacoste-Julien
Nicolas Roux
We study the implicit regularization of optimization methods for linear models interpolating the training data in the under-parametrized and… (see more) over-parametrized regimes. Since it is difficult to determine whether an optimizer converges to solutions that minimize a known norm, we flip the problem and investigate what is the corresponding norm minimized by an interpolating solution. Using this reasoning, we prove that for over-parameterized linear regression, projections onto linear spans can be used to move between different interpolating solutions. For under-parameterized linear classification, we prove that for any linear classifier separating the data, there exists a family of quadratic norms ||.||_P such that the classifier's direction is the same as that of the maximum P-margin solution. For linear classification, we argue that analyzing convergence to the standard maximum l2-margin is arbitrary and show that minimizing the norm induced by the data results in better generalization. Furthermore, for over-parameterized linear classification, projections onto the data-span enable us to use techniques from the under-parameterized setting. On the empirical side, we propose techniques to bias optimizers towards better generalizing solutions, improving their test performance. We validate our theoretical results via synthetic experiments, and use the neural tangent kernel to handle non-linear models.
2020-06-10
ArXiv (preprint)
arxiv.org
arxiv.org
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold
Pierre-Antoine Manzagol
Reza Babanezhad Harikandeh
Ioannis Mitliagkas
Nicolas Roux
Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing pas… (see more)t gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting. One issue is the staleness due to using past gradients. We propose to correct this staleness using the idea of implicit gradient transport (IGT) which transforms gradients computed at previous iterates into gradients evaluated at the current iterate without using the Hessian explicitly. In addition to reducing the variance and bias of our updates over time, IGT can be used as a drop-in replacement for the gradient estimate in a number of well-understood methods such as heavy ball or Adam. We show experimentally that it achieves state-of-the-art results on a wide range of architectures and benchmarks. Additionally, the IGT gradient estimator yields the optimal asymptotic convergence rate for online stochastic optimization in the restricted setting where the Hessians of all component functions are equal.
2018-12-31
Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (published)
arxiv.org
arxiv.org