Nicolas Le Roux | DeepAI

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∙ 06/21/2023

Deep Language Networks: Joint Prompt Training of Stacked LLMs using Variational Inference

We view large language models (LLMs) as stochastic language layers in a ...

0 Alessandro Sordoni, et al. ∙

∙ 06/11/2023

Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements

The growing utilization of machine learning (ML) in decision-making proc...

0 Maryam Molamohammadi, et al. ∙

∙ 05/24/2023

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees

Actor-critic (AC) methods are widely used in reinforcement learning (RL)...

0 Sharan Vaswani, et al. ∙

∙ 02/06/2023

Target-based Surrogates for Stochastic Optimization

We consider minimizing functions for which it is expensive to compute th...

0 Jonathan Wilder Lavington, et al. ∙

∙ 11/07/2022

Multi-Head Adapter Routing for Data-Efficient Fine-Tuning

Parameter-efficient fine-tuning (PEFT) methods can adapt large language ...

0 Lucas Caccia, et al. ∙

∙ 08/12/2021

A functional mirror ascent view of policy gradient methods with function approximation

We use functional mirror ascent to propose a general framework (referred...

13 Sharan Vaswani, et al. ∙

∙ 08/07/2021

Impact of Aliasing on Generalization in Deep Convolutional Networks

We investigate the impact of aliasing on generalization in Deep Convolut...

19 Cristina Vasconcelos, et al. ∙

∙ 06/30/2021

On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging

We study the stochastic bilinear minimax optimization problem, presentin...

1 Chris Junchi Li, et al. ∙

∙ 06/18/2019

Information matrices and generalization

This work revisits the use of information criteria to characterize the g...

4 Valentin Thomas, et al. ∙

∙ 06/08/2019

Reducing the variance in online optimization by transporting past gradients

Most stochastic optimization methods use gradients once before discardin...

0 Sébastien M. R. Arnold, et al. ∙

∙ 02/13/2019

Anytime Tail Averaging

Tail averaging consists in averaging the last examples in a stream. Comm...

0 Nicolas Le Roux, et al. ∙

∙ 02/08/2019

Distributional reinforcement learning with linear function approximation

Despite many algorithmic advances, our theoretical understanding of prac...

18 Marc G. Bellemare, et al. ∙

∙ 02/06/2019

Negative eigenvalues of the Hessian in deep neural networks

The loss function of deep networks is known to be non-convex but the pre...

0 Guillaume Alain, et al. ∙

∙ 01/31/2019

A Geometric Perspective on Optimal Representations for Reinforcement Learning

This paper proposes a new approach to representation learning based on g...

10 Marc G. Bellemare, et al. ∙

∙ 01/31/2019

The Value Function Polytope in Reinforcement Learning

We establish geometric and topological properties of the space of value ...

10 Robert Dadashi, et al. ∙

∙ 11/27/2018

Understanding the impact of entropy on policy optimization

Entropy regularization is commonly used to improve policy optimization i...

0 Zafarali Ahmed, et al. ∙

∙ 11/27/2018

Understanding the impact of entropy in policy learning

Entropy regularization is commonly used to improve policy optimization i...

0 Zafarali Ahmed, et al. ∙

∙ 10/20/2017

Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods

Our goal is to improve variance reducing stochastic methods through bett...

0 Robert M. Gower, et al. ∙

∙ 04/03/2017

A comparative study of counterfactual estimators

We provide a comparative study of several widely used off-policy estimat...

0 Thomas Nedelec, et al. ∙

∙ 12/28/2016

Efficient iterative policy optimization

We tackle the issue of finding a good policy when the number of policy u...

0 Nicolas Le Roux, et al. ∙

∙ 06/29/2016

Tighter bounds lead to improved classifiers

The standard approach to supervised classification involves the minimiza...

0 Nicolas Le Roux, et al. ∙

∙ 09/10/2013

Minimizing Finite Sums with the Stochastic Average Gradient

We propose the stochastic average gradient (SAG) method for optimizing t...

0 Mark Schmidt, et al. ∙

∙ 07/19/2011

Weakly Supervised Learning of Foreground-Background Segmentation using Masked RBMs

We propose an extension of the Restricted Boltzmann Machine (RBM) that a...

0 Nicolas Heess, et al. ∙

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