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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 ...
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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...
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05/24/2023
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Actor-critic (AC) methods are widely used in reinforcement learning (RL)...
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02/06/2023
Target-based Surrogates for Stochastic Optimization
We consider minimizing functions for which it is expensive to compute th...
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11/07/2022
Multi-Head Adapter Routing for Data-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) methods can adapt large language ...
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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...
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08/07/2021
Impact of Aliasing on Generalization in Deep Convolutional Networks
We investigate the impact of aliasing on generalization in Deep Convolut...
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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...
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06/18/2019
Information matrices and generalization
This work revisits the use of information criteria to characterize the g...
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06/08/2019
Reducing the variance in online optimization by transporting past gradients
Most stochastic optimization methods use gradients once before discardin...
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02/13/2019
Anytime Tail Averaging
Tail averaging consists in averaging the last examples in a stream. Comm...
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02/08/2019
Distributional reinforcement learning with linear function approximation
Despite many algorithmic advances, our theoretical understanding of prac...
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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...
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01/31/2019
A Geometric Perspective on Optimal Representations for Reinforcement Learning
This paper proposes a new approach to representation learning based on g...
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01/31/2019
The Value Function Polytope in Reinforcement Learning
We establish geometric and topological properties of the space of value ...
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11/27/2018
Understanding the impact of entropy on policy optimization
Entropy regularization is commonly used to improve policy optimization i...
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11/27/2018
Understanding the impact of entropy in policy learning
Entropy regularization is commonly used to improve policy optimization i...
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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...
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04/03/2017
A comparative study of counterfactual estimators
We provide a comparative study of several widely used off-policy estimat...
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12/28/2016
Efficient iterative policy optimization
We tackle the issue of finding a good policy when the number of policy u...
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06/29/2016
Tighter bounds lead to improved classifiers
The standard approach to supervised classification involves the minimiza...
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09/10/2013
Minimizing Finite Sums with the Stochastic Average Gradient
We propose the stochastic average gradient (SAG) method for optimizing t...
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07/19/2011
