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09/08/2023
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
A fundamental question in theoretical machine learning is generalization...
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07/01/2023
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations
The problem of function approximation by neural dynamical systems has ty...
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05/24/2023
Can Transformers Learn to Solve Problems Recursively?
Neural networks have in recent years shown promise for helping software ...
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05/18/2023
A unified framework for information-theoretic generalization bounds
This paper presents a general methodology for deriving information-theor...
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05/04/2023
Majorizing Measures, Codes, and Information
The majorizing measure theorem of Fernique and Talagrand is a fundamenta...
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04/27/2023
A Chain Rule for the Expected Suprema of Bernoulli Processes
We obtain an upper bound on the expected supremum of a Bernoulli process...
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03/16/2023
Variational Principles for Mirror Descent and Mirror Langevin Dynamics
Mirror descent, introduced by Nemirovski and Yudin in the 1970s, is a pr...
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12/01/2022
Nonlinear controllability and function representation by neural stochastic differential equations
There has been a great deal of recent interest in learning and approxima...
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04/03/2022
Fitting an immersed submanifold to data via Sussmann's orbit theorem
This paper describes an approach for fitting an immersed submanifold of ...
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02/14/2022
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
This paper proposes a class of neural ordinary differential equations pa...
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10/04/2021
Information-theoretic generalization bounds for black-box learning algorithms
We derive information-theoretic generalization bounds for supervised lea...
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12/29/2020
Minimum Excess Risk in Bayesian Learning
We analyze the best achievable performance of Bayesian learning under ge...
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11/18/2020
Learning Recurrent Neural Net Models of Nonlinear Systems
We consider the following learning problem: Given sample pairs of input ...
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02/05/2020
A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics
We consider the problem of universal approximation of functions by two-l...
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11/12/2019
Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection
Markov blanket feature selection, while theoretically optimal, generally...
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06/21/2019
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
There has been a recent shift in sequence-to-sequence modeling from recu...
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05/23/2019
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit
In deep latent Gaussian models, the latent variable is generated by a ti...
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05/17/2019
Non-signaling Approximations of Stochastic Team Problems
In this paper, we consider non-signaling approximation of finite stochas...
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03/05/2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions
We introduce and study a class of probabilistic generative models, where...
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12/23/2018
Learning finite-dimensional coding schemes with nonlinear reconstruction maps
This paper generalizes the Maurer--Pontil framework of finite-dimensiona...
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09/13/2018
Channel Polarization through the Lens of Blackwell Measures
Each memoryless binary-input channel (BIC) can be uniquely described by ...
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02/18/2018
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
We study the detailed path-wise behavior of the discrete-time Langevin a...
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05/22/2017
Information-theoretic analysis of generalization capability of learning algorithms
We derive upper bounds on the generalization error of a learning algorit...
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05/22/2017
Cost-Performance Tradeoffs in Fusing Unreliable Computational Units
We investigate fusing several unreliable computational units that perfor...
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05/19/2017
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD
We present a generic framework for trading off fidelity and cost in comp...
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02/13/2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of Sto...
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12/07/2011
