13 citations · 29 across the 17 of their papers we have counts for
23 papers
Variable-Complexity Weighted-Tempered Gibbs Samplers for Bayesian Variable Selection
Lan V. Truong
Subset weighted-Tempered Gibbs Sampler (wTGS) has been recently introduced by Jankowiak to reduce the computation complexity per MCMC iteration in high-dimensional applications whe…
Global Convergence Rate of Deep Equilibrium Models with General Activations
Lan V. Truong
In a recent paper, Ling et al. investigated the over-parametrized Deep Equilibrium Model (DEQ) with ReLU activation. They proved that the gradient descent converges to a globally o…
Generalized Random Gilbert-Varshamov Codes: Typical Error Exponent and Concentration Properties
Lan V. Truong, Albert Guillén i Fàbregas
We find the exact typical error exponent of constant composition generalized random Gilbert-Varshamov (RGV) codes over DMCs channels with generalized likelihood decoding. We show t…
Generative Adversarial Nets: Can we generate a new dataset based on only one training set?
Lan V. Truong
A generative adversarial network (GAN) is a class of machine learning frameworks designed by Goodfellow et al. in 2014. In the GAN framework, the generative model is pitted against…
On Rademacher Complexity-based Generalization Bounds for Deep Learning
Lan V. Truong
We show that the Rademacher complexity-based framework can establish non-vacuous generalization bounds for Convolutional Neural Networks (CNNs) in the context of classifying a smal…
Generalization Bounds on Multi-Kernel Learning with Mixed Datasets
Lan V. Truong
This paper presents novel generalization bounds for the multi-kernel learning problem. Motivated by applications in sensor networks and spatial-temporal models, we assume that the…
Concentration Properties of Random Codes
Lan V. Truong, Giuseppe Cocco, Josep Font-Segura +1
This paper studies the concentration properties of random codes. Specifically, we show that, for discrete memoryless channels, the error exponent of a randomly generated code with…
Generalization Error Bounds on Deep Learning with Markov Datasets
Lan V. Truong
In this paper, we derive upper bounds on generalization errors for deep neural networks with Markov datasets. These bounds are developed based on Koltchinskii and Panchenko's appro…
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity
Lan V. Truong
We establish exact asymptotic expressions for the normalized mutual information and minimum mean-square-error (MMSE) of sparse linear regression in the sub-linear sparsity regime.…
Replica Analysis of the Linear Model with Markov or Hidden Markov Signal Priors
Lan V. Truong
This paper estimates free energy, average mutual information, and minimum mean square error (MMSE) of a linear model under two assumptions: (1) the source is generated by a Markov…
On the All-Or-Nothing Behavior of Bernoulli Group Testing
Lan V. Truong, Matthew Aldridge, Jonathan Scarlett
In this paper, we study the problem of non-adaptive group testing, in which one seeks to identify which items are defective given a set of suitably-designed tests whose outcomes in…
Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits
Lan V. Truong, Jonathan Scarlett
The support recovery problem consists of determining a sparse subset of variables that is relevant in generating a set of observations. In this paper, we study the support recovery…
On the Capacity of Symmetric -user Gaussian Interference Channels with Feedback
Lan V. Truong, Hirosuke Yamamoto
A general time-varying feedback coding scheme is proposed for -user fully connected symmetric Gaussian interference channels. Based on the analysis of the general coding scheme,…
The Optimal Compression Rate of Variable-to-Fixed Length Source Coding with a Non-Vanishing Excess-Distortion Probability
Lan V. Truong, Vincent Y. F. Tan
We consider the variable-to-fixed length lossy source coding (VFSC) problem. The optimal compression rate of the average length of variable-to-fixed source coding, allowing a non-v…
The Reliability Function of Lossy Source-Channel Coding of Variable-Length Codes with Feedback
Lan V. Truong, Vincent Y. F. Tan
We consider transmission of discrete memoryless sources (DMSes) across discrete memoryless channels (DMCs) using variable-length lossy source-channel codes with feedback. The relia…
Moderate Deviation Asymptotics for Variable-Length Codes with Feedback
Lan V. Truong, Vincent Y. F. Tan
We consider data transmission across discrete memoryless channels (DMCs) using variable-length codes with feedback. We consider the family of such codes whose rates are below…
Performance of Viterbi Decoding with and without ARQ on Rician Fading Channels
Lan V. Truong
In this paper, we investigate the performance of the Viterbi decoding algorithm with/without Automatic Repeat reQuest (ARQ) over a Rician flat fading channel with unlimited interle…
On the Reliability Function of the Common-Message Broadcast Channel with Variable-Length Feedback
Lan V. Truong, Vincent Y. F. Tan
We derive upper and lower bounds on the reliability function for the common-message discrete memoryless broadcast channel with variable-length feedback. We show that the bounds are…
On Gaussian MACs with Variable-Length Feedback and Non-Vanishing Error~Probabilities
Lan V. Truong, Vincent Y. F. Tan
We characterize the fundamental limits of transmission of information over a Gaussian multiple access channel (MAC) with the use of variable-length feedback codes and under a non-v…
On Gaussian Channels with Feedback under Expected Power Constraints and with Non-Vanishing Error Probabilities
Lan V. Truong, Silas L. Fong, Vincent Y. F. Tan
In this paper, we consider single- and multi-user Gaussian channels with feedback under expected power constraints and with non-vanishing error probabilities. In the first of two c…
On the Capacity of Symmetric Gaussian Interference Channels with Feedback
Lan V. Truong, Hirosuke Yamamoto
In this paper, we propose a new coding scheme for symmetric Gaussian interference channels with feedback based on the ideas of time-varying coding schemes. The proposed scheme impr…
Posterior Matching for Gaussian Broadcast Channels with Feedback
Lan V. Truong, Hirosuke Yamamoto
In this paper, the posterior matching scheme proposed by Shayevits and Feder is extended to the Gaussian broadcast channel with feedback, and the error probabilities and achievable…
Posterior Matching Scheme for Gaussian Multiple Access Channel with Feedback
Lan V. Truong
Posterior matching is a method proposed by Ofer Shayevitz and Meir Feder to design capacity achieving coding schemes for general point-to-point memoryless channels with feedback. I…