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Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
14May
Seminar Calendar, Information and Innovation Management

Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers

14 May 2025 | 11:00 a.m.. — 12:00 noon
KKL 1121, 11/F ., K.K. Leung Building, HKU
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SPEAKER

Prof. Yuqi Gu
Assistant Professor
Department of Statistics
Columbia University

ABSTRACT

In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose an interpretable deep generative modeling framework for rich data types with discrete latent layers, called Deep Discrete Encoders (DDEs). A DDE is a directed graphical model with multiple binary latent layers. Theoretically, we propose transparent identifiability conditions for DDEs, which imply progressively smaller sizes of the latent layers as they go deeper. Identifiability ensures consistent parameter estimation and inspires an interpretable design of the deep architecture. Computationally, we propose a scalable estimation pipeline of a layerwise nonlinear spectral initialization followed by a penalized stochastic approximation EM algorithm. This procedure can efficiently estimate models with exponentially many latent components. Extensive simulation studies validate our theoretical results and demonstrate the proposed algorithms’ excellent performance. We apply DDEs to three diverse real datasets for hierarchical topic modeling, image representation learning, response time modeling in educational testing, and obtain interpretable findings.

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Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
About Speaker
Prof. Yuqi Gu

Assistant Professor | Department of Statistics | Columbia University

Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
14 May 202511:00 a.m.. — 12:00 noon
KKL 1121, 11/F ., K.K. Leung Building, HKU
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