223 citations · 513 across the 11 of their papers we have counts for
11 papers
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha +6
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contribution…
Local Policy Improvement for Recommender Systems
Dawen Liang, Nikos Vlassis
Recommender systems predict what items a user will interact with next, based on their past interactions. The problem is often approached through supervised learning, but recent adv…
Learning Correlated Latent Representations with Adaptive Priors
Da Tang, Dawen Liang, Nicholas Ruozzi +1
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data. When the correlation structure amon…
Correlated Variational Auto-Encoders
Da Tang, Dawen Liang, Tony Jebara +1
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton v…
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Yixin Wang, Dawen Liang, Laurent Charlin +1
The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question--…
Variational Autoencoders for Collaborative Filtering
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman +1
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacit…
On the challenges of learning with inference networks on sparse, high-dimensional data
Rahul G. Krishnan, Dawen Liang, Matthew Hoffman
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such mode…
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B. Dieng +3
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative p…
Modeling User Exposure in Recommendation
Dawen Liang, Laurent Charlin, James McInerney +1
Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feed…
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Dawen Liang, Matthew D. Hoffman
Beta process is the standard nonparametric Bayesian prior for latent factor model. In this paper, we derive a structured mean-field variational inference algorithm for a beta proce…
A Generative Product-of-Filters Model of Audio
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes sim…