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Found 14 papers, 6 papers with code
A Generative Product-of-Filters Model of Audio
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.
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Beta process is the standard nonparametric Bayesian prior for latent factor model.
Modeling User Exposure in Recommendation
The exposure is modeled as a latent variable and the model infers its value from data.
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information.
On the challenges of learning with inference networks on sparse, high-dimensional data
We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network.
Variational Autoencoders for Collaborative Filtering
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
Ranked #5 on
Recommendation Systems
on Million Song Dataset
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome."
Correlated Variational Auto-Encoders
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.
Learning Correlated Latent Representations with Adaptive Priors
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.
Local Policy Improvement for Recommender Systems
The conventional way to address this problem is through importance sampling correction, but this comes with practical limitations.
Large Language Models as Zero-Shot Conversational Recommenders
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.
Off-Policy Evaluation for Large Action Spaces via Policy Convolution
To address these challenges, we introduce the Policy Convolution (PC) family of estimators.
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL
We study Risk-Sensitive Reinforcement Learning (RSRL) with the Optimized Certainty Equivalent (OCE) risk, which generalizes Conditional Value-at-risk (CVaR), entropic risk and Markowitz's mean-variance.
