no code implementations • 20 May 2024 • Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian McAuley
The RTA framework marries the benefits of both LLMs and traditional recommender systems (RecSys): understanding complex queries as LLMs do; while efficiently controlling the recommended item distributions in conversational recommendations as traditional RecSys do.
no code implementations • 10 Mar 2024 • Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
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.
no code implementations • 8 Mar 2024 • Alex Ayoub, Kaiwen Wang, Vincent Liu, Samuel Robertson, James McInerney, Dawen Liang, Nathan Kallus, Csaba Szepesvári
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
no code implementations • 24 Oct 2023 • Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian McAuley
To address these challenges, we introduce the Policy Convolution (PC) family of estimators.
1 code implementation • 19 Aug 2023 • Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian McAuley
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.
no code implementations • 22 Dec 2022 • Dawen Liang, Nikos Vlassis
The conventional way to address this problem is through importance sampling correction, but this comes with practical limitations.
no code implementations • 14 Jun 2019 • Da Tang, Dawen Liang, Nicholas Ruozzi, Tony Jebara
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.
2 code implementations • ICLR Workshop DeepGenStruct 2019 • Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.
no code implementations • 20 Aug 2018 • Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
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."
18 code implementations • 16 Feb 2018 • Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
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
1 code implementation • 17 Oct 2017 • 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.
no code implementations • 31 Oct 2016 • Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
Probabilistic modeling is a powerful approach for analyzing empirical information.
1 code implementation • 23 Oct 2015 • Dawen Liang, Laurent Charlin, James McInerney, David M. Blei
The exposure is modeled as a latent variable and the model infers its value from data.
no code implementations • 7 Nov 2014 • Dawen Liang, Matthew D. Hoffman
Beta process is the standard nonparametric Bayesian prior for latent factor model.
1 code implementation • 20 Dec 2013 • 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.