49 citations · 235 across the 48 of their papers we have counts for
11 papers · 1 filter
Offline Policy Selection under Uncertainty
Mengjiao Yang, Bo Dai, Ofir Nachum +2
The presence of uncertainty in policy evaluation significantly complicates the process of policy ranking and selection in real-world settings. We formally consider offline policy s…
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Hanjun Dai, Rishabh Singh, Bo Dai +2
Discrete structures play an important role in applications like program language modeling and software engineering. Current approaches to predicting complex structures typically co…
CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai, Ofir Nachum, Yinlam Chow +3
We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given on…
Attention that does not Explain Away
Nan Ding, Xinjie Fan, Zhenzhong Lan +2
Models based on the Transformer architecture have achieved better accuracy than the ones based on competing architectures for a large set of tasks. A unique feature of the Transfor…
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks
Denny Zhou, Mao Ye, Chen Chen +6
For deploying a deep learning model into production, it needs to be both accurate and compact to meet the latency and memory constraints. This usually results in a network that is…
A maximum-entropy approach to off-policy evaluation in average-reward MDPs
Nevena Lazic, Dong Yin, Mehrdad Farajtabar +4
This work focuses on off-policy evaluation (OPE) with function approximation in infinite-horizon undiscounted Markov decision processes (MDPs). For MDPs that are ergodic and linear…
Variational Inference for Deep Probabilistic Canonical Correlation Analysis
Mahdi Karami, Dale Schuurmans
In this paper, we propose a deep probabilistic multi-view model that is composed of a linear multi-view layer based on probabilistic canonical correlation analysis (CCA) descriptio…
Batch Stationary Distribution Estimation
Junfeng Wen, Bo Dai, Lihong Li +1
We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. Classical simulation-based approaches assume acc…
Energy-Based Processes for Exchangeable Data
Mengjiao Yang, Bo Dai, Hanjun Dai +1
Recently there has been growing interest in modeling sets with exchangeability such as point clouds. A shortcoming of current approaches is that they restrict the cardinality of th…
ConQUR: Mitigating Delusional Bias in Deep Q-learning
Andy Su, Jayden Ooi, Tyler Lu +2
Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular…
GenDICE: Generalized Offline Estimation of Stationary Values
Ruiyi Zhang, Bo Dai, Lihong Li +1
An important problem that arises in reinforcement learning and Monte Carlo methods is estimating quantities defined by the stationary distribution of a Markov chain. In many real-w…