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Found 15 papers, 1 papers with code
基于自动识别的委婉语历时性发展变化与社会共变研究(A Study on the Diachronic Development and Social Covariance of Euphemism Based on Automatic Recognition)
“本文主要以汉语委婉语作为研究对象, 基于大量人工标注, 借助机器学习有监督分类方法, 实现了较高精度的委婉语自动识别, 并基于此对1946年-2017年的《人民日报》中的委婉语历时变化发展情况进行量化统计分析。从大规模数据的角度探讨委婉语历时性发展变化、委婉语与社会之间的共变关系, 验证了语言的格雷什姆规律与更新规律。”
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate
The Covid-19 pandemic has led to infodemic of low quality information leading to poor health decisions.
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
However, a precise understanding of the statistical limits with function representations, remains elusive, even when such a representation is linear.
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
In this paper, we propose a new algorithm based on stage-wise exploration and adaptive policy elimination that achieves a regret of $\widetilde{O}(\sqrt{H^4S^2AT})$ while requiring a switching cost of $O(HSA \log\log T)$.
Towards Instance-Optimal Offline Reinforcement Learning with Pessimism
We study the offline reinforcement learning (offline RL) problem, where the goal is to learn a reward-maximizing policy in an unknown Markov Decision Process (MDP) using the data coming from a policy $\mu$.
Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings
This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for episodic MDP) and provides a unified framework towards optimal learning for several well-motivated offline tasks.
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
Our main result shows that OPDVR provably identifies an $\epsilon$-optimal policy with $\widetilde{O}(H^2/d_m\epsilon^2)$ episodes of offline data in the finite-horizon stationary transition setting, where $H$ is the horizon length and $d_m$ is the minimal marginal state-action distribution induced by the behavior policy.
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
The problem of Offline Policy Evaluation (OPE) in Reinforcement Learning (RL) is a critical step towards applying RL in real-life applications.
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning
We consider the problem of off-policy evaluation for reinforcement learning, where the goal is to estimate the expected reward of a target policy $\pi$ using offline data collected by running a logging policy $\mu$.
Shared Generative Latent Representation Learning for Multi-view Clustering
Clustering multi-view data has been a fundamental research topic in the computer vision community.
Low-rank Multi-view Clustering in Third-Order Tensor Space
Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace.
Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices
Due to its promising classification performance, sparse representation based classification(SRC) algorithm has attracted great attention in the past few years.
Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds
Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks.
Supervised learning of sparse context reconstruction coefficients for data representation and classification
Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.
Representing data by sparse combination of contextual data points for classification
In this objective, the reconstruction error is minimized and the coefficient spar- sity is encouraged.
