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Found 37 papers, 4 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。”
Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole.
Offline Multitask Representation Learning for Reinforcement Learning
We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from different tasks that share a common representation and is asked to learn the shared representation.
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data.
Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making
Artificial Intelligence (AI) is increasingly employed in various decision-making tasks, typically as a Recommender, providing recommendations that the AI deems correct.
Poisoning Federated Recommender Systems with Fake Users
Current poisoning attacks on federated recommender systems often rely on additional information, such as the local training data of genuine users or item popularity.
Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making
AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance.
Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making
In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations
The collection and curation of high-quality training data is crucial for developing text classification models with superior performance, but it is often associated with significant costs and time investment.
Model-Free Algorithm with Improved Sample Efficiency for Zero-Sum Markov Games
In this work, we propose a model-free stage-based Q-learning algorithm and show that it achieves the same sample complexity as the best model-based algorithm, and hence for the first time demonstrate that model-free algorithms can enjoy the same optimality in the $H$ dependence as model-based algorithms.
Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
Developing theoretical guarantees on the sample complexity of offline RL methods is an important step towards making data-hungry RL algorithms practically viable.
Non-stationary Reinforcement Learning under General Function Approximation
To the best of our knowledge, this is the first dynamic regret analysis in non-stationary MDPs with general function approximation.
TheoremQA: A Theorem-driven Question Answering dataset
We evaluate a wide spectrum of 16 large language and code models with different prompting strategies like Chain-of-Thoughts and Program-of-Thoughts.
Ranked #1 on
Natural Questions
on TheoremQA
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections
Experts across diverse disciplines are often interested in making sense of large text collections.
No-Regret Linear Bandits beyond Realizability
It achieves a near-optimal $\sqrt{T}$ regret for problems that the best-known regret is almost linear in time horizon $T$.
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs
In many real-life reinforcement learning (RL) problems, deploying new policies is costly.
Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making
To mitigate this gap, we proposed to promote humans' appropriate trust based on the CL of both sides at a task-instance level.
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning.
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
To the best of our knowledge, these are the first $\tilde{\mathcal{O}}(\frac{1}{K})$ bound and absolute zero sub-optimality bound respectively for offline RL with linear function approximation from adaptive data with partial coverage.
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
Offline reinforcement learning, which aims at optimizing sequential decision-making strategies with historical data, has been extensively applied in real-life applications.
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks
We propose a quasi neural network to approximate the distribution propagation, which is a neural network with continuous parameters and smooth activation function.
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality
Goal-oriented Reinforcement Learning, where the agent needs to reach the goal state while simultaneously minimizing the cost, has received significant attention in real-world applications.
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.
