Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN2.9k papers
- Centre National de la Recherche ScientifiqueFR2.7k papers
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT2.6k papers
- Institute of High Energy PhysicsCN2.6k papers
- University of Science and Technology of ChinaCN2.5k papers
- Peking UniversityCN2.4k papers
- University of Chinese Academy of SciencesCN2.3k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT2.3k papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR2.3k papers
- Rutherford Appleton LaboratoryGB2.2k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di GenovaIT2.2k papers
- European Organization for Nuclear ResearchCH2.2k papers
124 papers · 2 filters
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Qingyi Pan, Wenbo Hu, Jun Zhu
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to impro…
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Han Zhang, Wenhao Zheng, Charley Chen +4
Since the label collecting is prohibitive and time-consuming, unsupervised methods are preferred in applications such as fraud detection. Meanwhile, such applications usually requi…
Weighting-Based Treatment Effect Estimation via Distribution Learning
Dongcheng Zhang, Kunpeng Zhang
Existing weighting methods for treatment effect estimation are often built upon the idea of propensity scores or covariate balance. They usually impose strong assumptions on treatm…
Decentralized Federated Learning via Mutual Knowledge Transfer
Chengxi Li, Gang Li, Pramod K. Varshney
In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for…
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Zhiyuan Li, Yuping Luo, Kaifeng Lyu
Matrix factorization is a simple and natural test-bed to investigate the implicit regularization of gradient descent. Gunasekar et al. (2017) conjectured that Gradient Flow with in…
A One-Size-Fits-All Solution to Conservative Bandit Problems
Yihan Du, Siwei Wang, Longbo Huang
In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i.e., the learner's reward performance must be at least as well as a gi…
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
Nan Tang, Ju Fan, Fangyi Li +5
Can AI help automate human-easy but computer-hard data preparation tasks that burden data scientists, practitioners, and crowd workers? We answer this question by presenting RPT, a…
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis
Weijie He, Xiaohao Mao, Chao Ma +3
A growing number of people are seeking healthcare advice online. Usually, they diagnose their medical conditions based on the symptoms they are experiencing, which is also known as…
Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning
Haoyi Fan, Fengbin Zhang, Yue Gao
Self-supervised learning achieves superior performance in many domains by extracting useful representations from the unlabeled data. However, most of traditional self-supervised me…
Fully Gap-Dependent Bounds for Multinomial Logit Bandit
Jiaqi Yang
We study the multinomial logit (MNL) bandit problem, where at each time step, the seller offers an assortment of size at most from a pool of items, and the buyer purchases…
Bi-tuning of Pre-trained Representations
Jincheng Zhong, Ximei Wang, Zhi Kou +2
It is common within the deep learning community to first pre-train a deep neural network from a large-scale dataset and then fine-tune the pre-trained model to a specific downstrea…
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin +3
Spiking neural networks (SNNs) based on Leaky Integrate and Fire (LIF) model have been applied to energy-efficient temporal and spatiotemporal processing tasks. Thanks to the bio-p…
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits
Siwei Wang, Longbo Huang, John C. S. Lui
We study the online restless bandit problem, where the state of each arm evolves according to a Markov chain, and the reward of pulling an arm depends on both the pulled arm and th…
On Self-Distilling Graph Neural Network
Yuzhao Chen, Yatao Bian, Xi Xiao +3
Recently, the teacher-student knowledge distillation framework has demonstrated its potential in training Graph Neural Networks (GNNs). However, due to the difficulty of training o…
Latent Causal Invariant Model
Xinwei Sun, Botong Wu, Xiangyu Zheng +4
Current supervised learning can learn spurious correlation during the data-fitting process, imposing issues regarding interpretability, out-of-distribution (OOD) generalization, an…
Remarks on Optimal Scores for Speaker Recognition
Dong Wang
In this article, we first establish the theory of optimal scores for speaker recognition. Our analysis shows that the minimum Bayes risk (MBR) decisions for both the speaker identi…
Off-Policy Interval Estimation with Lipschitz Value Iteration
Ziyang Tang, Yihao Feng, Na Zhang +2
Off-policy evaluation provides an essential tool for evaluating the effects of different policies or treatments using only observed data. When applied to high-stakes scenarios such…
Fighting Copycat Agents in Behavioral Cloning from Observation Histories
Chuan Wen, Jierui Lin, Trevor Darrell +2
Imitation learning trains policies to map from input observations to the actions that an expert would choose. In this setting, distribution shift frequently exacerbates the effect…
Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Guangxiang Zhu, Minghao Zhang, Honglak Lee +1
Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by…
Nearly Minimax Optimal Reward-free Reinforcement Learning
Zihan Zhang, Simon S. Du, Xiangyang Ji
We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple rewar…
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu, Jing Lu, Xiwei Zhao +8
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest…
Imitation with Neural Density Models
Kuno Kim, Akshat Jindal, Yang Song +3
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) us…
Interpreting Deep Learning Model Using Rule-based Method
Xiaojian Wang, Jingyuan Wang, Ke Tang
Deep learning models are favored in many research and industry areas and have reached the accuracy of approximating or even surpassing human level. However they've long been consid…
Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classification
Min Qian, Yan-Fu Li
With the abundance of industrial datasets, imbalanced classification has become a common problem in several application domains. Oversampling is an effective method to solve imbala…