Search Results for author: Le Song

Found 145 papers, 51 papers with code

Temporal Logic Point Processes

no code implementations ICML 2020 Shuang Li, Lu Wang, Ruizhi Zhang, xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, Le Song

We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.

Point Processes

Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

1 code implementation ACL 2022 Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

1 code implementation18 Mar 2022 Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.

Molecule Generation for Drug Design: a Graph Learning Perspective

no code implementations18 Feb 2022 Nianzu Yang, Huaijin Wu, Junchi Yan, Xiaoyong Pan, Ye Yuan, Le Song

From the application perspective, one of the emerging and attractive areas is aiding the design and discovery of molecules, especially in drug industry.

Graph Learning

Learning Temporal Rules from Noisy Timeseries Data

no code implementations11 Feb 2022 Karan Samel, Zelin Zhao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan Essa, Le Song

Events across a timeline are a common data representation, seen in different temporal modalities.

Locality Sensitive Teaching

no code implementations NeurIPS 2021 Zhaozhuo Xu, Beidi Chen, Chaojian Li, Weiyang Liu, Le Song, Yingyan Lin, Anshumali Shrivastava

However, as one of the most influential and practical MT paradigms, iterative machine teaching (IMT) is prohibited on IoT devices due to its inefficient and unscalable algorithms.

Multi-task Learning of Order-Consistent Causal Graphs

no code implementations NeurIPS 2021 Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song

We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.

Multi-Task Learning

RoMA: Robust Model Adaptation for Offline Model-based Optimization

no code implementations NeurIPS 2021 Sihyun Yu, Sungsoo Ahn, Le Song, Jinwoo Shin

We consider the problem of searching an input maximizing a black-box objective function given a static dataset of input-output queries.

GNN is a Counter? Revisiting GNN for Question Answering

no code implementations ICLR 2022 Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, Tao Qin

To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability.

Knowledge Graphs Question Answering

ProTo: Program-Guided Transformer for Program-Guided Tasks

1 code implementation NeurIPS 2021 Zelin Zhao, Karan Samel, Binghong Chen, Le Song

Furthermore, we propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program by leveraging cross-attention and masked self-attention to pass messages between the specification and routines in the program.

Decision Making Learning to Execute +2

Spanning Tree-based Graph Generation for Molecules

no code implementations ICLR 2022 Sungsoo Ahn, Binghong Chen, Tianzhe Wang, Le Song

In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.

Graph Generation Molecular Graph Generation

Neural Temporal Logic Programming

no code implementations29 Sep 2021 Karan Samel, Zelin Zhao, Binghong Chen, Shuang Li, Dharmashankar Subramanian, Irfan Essa, Le Song

Events across a timeline are a common data representation, seen in different temporal modalities.

Provable Learning-based Algorithm For Sparse Recovery

no code implementations ICLR 2022 Xinshi Chen, Haoran Sun, Le Song

In this work, we propose PLISA (Provable Learning-based Iterative Sparse recovery Algorithm) to learn algorithms automatically from data.

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

1 code implementation ACL 2021 Yinghao Li, Pranav Shetty, Lucas Liu, Chao Zhang, Le Song

To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way.

Named Entity Recognition NER +1

TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

no code implementations17 May 2021 Lu Wang, xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei zhang, Xiaofeng He, Le Song, Jingren Zhou, Hongxia Yang

Secondly, on top of the proposed graph transformer, we introduce a two-stream encoder that separately extracts representations from temporal neighborhoods associated with the two interaction nodes and then utilizes a co-attentional transformer to model inter-dependencies at a semantic level.

Contrastive Learning Graph Learning +2

Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients

no code implementations25 Apr 2021 Yuyu Zhang, Heng Chi, Binghong Chen, Tsz Ling Elaine Tang, Lucia Mirabella, Le Song, Glaucio H. Paulino

We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems.

How to Design Sample and Computationally Efficient VQA Models

no code implementations22 Mar 2021 Karan Samel, Zelin Zhao, Binghong Chen, Kuan Wang, Robin Luo, Le Song

In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested.

Question Answering Visual Question Answering +1

Concentric Spherical GNN for 3D Representation Learning

no code implementations18 Mar 2021 James Fox, Bo Zhao, Sivasankaran Rajamanickam, Rampi Ramprasad, Le Song

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.

3D Classification Representation Learning

A Biased Graph Neural Network Sampler with Near-Optimal Regret

1 code implementation NeurIPS 2021 Qingru Zhang, David Wipf, Quan Gan, Le Song

Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data.

Improving Learning to Branch via Reinforcement Learning

no code implementations NeurIPS Workshop LMCA 2020 Haoran Sun, Wenbo Chen, Hui Li, Le Song

Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP).

reinforcement-learning

Learning Two-Time-Scale Representations For Large Scale Recommendations

no code implementations1 Jan 2021 Xinshi Chen, Yan Zhu, Haowen Xu, Muhan Zhang, Liang Xiong, Le Song

We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.

A Framework For Differentiable Discovery Of Graph Algorithms

no code implementations NeurIPS Workshop LMCA 2020 Hanjun Dai, Xinshi Chen, Yu Li, Xin Gao, Le Song

Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms.

Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA

no code implementations1 Jan 2021 Karan Samel, Zelin Zhao, Kuan Wang, Robin Luo, Binghong Chen, Le Song

We present a differentiable end-to-end program executor (DePe), which addresses Visual Question Answering (VQA) in a sample and computationally efficient manner.

Question Answering Visual Question Answering +1

Molecule Optimization by Explainable Evolution

no code implementations ICLR 2021 Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.

Drug Discovery

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

no code implementations1 Jan 2021 Binghong Chen, Chengtao Li, Hanjun Dai, Rampi Ramprasad, Le Song

We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.

Domain Adaptation

Understanding Deep Architecture with Reasoning Layer

1 code implementation NeurIPS 2020 Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Recently, there is a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models

no code implementations NeurIPS 2020 Yingxiang Yang, Negar Kiyavash, Le Song, Niao He

Macroscopic data aggregated from microscopic events are pervasive in machine learning, such as country-level COVID-19 infection statistics based on city-level data.

Stochastic Optimization

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

no code implementations4 Nov 2020 Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will R. Gutekunst, Le Song, Rampi Ramprasad

The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives.

GPR

Question Directed Graph Attention Network for Numerical Reasoning over Text

no code implementations EMNLP 2020 Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.

Graph Attention Machine Reading Comprehension +2

Answering Any-hop Open-domain Questions with Iterative Document Reranking

no code implementations16 Sep 2020 Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song

Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.

Multi-hop Question Answering Open-Domain Question Answering

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

1 code implementation ICML 2020 Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product.

Understanding Deep Architectures with Reasoning Layer

1 code implementation24 Jun 2020 Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.

Bandit Samplers for Training Graph Neural Networks

2 code implementations NeurIPS 2020 Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi

However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).

Graph Attention

Learning to Stop While Learning to Predict

1 code implementation ICML 2020 Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song

Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already.

Meta-Learning

Variational Policy Propagation for Multi-agent Reinforcement Learning

no code implementations19 Apr 2020 Chao Qu, Hui Li, Chang Liu, Junwu Xiong, James Zhang, Wei Chu, Weiqiang Wang, Yuan Qi, Le Song

We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

Orthogonal Over-Parameterized Training

1 code implementation CVPR 2021 Weiyang Liu, Rongmei Lin, Zhen Liu, James M. Rehg, Liam Paull, Li Xiong, Le Song, Adrian Weller

The inductive bias of a neural network is largely determined by the architecture and the training algorithm.

DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

no code implementations28 Feb 2020 Yuyu Zhang, Ping Nie, Xiubo Geng, Arun Ramamurthy, Le Song, Daxin Jiang

Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.

Open-Domain Question Answering

Heterogeneous Graph Neural Networks for Malicious Account Detection

1 code implementation27 Feb 2020 Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song

We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.

RNA Secondary Structure Prediction By Learning Unrolled Algorithms

1 code implementation ICLR 2020 Xinshi Chen, Yu Li, Ramzan Umarov, Xin Gao, Le Song

The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints.

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

1 code implementation ICLR 2020 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.

Variational Inference

Neural Similarity Learning

1 code implementation NeurIPS 2019 Weiyang Liu, Zhen Liu, James M. Rehg, Le Song

By generalizing inner product with a bilinear matrix, we propose the neural similarity which serves as a learnable parametric similarity measure for CNNs.

Few-Shot Learning

Learn to Explain Efficiently via Neural Logic Inductive Learning

1 code implementation ICLR 2020 Yuan Yang, Le Song

The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.

Inductive logic programming

Deep Interaction Processes for Time-Evolving Graphs

no code implementations25 Sep 2019 xiaofu Chang, Jianfeng Wen, Xuqin Liu, Yanming Fang, Le Song, Yuan Qi

To model the dependency between latent dynamic representations of each node, we define a mixture of temporal cascades in which a node's neural representation depends on not only this node's previous representations but also the previous representations of related nodes that have interacted with this node.

Language Modeling with Shared Grammar

no code implementations ACL 2019 Yuyu Zhang, Le Song

Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language.

Language Modelling

Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction

no code implementations23 Jun 2019 Jian-Ya Ding, Chao Zhang, Lei Shen, Shengyin Li, Bing Wang, Yinghui Xu, Le Song

In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients.

Combinatorial Optimization

Regularizing Neural Networks via Minimizing Hyperspherical Energy

1 code implementation CVPR 2020 Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M. Rehg, Li Xiong, Le Song

Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.

Can Graph Neural Networks Help Logic Reasoning?

no code implementations5 Jun 2019 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.

GLAD: Learning Sparse Graph Recovery

1 code implementation ICLR 2020 Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, Han Liu, Le Song

Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.

Learning a Meta-Solver for Syntax-Guided Program Synthesis

no code implementations ICLR 2019 Xujie Si, Yuan Yang, Hanjun Dai, Mayur Naik, Le Song

Our framework consists of three components: 1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network; 2) a grammar adaptive policy network which enables learning a transferable policy; and 3) a reinforcement learning algorithm that jointly trains the specification and grammar embedding and adaptive policy.

Meta-Learning Program Synthesis

Exponential Family Estimation via Adversarial Dynamics Embedding

1 code implementation NeurIPS 2019 Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks.

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

1 code implementation ICLR 2020 Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

no code implementations7 Feb 2019 Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael. I. Jordan, Yuan Qi, Le Song

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback.

Counterfactual Inference Domain Adaptation

Particle Flow Bayes' Rule

2 code implementations2 Feb 2019 Xinshi Chen, Hanjun Dai, Le Song

We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation.

Bayesian Inference Meta-Learning +1

Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

no code implementations NeurIPS 2019 Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong

To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.

Multi-agent Reinforcement Learning reinforcement-learning

Double Neural Counterfactual Regret Minimization

no code implementations ICLR 2020 Hui Li, Kailiang Hu, Zhibang Ge, Tao Jiang, Yuan Qi, Le Song

Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG).

Meta Architecture Search

1 code implementation NeurIPS 2019 Albert Shaw, Wei Wei, Weiyang Liu, Le Song, Bo Dai

Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks.

Bayesian Inference Few-Shot Learning +1

Coupled Variational Bayes via Optimization Embedding

1 code implementation NeurIPS 2018 Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.

Variational Inference

Reinforcement Learning for Uplift Modeling

1 code implementation26 Nov 2018 Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong

Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.

reinforcement-learning

Learning Temporal Point Processes via Reinforcement Learning

no code implementations NeurIPS 2018 Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time.

Point Processes reinforcement-learning

Kernel Exponential Family Estimation via Doubly Dual Embedding

1 code implementation6 Nov 2018 Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

We investigate penalized maximum log-likelihood estimation for exponential family distributions whose natural parameter resides in a reproducing kernel Hilbert space.

Characterizing Malicious Edges targeting on Graph Neural Networks

no code implementations27 Sep 2018 Xiaojun Xu, Yue Yu, Bo Li, Le Song, Chengfeng Liu, Carl Gunter

Extensive experiments are conducted to show that the proposed detection mechanism can achieve AUC above 90% against the two attack strategies on both Cora and Citeseer datasets.

Graph Generation Link Prediction +1

Latent Dirichlet Allocation for Internet Price War

no code implementations23 Aug 2018 Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong

Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.

Learning Steady-States of Iterative Algorithms over Graphs

no code implementations ICML 2018 Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, Le Song

Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steady-state conditions.

Learning towards Minimum Hyperspherical Energy

4 code implementations NeurIPS 2018 Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.

Learning to Optimize via Wasserstein Deep Inverse Optimal Control

no code implementations22 May 2018 Yichen Wang, Le Song, Hongyuan Zha

We first propose a unified KL framework that generalizes existing maximum entropy inverse optimal control methods.

Recommendation Systems reinforcement-learning

Decoupled Networks

1 code implementation CVPR 2018 Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song

Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.

Iterative Learning with Open-set Noisy Labels

1 code implementation CVPR 2018 Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia

We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions.

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

3 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

no code implementations ICML 2018 Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.

Q-Learning reinforcement-learning

Boosting the Actor with Dual Critic

no code implementations ICLR 2018 Bo Dai, Albert Shaw, Niao He, Lihong Li, Le Song

This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.

Predicting User Activity Level In Point Processes With Mass Transport Equation

no code implementations NeurIPS 2017 Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song

Point processes are powerful tools to model user activities and have a plethora of applications in social sciences.

Point Processes

Deep Hyperspherical Learning

no code implementations NeurIPS 2017 Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song

In light of such challenges, we propose hyperspherical convolution (SphereConv), a novel learning framework that gives angular representations on hyperspheres.

Representation Learning

Stochastic Training of Graph Convolutional Networks with Variance Reduction

2 code implementations ICML 2018 Jianfei Chen, Jun Zhu, Le Song

Previous attempts on reducing the receptive field size by subsampling neighbors do not have a convergence guarantee, and their receptive field size per node is still in the order of hundreds.

Towards Black-box Iterative Machine Teaching

no code implementations ICML 2018 Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song

We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.

Variational Reasoning for Question Answering with Knowledge Graph

1 code implementation12 Sep 2017 Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song

Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.

Knowledge Graphs Question Answering +1

Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

1 code implementation22 Aug 2017 Xiaojun Xu, Chang Liu, Qian Feng, Heng Yin, Le Song, Dawn Song

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.

Graph Embedding Graph Matching +1

On the Complexity of Learning Neural Networks

no code implementations NeurIPS 2017 Le Song, Santosh Vempala, John Wilmes, Bo Xie

Moreover, this hard family of functions is realizable with a small (sublinear in dimension) number of activation units in the single hidden layer.

Iterative Machine Teaching

2 code implementations ICML 2017 Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, Le Song

Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.

Wasserstein Learning of Deep Generative Point Process Models

1 code implementation NeurIPS 2017 Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, Hongyuan Zha

Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena.

Point Processes

Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs

2 code implementations ICML 2017 Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song

The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.

Entity Embeddings Knowledge Graphs +1

SphereFace: Deep Hypersphere Embedding for Face Recognition

14 code implementations CVPR 2017 Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.

Face Identification Face Recognition +1

Learning Combinatorial Optimization Algorithms over Graphs

8 code implementations NeurIPS 2017 Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

Combinatorial Optimization Graph Embedding

Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks

no code implementations24 Mar 2017 Shuai Xiao, Junchi Yan, Mehrdad Farajtabar, Le Song, Xiaokang Yang, Hongyuan Zha

A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied.

Point Processes Time Series

Fake News Mitigation via Point Process Based Intervention

no code implementations ICML 2017 Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha

We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.

reinforcement-learning

Deep Semi-Random Features for Nonlinear Function Approximation

1 code implementation28 Feb 2017 Kenji Kawaguchi, Bo Xie, Vikas Verma, Le Song

For deep models, with no unrealistic assumptions, we prove universal approximation ability, a lower bound on approximation error, a partial optimization guarantee, and a generalization bound.

Stochastic Generative Hashing

2 code implementations ICML 2017 Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song

Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases.

Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks

no code implementations8 Dec 2016 Nan Du, YIngyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song

A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time.

GRAM: Graph-based Attention Model for Healthcare Representation Learning

1 code implementation21 Nov 2016 Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun

-Interpretation:The representations learned by deep learning methods should align with medical knowledge.

Representation Learning

Diverse Neural Network Learns True Target Functions

no code implementations9 Nov 2016 Bo Xie, YIngyu Liang, Le Song

In this paper, we answer these questions by analyzing one-hidden-layer neural networks with ReLU activation, and show that despite the non-convexity, neural networks with diverse units have no spurious local minima.

Relation Linking

Distilling Information Reliability and Source Trustworthiness from Digital Traces

no code implementations24 Oct 2016 Behzad Tabibian, Isabel Valera, Mehrdad Farajtabar, Le Song, Bernhard Schölkopf, Manuel Gomez-Rodriguez

Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.

Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits

no code implementations14 Oct 2016 Shuang Li, Yao Xie, Le Song

We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given pre-specified level that bounds the tail probability of the maximum of a (possibly dependent but stationary) random sequence.

Change Point Detection

Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

no code implementations13 Sep 2016 Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song

DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time.

Activity Prediction Network Embedding +2

Fast and Simple Optimization for Poisson Likelihood Models

no code implementations3 Aug 2016 Niao He, Zaid Harchaoui, Yichen Wang, Le Song

Since almost all gradient-based optimization algorithms rely on Lipschitz-continuity, optimizing Poisson likelihood models with a guarantee of convergence can be challenging, especially for large-scale problems.

Time Series Time Series Analysis

Learning from Conditional Distributions via Dual Embeddings

no code implementations15 Jul 2016 Bo Dai, Niao He, Yunpeng Pan, Byron Boots, Le Song

In such problems, each sample $x$ itself is associated with a conditional distribution $p(z|x)$ represented by samples $\{z_i\}_{i=1}^M$, and the goal is to learn a function $f$ that links these conditional distributions to target values $y$.

Smart broadcasting: Do you want to be seen?

no code implementations22 May 2016 Mohammad Reza Karimi, Erfan Tavakoli, Mehrdad Farajtabar, Le Song, Manuel Gomez-Rodriguez

Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them.

Point Processes

Detecting weak changes in dynamic events over networks

no code implementations29 Mar 2016 Shuang Li, Yao Xie, Mehrdad Farajtabar, Apurv Verma, Le Song

Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics.

Change Point Detection Point Processes

Discriminative Embeddings of Latent Variable Models for Structured Data

1 code implementation17 Mar 2016 Hanjun Dai, Bo Dai, Le Song

Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

no code implementations NeurIPS 2015 Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M. Rehg

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time.

Time-Sensitive Recommendation From Recurrent User Activities

no code implementations NeurIPS 2015 Nan Du, Yichen Wang, Niao He, Jimeng Sun, Le Song

By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services.

Point Processes Recommendation Systems

M-Statistic for Kernel Change-Point Detection

no code implementations NeurIPS 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning.

Change Point Detection

A Continuous-time Mutually-Exciting Point Process Framework for Prioritizing Events in Social Media

no code implementations13 Nov 2015 Mehrdad Farajtabar, Safoora Yousefi, Long Q. Tran, Le Song, Hongyuan Zha

In our experiments, we demonstrate that our algorithm is able to achieve the-state-of-the-art performance in terms of analyzing, predicting, and prioritizing events.

Online Supervised Subspace Tracking

no code implementations1 Sep 2015 Yao Xie, Ruiyang Song, Hanjun Dai, Qingbin Li, Le Song

The optimization problem for OSDR is non-convex and hard to analyze in general; we provide convergence analysis of OSDR in a simple linear regression setting.

Dimensionality Reduction Time Series

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

1 code implementation NeurIPS 2015 Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it.

Scan $B$-Statistic for Kernel Change-Point Detection

no code implementations5 Jul 2015 Shuang Li, Yao Xie, Hanjun Dai, Le Song

A novel theoretical result of the paper is the characterization of the tail probability of these statistics using the change-of-measure technique, which focuses on characterizing the tail of the detection statistics rather than obtaining its asymptotic distribution under the null distribution.

Change Point Detection

Provable Bayesian Inference via Particle Mirror Descent

no code implementations9 Jun 2015 Bo Dai, Niao He, Hanjun Dai, Le Song

Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters.

Bayesian Inference Gaussian Processes

Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients

no code implementations NeurIPS 2015 Bo Xie, YIngyu Liang, Le Song

We propose a simple, computationally efficient, and memory friendly algorithm based on the "doubly stochastic gradients" to scale up a range of kernel nonlinear component analysis, such as kernel PCA, CCA and SVD.

Communication Efficient Distributed Kernel Principal Component Analysis

no code implementations23 Mar 2015 Maria-Florina Balcan, YIngyu Liang, Le Song, David Woodruff, Bo Xie

Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality?

Deep Fried Convnets

1 code implementation ICCV 2015 Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Song, Ziyu Wang

The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters.

Image Classification

A la Carte - Learning Fast Kernels

no code implementations19 Dec 2014 Zichao Yang, Alexander J. Smola, Le Song, Andrew Gordon Wilson

Kernel methods have great promise for learning rich statistical representations of large modern datasets.

Shaping Social Activity by Incentivizing Users

no code implementations NeurIPS 2014 Mehrdad Farajtabar, Nan Du, Manuel Gomez Rodriguez, Isabel Valera, Hongyuan Zha, Le Song

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network.

Learning Time-Varying Coverage Functions

no code implementations NeurIPS 2014 Nan Du, YIngyu Liang, Maria-Florina F. Balcan, Le Song

Coverage functions are an important class of discrete functions that capture laws of diminishing returns.

Scalable Kernel Methods via Doubly Stochastic Gradients

1 code implementation NeurIPS 2014 Bo Dai, Bo Xie, Niao He, YIngyu Liang, Anant Raj, Maria-Florina Balcan, Le Song

The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems.

Active Learning and Best-Response Dynamics

no code implementations NeurIPS 2014 Maria-Florina Balcan, Chris Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song

We examine an important setting for engineered systems in which low-power distributed sensors are each making highly noisy measurements of some unknown target function.

Active Learning Denoising

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning

no code implementations16 Jan 2014 Le Song, Han Liu, Ankur Parikh, Eric Xing

Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.

Budgeted Influence Maximization for Multiple Products

no code implementations8 Dec 2013 Nan Du, YIngyu Liang, Maria Florina Balcan, Le Song

The typical algorithmic problem in viral marketing aims to identify a set of influential users in a social network, who, when convinced to adopt a product, shall influence other users in the network and trigger a large cascade of adoptions.

Combinatorial Optimization

Scalable Influence Estimation in Continuous-Time Diffusion Networks

no code implementations NeurIPS 2013 Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha

If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages?

Nonparametric Estimation of Multi-View Latent Variable Models

no code implementations13 Nov 2013 Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

We establish that the sample complexity for the proposed method is quadratic in the number of latent components and is a low order polynomial in the other relevant parameters.

Least Squares Revisited: Scalable Approaches for Multi-class Prediction

no code implementations7 Oct 2013 Alekh Agarwal, Sham M. Kakade, Nikos Karampatziakis, Le Song, Gregory Valiant

This work provides simple algorithms for multi-class (and multi-label) prediction in settings where both the number of examples n and the data dimension d are relatively large.

Learning Networks of Heterogeneous Influence

no code implementations NeurIPS 2012 Nan Du, Le Song, Ming Yuan, Alex J. Smola

However, the underlying transmission networks are often hidden and incomplete, and we observe only the time stamps when cascades of events happen.

Spectral Methods for Learning Multivariate Latent Tree Structure

no code implementations NeurIPS 2011 Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang

The setting is one where we only have samples from certain observed variables in the tree, and our goal is to estimate the tree structure (i. e., the graph of how the underlying hidden variables are connected to each other and to the observed variables).

Kernel Bayes' Rule

no code implementations NeurIPS 2011 Kenji Fukumizu, Le Song, Arthur Gretton

A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on kernel representations of probabilities in reproducing kernel Hilbert spaces.

Bayesian Inference

Kernel Embeddings of Latent Tree Graphical Models

no code implementations NeurIPS 2011 Le Song, Eric P. Xing, Ankur P. Parikh

Latent tree graphical models are natural tools for expressing long range and hierarchical dependencies among many variables which are common in computer vision, bioinformatics and natural language processing problems.

Sparsistent Learning of Varying-coefficient Models with Structural Changes

no code implementations NeurIPS 2009 Mladen Kolar, Le Song, Eric P. Xing

In this paper, we investigate sparsistent learning of a sub-family of this model --- piecewise constant VCVS models.

Model Selection

Time-Varying Dynamic Bayesian Networks

no code implementations NeurIPS 2009 Le Song, Mladen Kolar, Eric P. Xing

In this paper, we propose a time-varying dynamic Bayesian network (TV-DBN) for modeling the structurally varying directed dependency structures underlying non-stationary biological/neural time series.

Time Series

A state-space mixed membership blockmodel for dynamic network tomography

no code implementations31 Dec 2008 Eric P. Xing, Wenjie Fu, Le Song

In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes.

Kernelized Sorting

no code implementations NeurIPS 2008 Novi Quadrianto, Le Song, Alex J. Smola

Object matching is a fundamental operation in data analysis.

Kernel Measures of Independence for non-iid Data

no code implementations NeurIPS 2008 Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola

Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion.

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