
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
04/27/2019 ∙ by Bo Dai, et al. ∙ 28 ∙ shareread it

CostEffective Incentive Allocation via Structured Counterfactual Inference
We address a practical problem ubiquitous in modern industry, in which a...
02/07/2019 ∙ by Romain Lopez, et al. ∙ 8 ∙ shareread it

Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
11/06/2018 ∙ by Bo Dai, et al. ∙ 4 ∙ shareread it

Neural Networkbased Graph Embedding for CrossPlatform Binary Code Similarity Detection
The problem of crossplatform binary code similarity detection aims at d...
08/22/2017 ∙ by Xiaojun Xu, et al. ∙ 0 ∙ shareread it

Deep Hyperspherical Learning
Convolution as inner product has been the founding basis of convolutiona...
11/08/2017 ∙ by Weiyang Liu, et al. ∙ 0 ∙ shareread it

Towards Blackbox Iterative Machine Teaching
In this paper, we make an important step towards the blackbox machine t...
10/21/2017 ∙ by Weiyang Liu, et al. ∙ 0 ∙ shareread it

Iterative Machine Teaching
In this paper, we consider the problem of machine teaching, the inverse ...
05/30/2017 ∙ by Weiyang Liu, et al. ∙ 0 ∙ shareread it

Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous seque...
05/23/2017 ∙ by Shuai Xiao, et al. ∙ 0 ∙ shareread it

Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NPhard co...
04/05/2017 ∙ by Hanjun Dai, et al. ∙ 0 ∙ shareread it

Deep SemiRandom Features for Nonlinear Function Approximation
We propose semirandom features for nonlinear function approximation. Th...
02/28/2017 ∙ by Kenji Kawaguchi, et al. ∙ 0 ∙ shareread it

Diverse Neural Network Learns True Target Functions
Neural networks are a powerful class of functions that can be trained wi...
11/09/2016 ∙ by Bo Xie, et al. ∙ 0 ∙ shareread it

Distilling Information Reliability and Source Trustworthiness from Digital Traces
Online knowledge repositories typically rely on their users or dedicated...
10/24/2016 ∙ by Behzad Tabibian, et al. ∙ 0 ∙ shareread it

DataDriven Threshold Machine: Scan Statistics, ChangePoint Detection, and Extreme Bandits
We present a novel distributionfree approach, the datadriven threshold...
10/14/2016 ∙ by Shuang Li, et al. ∙ 0 ∙ shareread it

Fast and Simple Optimization for Poisson Likelihood Models
Poisson likelihood models have been prevalently used in imaging, social ...
08/03/2016 ∙ by Niao He, et al. ∙ 0 ∙ shareread it

Learning from Conditional Distributions via Dual Embeddings
Many machine learning tasks, such as learning with invariance and policy...
07/15/2016 ∙ by Bo Dai, et al. ∙ 0 ∙ shareread it

Stochastic Generative Hashing
Learningbased binary hashing has become a powerful paradigm for fast se...
01/11/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ shareread it

Smart broadcasting: Do you want to be seen?
Many users in online social networks are constantly trying to gain atten...
05/22/2016 ∙ by Mohammad Reza Karimi, et al. ∙ 0 ∙ shareread it

Detecting weak changes in dynamic events over networks
Large volume of networked streaming event data are becoming increasingly...
03/29/2016 ∙ by Shuang Li, et al. ∙ 0 ∙ shareread it

Online Supervised Subspace Tracking
We present a framework for supervised subspace tracking, when there are ...
09/01/2015 ∙ by Yao Xie, et al. ∙ 0 ∙ shareread it

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Coevolution
Information diffusion in online social networks is affected by the under...
07/08/2015 ∙ by Mehrdad Farajtabar, et al. ∙ 0 ∙ shareread it

Scan BStatistic for Kernel ChangePoint Detection
Detecting the emergence of an abrupt changepoint is a classic problem i...
07/05/2015 ∙ by Shuang Li, et al. ∙ 0 ∙ shareread it

Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex ...
06/09/2015 ∙ by Bo Dai, et al. ∙ 0 ∙ shareread it

Deep Fried Convnets
The fully connected layers of a deep convolutional neural network typica...
12/22/2014 ∙ by Zichao Yang, et al. ∙ 0 ∙ shareread it

A la Carte  Learning Fast Kernels
Kernel methods have great promise for learning rich statistical represen...
12/19/2014 ∙ by Zichao Yang, et al. ∙ 0 ∙ shareread it

Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
07/21/2014 ∙ by Bo Dai, et al. ∙ 0 ∙ shareread it

Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Softthresholding Algorithm
Information spreads across social and technological networks, but often ...
05/12/2014 ∙ by Hadi Daneshmand, et al. ∙ 0 ∙ shareread it

Nonparametric Latent Tree Graphical Models: Inference, Estimation, and Structure Learning
Tree structured graphical models are powerful at expressing long range o...
01/16/2014 ∙ by Le Song, et al. ∙ 0 ∙ shareread it

Budgeted Influence Maximization for Multiple Products
The typical algorithmic problem in viral marketing aims to identify a se...
12/08/2013 ∙ by Nan Du, et al. ∙ 0 ∙ shareread it

Nonparametric Estimation of MultiView Latent Variable Models
Spectral methods have greatly advanced the estimation of latent variable...
11/13/2013 ∙ by Le Song, et al. ∙ 0 ∙ shareread it

Least Squares Revisited: Scalable Approaches for Multiclass Prediction
This work provides simple algorithms for multiclass (and multilabel) p...
10/07/2013 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ shareread it

A Spectral Algorithm for Latent Junction Trees
Latent variable models are an elegant framework for capturing rich proba...
10/16/2012 ∙ by Ankur P. Parikh, et al. ∙ 0 ∙ shareread it

Unfolding Latent Tree Structures using 4th Order Tensors
Discovering the latent structure from many observed variables is an impo...
10/03/2012 ∙ by Mariya Ishteva, et al. ∙ 0 ∙ shareread it

Spectral Methods for Learning Multivariate Latent Tree Structure
This work considers the problem of learning the structure of multivariat...
07/07/2011 ∙ by Kamalika Chaudhuri, et al. ∙ 0 ∙ shareread it

Infinite Hierarchical MMSB Model for Nested Communities/Groups in Social Networks
Actors in realistic social networks play not one but a number of diverse...
10/09/2010 ∙ by Qirong Ho, et al. ∙ 0 ∙ shareread it

Kernel Bayes' rule
A nonparametric kernelbased method for realizing Bayes' rule is propose...
09/29/2010 ∙ by Kenji Fukumizu, et al. ∙ 0 ∙ shareread it

Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question ans...
09/12/2017 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ shareread it

KnowEvolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The availability of large scale event data with time stamps has given ri...
05/16/2017 ∙ by Rakshit Trivedi, et al. ∙ 0 ∙ shareread it

On the Complexity of Learning Neural Networks
The stunning empirical successes of neural networks currently lack rigor...
07/14/2017 ∙ by Le Song, et al. ∙ 0 ∙ shareread it

Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ shareread it

Boosting the Actor with Dual Critic
This paper proposes a new actorcriticstyle algorithm called Dual Actor...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ shareread it

GeniePath: Graph Neural Networks with Adaptive Receptive Paths
We present, GeniePath, a scalable approach for learning adaptive recepti...
02/03/2018 ∙ by Ziqi Liu, et al. ∙ 0 ∙ shareread it

Learning to Explain: An InformationTheoretic Perspective on Model Interpretation
We introduce instancewise feature selection as a methodology for model i...
02/21/2018 ∙ by Jianbo Chen, et al. ∙ 0 ∙ shareread it

SyntaxDirected Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous...
02/24/2018 ∙ by Hanjun Dai, et al. ∙ 0 ∙ shareread it

Iterative Learning with Openset Noisy Labels
Largescale datasets possessing clean label annotations are crucial for ...
03/31/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ shareread it

Decoupled Networks
Inner productbased convolution has been a central component of convolut...
04/22/2018 ∙ by Weiyang Liu, et al. ∙ 0 ∙ shareread it

Learning to Optimize via Wasserstein Deep Inverse Optimal Control
We study the inverse optimal control problem in social sciences: we aim ...
05/22/2018 ∙ by Yichen Wang, et al. ∙ 0 ∙ shareread it

Learning towards Minimum Hyperspherical Energy
Neural networks are a powerful class of nonlinear functions that can be ...
05/23/2018 ∙ by Weiyang Liu, et al. ∙ 0 ∙ shareread it

KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question ...
05/31/2018 ∙ by Yuyu Zhang, et al. ∙ 0 ∙ shareread it

Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various ...
06/06/2018 ∙ by Hanjun Dai, et al. ∙ 0 ∙ shareread it

Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
07/22/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ shareread it
Le Song
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Associate Director, Center for Machine Learning, Associate Professor at Georgia Institute of Technology