Le Song

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Ant Financial ∙ Georgia Institute of Technology ∙ Carnegie Mellon University ∙ Intel

Associate Director, Center for Machine Learning, Associate Professor at Georgia Institute of Technology

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 ∙ share

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Cost-Effective 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 ∙ share

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Retrosynthesis Prediction with Conditional Graph Logic Network

Retrosynthesis is one of the fundamental problems in organic chemistry. ...
01/06/2020 ∙ by Hanjun Dai, et al. ∙ 6 ∙ share

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Kernel Exponential Family Estimation via Doubly Dual Embedding

We investigate penalized maximum log-likelihood estimation for exponenti...
11/06/2018 ∙ by Bo Dai, et al. ∙ 4 ∙ share

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

The problem of cross-platform binary code similarity detection aims at d...
08/22/2017 ∙ by Xiaojun Xu, et al. ∙ 0 ∙ share

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Deep Hyperspherical Learning

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

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Towards Black-box Iterative Machine Teaching

In this paper, we make an important step towards the black-box machine t...
10/21/2017 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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Iterative Machine Teaching

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

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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 ∙ share

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Learning Combinatorial Optimization Algorithms over Graphs

The design of good heuristics or approximation algorithms for NP-hard co...
04/05/2017 ∙ by Hanjun Dai, et al. ∙ 0 ∙ share

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Deep Semi-Random Features for Nonlinear Function Approximation

We propose semi-random features for nonlinear function approximation. Th...
02/28/2017 ∙ by Kenji Kawaguchi, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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

We present a novel distribution-free approach, the data-driven threshold...
10/14/2016 ∙ by Shuang Li, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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Stochastic Generative Hashing

Learning-based binary hashing has become a powerful paradigm for fast se...
01/11/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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Online Supervised Subspace Tracking

We present a framework for supervised subspace tracking, when there are ...
09/01/2015 ∙ by Yao Xie, et al. ∙ 0 ∙ share

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

Information diffusion in online social networks is affected by the under...
07/08/2015 ∙ by Mehrdad Farajtabar, et al. ∙ 0 ∙ share

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Scan B-Statistic for Kernel Change-Point Detection

Detecting the emergence of an abrupt change-point is a classic problem i...
07/05/2015 ∙ by Shuang Li, et al. ∙ 0 ∙ share

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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 ∙ share

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Deep Fried Convnets

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

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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 ∙ share

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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 ∙ share

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Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm

Information spreads across social and technological networks, but often ...
05/12/2014 ∙ by Hadi Daneshmand, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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Nonparametric Estimation of Multi-View Latent Variable Models

Spectral methods have greatly advanced the estimation of latent variable...
11/13/2013 ∙ by Le Song, et al. ∙ 0 ∙ share

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Least Squares Revisited: Scalable Approaches for Multi-class Prediction

This work provides simple algorithms for multi-class (and multi-label) p...
10/07/2013 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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Kernel Bayes' rule

A nonparametric kernel-based method for realizing Bayes' rule is propose...
09/29/2010 ∙ by Kenji Fukumizu, et al. ∙ 0 ∙ share

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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 ∙ share

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Know-Evolve: 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 ∙ share

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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 ∙ share

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Smoothed Dual Embedding Control

We revisit the Bellman optimality equation with Nesterov's smoothing tec...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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Boosting the Actor with Dual Critic

This paper proposes a new actor-critic-style algorithm called Dual Actor...
12/29/2017 ∙ by Bo Dai, et al. ∙ 0 ∙ share

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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 ∙ share

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Learning to Explain: An Information-Theoretic Perspective on Model Interpretation

We introduce instancewise feature selection as a methodology for model i...
02/21/2018 ∙ by Jianbo Chen, et al. ∙ 0 ∙ share

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Syntax-Directed Variational Autoencoder for Structured Data

Deep generative models have been enjoying success in modeling continuous...
02/24/2018 ∙ by Hanjun Dai, et al. ∙ 0 ∙ share

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Iterative Learning with Open-set Noisy Labels

Large-scale datasets possessing clean label annotations are crucial for ...
03/31/2018 ∙ by Yisen Wang, et al. ∙ 0 ∙ share

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Decoupled Networks

Inner product-based convolution has been a central component of convolut...
04/22/2018 ∙ by Weiyang Liu, et al. ∙ 0 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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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 ∙ share

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Le Song

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