b'Yingyu Liang'

research

∙ 01/31/2022

On the identifiability of mixtures of ranking models

Mixtures of ranking models are standard tools for ranking problems. Howe...

Xiaomin Zhang, et al. ∙

research

∙ 10/06/2021

An Analysis of Attentive Walk-Aggregating Graph Neural Networks

Graph neural networks (GNNs) have been shown to possess strong represent...

Mehmet F. Demirel, et al. ∙

research

∙ 10/21/2020

PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding

We look into the task of generalizing word embeddings: given a set of pr...

Zhao Jinman, et al. ∙

research

∙ 08/06/2020

Functional Regularization for Representation Learning: A Unified Theoretical Perspective

Unsupervised and self-supervised learning approaches have become a cruci...

Siddhant Garg, et al. ∙

research

∙ 08/04/2020

Can Adversarial Weight Perturbations Inject Neural Backdoors?

Adversarial machine learning has exposed several security hazards of neu...

Siddhant Garg, et al. ∙

research

∙ 07/10/2020

Learning Entangled Single-Sample Gaussians in the Subset-of-Signals Model

In the setting of entangled single-sample distributions, the goal is to ...

Yingyu Liang, et al. ∙

research

∙ 04/20/2020

Learning Entangled Single-Sample Distributions via Iterative Trimming

In the setting of entangled single-sample distributions, the goal is to ...

Hui Yuan, et al. ∙

research

∙ 04/12/2020

Gradients as Features for Deep Representation Learning

We address the challenging problem of deep representation learning–the e...

Fangzhou Mu, et al. ∙

research

∙ 04/10/2020

SimpleTran: Transferring Pre-Trained Sentence Embeddings for Low Resource Text Classification

Fine-tuning pre-trained sentence embedding models like BERT has become t...

Siddhant Garg, et al. ∙

research

∙ 02/23/2020

Sketching Transformed Matrices with Applications to Natural Language Processing

Suppose we are given a large matrix A=(a_i,j) that cannot be stored in m...

Yingyu Liang, et al. ∙

research

∙ 11/13/2019

Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

Multimodal language analysis often considers relationships between featu...

Zhongkai Sun, et al. ∙

research

∙ 08/16/2019

Shallow Domain Adaptive Embeddings for Sentiment Analysis

This paper proposes a way to improve the performance of existing algorit...

Prathusha K Sarma, et al. ∙

research

∙ 05/23/2019

Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models t...

Jiefeng Chen, et al. ∙

research

∙ 11/12/2018

Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

Neural networks have great success in many machine learning applications...

Zeyuan Allen-Zhu, et al. ∙

research

∙ 10/31/2018

Recovery Guarantees for Quadratic Tensors with Limited Observations

We consider the tensor completion problem of predicting the missing entr...

Hongyang Zhang, et al. ∙

research

∙ 09/12/2018

Generalizing Word Embeddings using Bag of Subwords

We approach the problem of generalizing pre-trained word embeddings beyo...

Jinman Zhao, et al. ∙

research

∙ 08/03/2018

Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

Neural networks have many successful applications, while much less theor...

Yuanzhi Li, et al. ∙

research

∙ 06/24/2018

N-Gram Graph, A Novel Molecule Representation

Virtual high-throughput screening provides a strategy for prioritizing c...

Shengchao Liu, et al. ∙

research

∙ 05/20/2018

Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gra...

Jiefeng Chen, et al. ∙

research

∙ 05/14/2018

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

Motivations like domain adaptation, transfer learning, and feature learn...

Mikhail Khodak, et al. ∙

research

∙ 05/11/2018

Domain Adapted Word Embeddings for Improved Sentiment Classification

Generic word embeddings are trained on large-scale generic corpora; Doma...

Prathusha K Sarma, et al. ∙

research

∙ 02/22/2018

Learning Mixtures of Linear Regressions with Nearly Optimal Complexity

Mixtures of Linear Regressions (MLR) is an important mixture model with ...

Yuanzhi Li, et al. ∙

research

∙ 06/13/2017

Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations

Non-negative matrix factorization is a basic tool for decomposing data i...

Yuanzhi Li, et al. ∙

research

∙ 04/27/2017

Optimal Sample Complexity for Matrix Completion and Related Problems via ℓ_2-Regularization

We study the strong duality of non-convex matrix factorization: we show ...

Maria-Florina Balcan, et al. ∙

research

∙ 03/02/2017

Generalization and Equilibrium in Generative Adversarial Nets (GANs)

We show that training of generative adversarial network (GAN) may not ha...

Sanjeev Arora, et al. ∙

research

∙ 11/11/2016

Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates

Non-negative matrix factorization is a popular tool for decomposing data...

Yuanzhi Li, et al. ∙

research

∙ 11/09/2016

Diverse Neural Network Learns True Target Functions

Neural networks are a powerful class of functions that can be trained wi...

Bo Xie, et al. ∙

research

∙ 10/13/2016

Mapping Between fMRI Responses to Movies and their Natural Language Annotations

Several research groups have shown how to correlate fMRI responses to th...

Kiran Vodrahalli, et al. ∙

research

∙ 02/06/2016

Recovery guarantee of weighted low-rank approximation via alternating minimization

Many applications require recovering a ground truth low-rank matrix from...

Yuanzhi Li, et al. ∙

research

∙ 01/14/2016

Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Word embeddings are ubiquitous in NLP and information retrieval, but it'...

Sanjeev Arora, et al. ∙

research

∙ 02/12/2015

RAND-WALK: A Latent Variable Model Approach to Word Embeddings

Semantic word embeddings represent the meaning of a word via a vector, a...

Sanjeev Arora, et al. ∙

research

∙ 07/21/2014

Scalable Kernel Methods via Doubly Stochastic Gradients

The general perception is that kernel methods are not scalable, and neur...

Bo Dai, et al. ∙

research

∙ 04/09/2014

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

Learning sparse combinations is a frequent theme in machine learning. In...

Aurélien Bellet, et al. ∙

research

∙ 12/08/2013

Budgeted Influence Maximization for Multiple Products

The typical algorithmic problem in viral marketing aims to identify a se...

Nan Du, et al. ∙

research

∙ 06/03/2013

Distributed k-Means and k-Median Clustering on General Topologies

This paper provides new algorithms for distributed clustering for two po...

Maria-Florina Balcan, et al. ∙

Yingyu Liang

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