b'Yingyu Liang'

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

We look into the task of generalizing word embeddings: given a set of pr...
10/21/2020 ∙ by Zhao Jinman, et al. ∙ 0 ∙ share

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Functional Regularization for Representation Learning: A Unified Theoretical Perspective

Unsupervised and self-supervised learning approaches have become a cruci...
08/06/2020 ∙ by Siddhant Garg, et al. ∙ 13 ∙ share

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Can Adversarial Weight Perturbations Inject Neural Backdoors?

Adversarial machine learning has exposed several security hazards of neu...
08/04/2020 ∙ by Siddhant Garg, et al. ∙ 13 ∙ share

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

In the setting of entangled single-sample distributions, the goal is to ...
07/10/2020 ∙ by Yingyu Liang, et al. ∙ 0 ∙ share

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Learning Entangled Single-Sample Distributions via Iterative Trimming

In the setting of entangled single-sample distributions, the goal is to ...
04/20/2020 ∙ by Hui Yuan, et al. ∙ 0 ∙ share

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Gradients as Features for Deep Representation Learning

We address the challenging problem of deep representation learning–the e...
04/12/2020 ∙ by Fangzhou Mu, et al. ∙ 10 ∙ share

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

Fine-tuning pre-trained sentence embedding models like BERT has become t...
04/10/2020 ∙ by Siddhant Garg, et al. ∙ 0 ∙ share

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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...
02/23/2020 ∙ by Yingyu Liang, et al. ∙ 0 ∙ share

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

Multimodal language analysis often considers relationships between featu...
11/13/2019 ∙ by Zhongkai Sun, et al. ∙ 0 ∙ share

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Shallow Domain Adaptive Embeddings for Sentiment Analysis

This paper proposes a way to improve the performance of existing algorit...
08/16/2019 ∙ by Prathusha K Sarma, et al. ∙ 0 ∙ share

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Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models t...
05/23/2019 ∙ by Jiefeng Chen, et al. ∙ 4 ∙ share

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

Neural networks have great success in many machine learning applications...
11/12/2018 ∙ by Zeyuan Allen-Zhu, et al. ∙ 0 ∙ share

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Recovery Guarantees for Quadratic Tensors with Limited Observations

We consider the tensor completion problem of predicting the missing entr...
10/31/2018 ∙ by Hongyang Zhang, et al. ∙ 0 ∙ share

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Generalizing Word Embeddings using Bag of Subwords

We approach the problem of generalizing pre-trained word embeddings beyo...
09/12/2018 ∙ by Jinman Zhao, et al. ∙ 0 ∙ share

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Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

Neural networks have many successful applications, while much less theor...
08/03/2018 ∙ by Yuanzhi Li, et al. ∙ 0 ∙ share

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N-Gram Graph, A Novel Molecule Representation

Virtual high-throughput screening provides a strategy for prioritizing c...
06/24/2018 ∙ by Shengchao Liu, et al. ∙ 0 ∙ share

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Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gra...
05/20/2018 ∙ by Jiefeng Chen, et al. ∙ 0 ∙ share

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

Motivations like domain adaptation, transfer learning, and feature learn...
05/14/2018 ∙ by Mikhail Khodak, et al. ∙ 0 ∙ share

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Domain Adapted Word Embeddings for Improved Sentiment Classification

Generic word embeddings are trained on large-scale generic corpora; Doma...
05/11/2018 ∙ by Prathusha K Sarma, et al. ∙ 0 ∙ share

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Learning Mixtures of Linear Regressions with Nearly Optimal Complexity

Mixtures of Linear Regressions (MLR) is an important mixture model with ...
02/22/2018 ∙ by Yuanzhi Li, et al. ∙ 0 ∙ share

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

Non-negative matrix factorization is a basic tool for decomposing data i...
06/13/2017 ∙ by Yuanzhi Li, et al. ∙ 0 ∙ share

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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 ...
04/27/2017 ∙ by Maria-Florina Balcan, et al. ∙ 0 ∙ share

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Generalization and Equilibrium in Generative Adversarial Nets (GANs)

We show that training of generative adversarial network (GAN) may not ha...
03/02/2017 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates

Non-negative matrix factorization is a popular tool for decomposing data...
11/11/2016 ∙ by Yuanzhi Li, 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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Mapping Between fMRI Responses to Movies and their Natural Language Annotations

Several research groups have shown how to correlate fMRI responses to th...
10/13/2016 ∙ by Kiran Vodrahalli, et al. ∙ 0 ∙ share

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Recovery guarantee of weighted low-rank approximation via alternating minimization

Many applications require recovering a ground truth low-rank matrix from...
02/06/2016 ∙ by Yuanzhi Li, et al. ∙ 0 ∙ share

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Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Word embeddings are ubiquitous in NLP and information retrieval, but it'...
01/14/2016 ∙ by Sanjeev Arora, et al. ∙ 0 ∙ share

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

Semantic word embeddings represent the meaning of a word via a vector, a...
02/12/2015 ∙ by Sanjeev Arora, 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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A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

Learning sparse combinations is a frequent theme in machine learning. In...
04/09/2014 ∙ by Aurélien Bellet, 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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Distributed k-Means and k-Median Clustering on General Topologies

This paper provides new algorithms for distributed clustering for two po...
06/03/2013 ∙ by Maria-Florina Balcan, et al. ∙ 0 ∙ share

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Yingyu Liang

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