
PBoS: Probabilistic BagofSubwords for Generalizing Word Embedding
We look into the task of generalizing word embeddings: given a set of pr...
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Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Unsupervised and selfsupervised learning approaches have become a cruci...
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Can Adversarial Weight Perturbations Inject Neural Backdoors?
Adversarial machine learning has exposed several security hazards of neu...
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Learning Entangled SingleSample Gaussians in the SubsetofSignals Model
In the setting of entangled singlesample distributions, the goal is to ...
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Learning Entangled SingleSample Distributions via Iterative Trimming
In the setting of entangled singlesample distributions, the goal is to ...
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Gradients as Features for Deep Representation Learning
We address the challenging problem of deep representation learning–the e...
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SimpleTran: Transferring PreTrained Sentence Embeddings for Low Resource Text Classification
Finetuning pretrained sentence embedding models like BERT has become t...
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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...
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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...
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Shallow Domain Adaptive Embeddings for Sentiment Analysis
This paper proposes a way to improve the performance of existing algorit...
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Robust Attribution Regularization
An emerging problem in trustworthy machine learning is to train models t...
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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...
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Recovery Guarantees for Quadratic Tensors with Limited Observations
We consider the tensor completion problem of predicting the missing entr...
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Generalizing Word Embeddings using Bag of Subwords
We approach the problem of generalizing pretrained word embeddings beyo...
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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...
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NGram Graph, A Novel Molecule Representation
Virtual highthroughput screening provides a strategy for prioritizing c...
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Improving Adversarial Robustness by DataSpecific Discretization
A recent line of research proposed (either implicitly or explicitly) gra...
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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...
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Domain Adapted Word Embeddings for Improved Sentiment Classification
Generic word embeddings are trained on largescale generic corpora; Doma...
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Learning Mixtures of Linear Regressions with Nearly Optimal Complexity
Mixtures of Linear Regressions (MLR) is an important mixture model with ...
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Provable Alternating Gradient Descent for Nonnegative Matrix Factorization with Strong Correlations
Nonnegative matrix factorization is a basic tool for decomposing data i...
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Optimal Sample Complexity for Matrix Completion and Related Problems via ℓ_2Regularization
We study the strong duality of nonconvex matrix factorization: we show ...
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Generalization and Equilibrium in Generative Adversarial Nets (GANs)
We show that training of generative adversarial network (GAN) may not ha...
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Recovery Guarantee of Nonnegative Matrix Factorization via Alternating Updates
Nonnegative matrix factorization is a popular tool for decomposing data...
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Diverse Neural Network Learns True Target Functions
Neural networks are a powerful class of functions that can be trained wi...
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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...
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Recovery guarantee of weighted lowrank approximation via alternating minimization
Many applications require recovering a ground truth lowrank matrix from...
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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'...
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RANDWALK: A Latent Variable Model Approach to Word Embeddings
Semantic word embeddings represent the meaning of a word via a vector, a...
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Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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A Distributed FrankWolfe Algorithm for CommunicationEfficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In...
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Budgeted Influence Maximization for Multiple Products
The typical algorithmic problem in viral marketing aims to identify a se...
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Distributed kMeans and kMedian Clustering on General Topologies
This paper provides new algorithms for distributed clustering for two po...
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Yingyu Liang
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Assistant Professor in Computer Sciences at University of WisconsinMadison