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08/09/2023
When and How Does Known Class Help Discover Unknown Ones? Provable Understanding Through Spectral Analysis
Novel Class Discovery (NCD) aims at inferring novel classes in an unlabe...
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03/13/2023
Domain Generalization via Nuclear Norm Regularization
The ability to generalize to unseen domains is crucial for machine learn...
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06/03/2022
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
An important characteristic of neural networks is their ability to learn...
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01/31/2022
On the identifiability of mixtures of ranking models
Mixtures of ranking models are standard tools for ranking problems. Howe...
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10/06/2021
An Analysis of Attentive Walk-Aggregating Graph Neural Networks
Graph neural networks (GNNs) have been shown to possess strong represent...
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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...
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08/06/2020
Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Unsupervised and self-supervised learning approaches have become a cruci...
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08/04/2020
Can Adversarial Weight Perturbations Inject Neural Backdoors?
Adversarial machine learning has exposed several security hazards of neu...
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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 ...
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04/20/2020
Learning Entangled Single-Sample Distributions via Iterative Trimming
In the setting of entangled single-sample distributions, the goal is to ...
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04/12/2020
Gradients as Features for Deep Representation Learning
We address the challenging problem of deep representation learning–the e...
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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...
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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...
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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...
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08/16/2019
Shallow Domain Adaptive Embeddings for Sentiment Analysis
This paper proposes a way to improve the performance of existing algorit...
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05/23/2019
Robust Attribution Regularization
An emerging problem in trustworthy machine learning is to train models t...
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11/12/2018
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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10/31/2018
Recovery Guarantees for Quadratic Tensors with Limited Observations
We consider the tensor completion problem of predicting the missing entr...
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09/12/2018
Generalizing Word Embeddings using Bag of Subwords
We approach the problem of generalizing pre-trained word embeddings beyo...
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08/03/2018
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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06/24/2018
N-Gram Graph, A Novel Molecule Representation
Virtual high-throughput screening provides a strategy for prioritizing c...
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05/20/2018
Improving Adversarial Robustness by Data-Specific Discretization
A recent line of research proposed (either implicitly or explicitly) gra...
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05/14/2018
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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05/11/2018
Domain Adapted Word Embeddings for Improved Sentiment Classification
Generic word embeddings are trained on large-scale generic corpora; Doma...
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02/22/2018
Learning Mixtures of Linear Regressions with Nearly Optimal Complexity
Mixtures of Linear Regressions (MLR) is an important mixture model with ...
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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...
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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 ...
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03/02/2017
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
We show that training of generative adversarial network (GAN) may not ha...
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11/11/2016
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Non-negative matrix factorization is a popular tool for decomposing data...
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11/09/2016
Diverse Neural Network Learns True Target Functions
Neural networks are a powerful class of functions that can be trained wi...
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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...
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02/06/2016
Recovery guarantee of weighted low-rank approximation via alternating minimization
Many applications require recovering a ground truth low-rank matrix from...
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01/14/2016
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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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...
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07/21/2014
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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04/09/2014
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Learning sparse combinations is a frequent theme in machine learning. In...
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12/08/2013
Budgeted Influence Maximization for Multiple Products
The typical algorithmic problem in viral marketing aims to identify a se...
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06/03/2013
