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06/09/2023
Spectral gap-based deterministic tensor completion
Tensor completion is a core machine learning algorithm used in recommend...
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05/26/2023
Differentially private low-dimensional representation of high-dimensional data
Differentially private synthetic data provide a powerful mechanism to en...
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04/04/2023
A non-backtracking method for long matrix completion
We consider the problem of rectangular matrix completion in the regime w...
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02/11/2023
Algorithmically Effective Differentially Private Synthetic Data
We present a highly effective algorithmic approach for generating ε-diff...
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11/24/2022
Shifted Diffusion for Text-to-image Generation
We present Corgi, a novel method for text-to-image generation. Corgi is ...
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11/20/2022
MagicVideo: Efficient Video Generation With Latent Diffusion Models
We present an efficient text-to-video generation framework based on late...
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09/25/2022
Extreme singular values of inhomogeneous sparse random rectangular matrices
We develop a unified approach to bounding the largest and smallest singu...
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06/02/2022
Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches
A common approach for compressing large-scale data is through matrix ske...
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03/14/2022
Sparse random hypergraphs: Non-backtracking spectra and community detection
We consider the community detection problem in a sparse q-uniform hyperg...
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12/22/2021
Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Model
We consider the community detection problem in sparse random hypergraphs...
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01/12/2021
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Training Generative Adversarial Networks (GAN) on high-fidelity images u...
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12/16/2020
Self-Supervised Sketch-to-Image Synthesis
Imagining a colored realistic image from an arbitrarily drawn sketch is ...
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05/27/2020
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image Mu...
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05/23/2020
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
We propose a sequential variational autoencoder to learn disentangled re...
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11/20/2019
Sparse random tensors: concentration, regularization and applications
We prove a non-asymptotic concentration inequality of sparse inhomogeneo...
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11/05/2019
Federated Adversarial Domain Adaptation
Federated learning improves data privacy and efficiency in machine learn...
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10/23/2019
Deterministic tensor completion with hypergraph expanders
We provide a novel analysis of low rank tensor completion based on hyper...
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07/12/2019
Eigenvalues of the non-backtracking operator detached from the bulk
We describe the non-backtracking spectrum of a stochastic block model wi...
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05/26/2019
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learnin...
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04/22/2019
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Zero-Shot Learning
We investigate learning feature-to-feature translator networks by altern...
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04/11/2019
Community Detection in the Sparse Hypergraph Stochastic Block Model
We consider the community detection problem in sparse random hypergraphs...
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03/01/2019
Learning where to look: Semantic-Guided Multi-Attention Localization for Zero-Shot Learning
Zero-shot learning extends the conventional object classification to the...
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11/16/2018
Exact Recovery in the Hypergraph Stochastic Block Model: a Spectral Algorithm
We consider the exact recovery problem in the hypergraph stochastic bloc...
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08/20/2018
CU-Net: Coupled U-Nets
We design a new connectivity pattern for the U-Net architecture. Given s...
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12/04/2017
Imagine it for me: Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Most existing zero-shot learning methods consider the problem as a visua...
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09/04/2017
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
In this paper, we study learning visual classifiers from unstructured te...
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09/04/2017
