71 citations · 245 across the 12 of their papers we have counts for
12 papers
LANISTR: Multimodal Learning from Structured and Unstructured Data
Sayna Ebrahimi, Sercan O. Arik, Yihe Dong +1
Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured d…
SLM: End-to-end Feature Selection via Sparse Learnable Masks
Yihe Dong, Sercan O. Arik
Feature selection has been widely used to alleviate compute requirements during training, elucidate model interpretability, and improve model generalizability. We propose SLM -- Sp…
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Rui Wang, Yihe Dong, Sercan Ö. Arik +1
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (D…
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas
Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited. This work proposes a new way…
HNHN: Hypergraph Networks with Hyperedge Neurons
Yihe Dong, Will Sawin, Yoshua Bengio
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolut…
CoinPress: Practical Private Mean and Covariance Estimation
Sourav Biswas, Yihe Dong, Gautam Kamath +1
We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes. We demonstrate the effect…
A Study of Performance of Optimal Transport
Yihe Dong, Yu Gao, Richard Peng +2
We investigate the problem of efficiently computing optimal transport (OT) distances, which is equivalent to the node-capacitated minimum cost maximum flow problem in a bipartite g…
COPT: Coordinated Optimal Transport for Graph Sketching
Yihe Dong, Will Sawin
We introduce COPT, a novel distance metric between graphs defined via an optimization routine, computing a coordinated pair of optimal transport maps simultaneously. This gives an…
Scalable Nearest Neighbor Search for Optimal Transport
Arturs Backurs, Yihe Dong, Piotr Indyk +2
The Optimal Transport (a.k.a. Wasserstein) distance is an increasingly popular similarity measure for rich data domains, such as images or text documents. This raises the necessity…
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Yihe Dong, Samuel B. Hopkins, Jerry Li
We study two problems in high-dimensional robust statistics: \emph{robust mean estimation} and \emph{outlier detection}. In robust mean estimation the goal is to estimate the mean…
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
Hao Chen, Ilaria Chillotti, Yihe Dong +3
The -Nearest Neighbor Search (-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In…
Learning Space Partitions for Nearest Neighbor Search
Yihe Dong, Piotr Indyk, Ilya Razenshteyn +1
Space partitions of underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general metr…