1 code implementation • 1 Nov 2023 • Chuizheng Meng, Yihe Dong, Sercan Ö. Arik, Yan Liu, Tomas Pfister
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized controlled trials (RCTs) suffer from high cost or impracticality.
no code implementations • 26 May 2023 • Sayna Ebrahimi, Sercan O. Arik, Yihe Dong, Tomas Pfister
To bridge this gap, we propose LANISTR, an attention-based framework to learn from LANguage, Image, and STRuctured data.
no code implementations • 6 Apr 2023 • Yihe Dong, Sercan O. Arik
Feature selection has been widely used to alleviate compute requirements during training, elucidate model interpretability, and improve model generalizability.
1 code implementation • 7 Oct 2022 • Rui Wang, Yihe Dong, Sercan Ö. Arik, Rose Yu
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 (DNNs).
1 code implementation • 5 Mar 2021 • 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.
no code implementations • NeurIPS 2020 • 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.
1 code implementation • 22 Jun 2020 • Yihe Dong, Will Sawin, Yoshua Bengio
Hypergraphs provide a natural representation for many real world datasets.
3 code implementations • NeurIPS 2020 • Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman
We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes.
1 code implementation • 3 May 2020 • Yihe Dong, Yu Gao, Richard Peng, Ilya Razenshteyn, Saurabh Sawlani
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 graph.
1 code implementation • 9 Mar 2020 • 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.
1 code implementation • ICML 2020 • Arturs Backurs, Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner
Our extensive experiments, on real-world text and image datasets, show that Flowtree improves over various baselines and existing methods in either running time or accuracy.
Data Structures and Algorithms
1 code implementation • NeurIPS 2019 • Yihe Dong, Samuel B. Hopkins, Jerry Li
In robust mean estimation the goal is to estimate the mean $\mu$ of a distribution on $\mathbb{R}^d$ given $n$ independent samples, an $\varepsilon$-fraction of which have been corrupted by a malicious adversary.
no code implementations • 3 Apr 2019 • Hao Chen, Ilaria Chillotti, Yihe Dong, Oxana Poburinnaya, Ilya Razenshteyn, M. Sadegh Riazi
In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential.
1 code implementation • ICLR 2020 • Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner
Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms.