Search Results for author:
Found 11 papers, 9 papers with code
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
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).
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited.
COPT: Coordinated Optimal Transport on Graphs
We introduce COPT, a novel distance metric between graphs defined via an optimization routine, computing a coordinated pair of optimal transport maps simultaneously.
HNHN: Hypergraph Networks with Hyperedge Neurons
Hypergraphs provide a natural representation for many real world datasets.
CoinPress: Practical Private Mean and Covariance Estimation
We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes.
A Study of Performance of Optimal Transport
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.
COPT: Coordinated Optimal Transport for Graph Sketching
We introduce COPT, a novel distance metric between graphs defined via an optimization routine, computing a coordinated pair of optimal transport maps simultaneously.
Scalable Nearest Neighbor Search for Optimal Transport
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
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
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.
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
In this paper, we introduce SANNS, a system for secure $k$-NNS that keeps client's query and the search result confidential.
Learning Space Partitions for Nearest Neighbor Search
Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms.
