Search Results for author:
Found 38 papers, 18 papers with code
Online Distribution Learning with Local Private Constraints
We study the problem of online conditional distribution estimation with \emph{unbounded} label sets under local differential privacy.
Interpretable Online Network Dictionary Learning for Inferring Long-Range Chromatin Interactions
First, we demonstrate that online cvxNDL retains the accuracy of classical DL methods while simultaneously ensuring unique interpretability and scalability.
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls
Third, we compute the complexity of the convex hulls in hyperbolic spaces to assess the extent of data leakage; at the same time, in order to limit communication cost for the hulls, we propose a new quantization method for the Poincar\'e disc coupled with Reed-Solomon-like encoding.
Semi-Quantitative Group Testing for Efficient and Accurate qPCR Screening of Pathogens with a Wide Range of Loads
Pathogenic infections pose a significant threat to global health, affecting millions of people every year and presenting substantial challenges to healthcare systems worldwide.
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances.
Unlearning Graph Classifiers with Limited Data Resources
As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems.
Machine Unlearning of Federated Clusters
We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework.
Certified Graph Unlearning
For example, when unlearning $20\%$ of the nodes on the Cora dataset, our approach suffers only a $0. 1\%$ loss in test accuracy while offering a $4$-fold speed-up compared to complete retraining.
HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering
Furthermore, it is currently not known how to choose the most suitable approximation objective for noisy fitting.
Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces
The excellent performance of the Poincar\'e second-order and strategic perceptrons shows that the proposed framework can be extended to general machine learning problems in hyperbolic spaces.
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
We also provide a theoretical analysis that justifies the use of XMC over link prediction and motivates integrating XR-Transformers, a powerful method for solving XMC problems, into the GIANT framework.
Ranked #2 on
Node Property Prediction
on ogbn-papers100M
Highly Scalable and Provably Accurate Classification in Poincare Balls
For hierarchical data, the space of choice is a hyperbolic space since it guarantees low-distortion embeddings for tree-like structures.
You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks
We propose AllSet, a new hypergraph neural network paradigm that represents a highly general framework for (hyper)graph neural networks and for the first time implements hypergraph neural network layers as compositions of two multiset functions that can be efficiently learned for each task and each dataset.
Linear Classifiers in Product Space Forms
The results show that classification in low-dimensional product space forms for scRNA-seq data offers, on average, a performance improvement of $\sim15\%$ when compared to that in Euclidean spaces of the same dimension.
Query-Based Selection of Optimal Candidates under the Mallows Model
We study the secretary problem in which rank-ordered lists are generated by the Mallows model and the goal is to identify the highest-ranked candidate through a sequential interview process which does not allow rejected candidates to be revisited.
Methodology Discrete Mathematics Information Theory Combinatorics Information Theory 05A
AC-DC: Amplification Curve Diagnostics for Covid-19 Group Testing
The first part of the paper presents a review of the gold-standard testing protocol for Covid-19, real-time, reverse transcriptase PCR, and its properties and associated measurement data such as amplification curves that can guide the development of appropriate and accurate adaptive group testing protocols.
Geometry of Similarity Comparisons
To study this question, we introduce the notions of the \textit{ordinal capacity} of a target space form and \emph{ordinal spread} of the similarity measurements.
Support Estimation with Sampling Artifacts and Errors
Here we introduce the first known approach to support estimation in the presence of sampling artifacts and errors where each sample is assumed to arise from a Poisson repeat channel which simultaneously captures repetitions and deletions of samples.
Adaptive Universal Generalized PageRank Graph Neural Network
We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information extraction, regardless of the extent to which the node labels are homophilic or heterophilic.
GPR
Node Classification on Non-Homophilic (Heterophilic) Graphs
+1
Multi-MotifGAN (MMGAN): Motif-targeted Graph Generation and Prediction
Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity patterns known as network motifs.
Image processing in DNA
The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis.
Landing Probabilities of Random Walks for Seed-Set Expansion in Hypergraphs
We describe the first known mean-field study of landing probabilities for random walks on hypergraphs.
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
Landing probabilities (LP) of random walks (RW) over graphs encode rich information regarding graph topology.
Online Convex Matrix Factorization with Representative Regions
We address both problems by proposing the first online convex MF algorithm that maintains a collection of constant-size sets of representative data samples needed for interpreting each of the basis (Ding et al. [2010]) and has the same almost sure convergence guarantees as the online learning algorithm of Mairal et al. [2010].
Quadratic Decomposable Submodular Function Minimization: Theory and Practice (Computation and Analysis of PageRank over Hypergraphs)
We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs.
Regularized Weighted Chebyshev Approximations for Support Estimation
We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the state-of-the-art techniques in the area and outperforms them in practice.
Higher-Order Spectral Clustering under Superimposed Stochastic Block Model
In particular, we prove non-asymptotic upper bounds on the misclustering error of spectral community detection for a SupSBM setting in which triangles or 3-uniform hyperedges are superimposed with undirected edges.
Motif and Hypergraph Correlation Clustering
Our contributions are as follows: We first introduce several variants of motif correlation clustering and then show that these clustering problems are NP-hard.
Quadratic Decomposable Submodular Function Minimization
The problem is closely related to decomposable submodular function minimization and arises in many learning on graphs and hypergraphs settings, such as graph-based semi-supervised learning and PageRank.
Query K-means Clustering and the Double Dixie Cup Problem
We consider the problem of approximate $K$-means clustering with outliers and side information provided by same-cluster queries and possibly noisy answers.
Revisiting Decomposable Submodular Function Minimization with Incidence Relations
We introduce a new approach to decomposable submodular function minimization (DSFM) that exploits incidence relations.
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
We introduce submodular hypergraphs, a family of hypergraphs that have different submodular weights associated with different cuts of hyperedges.
Inhomogeneous Hypergraph Clustering with Applications
Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics.
Efficient Rank Aggregation via Lehmer Codes
We propose a novel rank aggregation method based on converting permutations into their corresponding Lehmer codes or other subdiagonal images.
Multiclass MinMax Rank Aggregation
We introduce a new family of minmax rank aggregation problems under two distance measures, the Kendall {\tau} and the Spearman footrule.
A new correlation clustering method for cancer mutation analysis
To test $C^3$, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying driver genes.
Correlation Clustering and Biclustering with Locally Bounded Errors
We consider a generalized version of the correlation clustering problem, defined as follows.
Correlation Clustering with Constrained Cluster Sizes and Extended Weights Bounds
We consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges.
