Search Results for author: Nikhil Rao

Found 29 papers, 10 papers with code

Text Enriched Sparse Hyperbolic Graph Convolutional Networks

no code implementations6 Jul 2022 Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Chandan K. Reddy

In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer.

Language Modelling Link Prediction

Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search

1 code implementation14 Jun 2022 Chandan K. Reddy, Lluís Màrquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, Karthik Subbian

This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.

Learning Backward Compatible Embeddings

1 code implementation7 Jun 2022 Weihua Hu, Rajas Bansal, Kaidi Cao, Nikhil Rao, Karthik Subbian, Jure Leskovec

We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models.

Fraud Detection Product Recommendation +1

Task-Agnostic Graph Explanations

1 code implementation16 Feb 2022 Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks.

Cluster-and-Conquer: A Framework For Time-Series Forecasting

no code implementations26 Oct 2021 Reese Pathak, Rajat Sen, Nikhil Rao, N. Benjamin Erichson, Michael I. Jordan, Inderjit S. Dhillon

Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step.

Time Series Forecasting

Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs

1 code implementation NeurIPS 2021 Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy

Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection.

Knowledge Graph Embedding Knowledge Graphs +1

Task-Agnostic Graph Neural Explanations

no code implementations29 Sep 2021 Yaochen Xie, Sumeet Katariya, Xianfeng Tang, Edward W Huang, Nikhil Rao, Karthik Subbian, Shuiwang Ji

TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.

Scalable Feature Selection for (Multitask) Gradient Boosted Trees

no code implementations5 Sep 2021 Cuize Han, Nikhil Rao, Daria Sorokina, Karthik Subbian

Gradient Boosted Decision Trees (GBDTs) are widely used for building ranking and relevance models in search and recommendation.

Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs

1 code implementation23 Dec 2020 Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy

Promising approaches to tackle this problem include embedding the KG units (e. g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results.

Anomaly Detection Knowledge Graphs +2

Learning Robust Models for e-Commerce Product Search

no code implementations ACL 2020 Thanh V. Nguyen, Nikhil Rao, Karthik Subbian

Showing items that do not match search query intent degrades customer experience in e-commerce.

Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering

no code implementations29 May 2019 Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh

In this paper, we propose using Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, for exploiting deeper neighborhood information.

Collaborative Filtering Recommendation Systems

A Simple Approach to Learn Polysemous Word Embeddings

2 code implementations6 Jul 2017 Yifan Sun, Nikhil Rao, Weicong Ding

Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.

Word Embeddings Word Sense Induction +1

Matrix Completion via Factorizing Polynomials

no code implementations4 May 2017 Vatsal Shah, Nikhil Rao, Weicong Ding

While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities.

Matrix Completion Recommendation Systems

Dynamic Word Embeddings for Evolving Semantic Discovery

3 code implementations2 Mar 2017 Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, Hui Xiong

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.

Word Embeddings

Structured Sparse Regression via Greedy Hard Thresholding

no code implementations NeurIPS 2016 Prateek Jain, Nikhil Rao, Inderjit S. Dhillon

Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups.

Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction

no code implementations NeurIPS 2016 Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon

We develop novel regularization schemes and use scalable matrix factorization methods that are eminently suited for high-dimensional time series data that has many missing values.

Time Series Analysis Time Series Prediction

On Learning High Dimensional Structured Single Index Models

no code implementations13 Mar 2016 Nikhil Rao, Ravi Ganti, Laura Balzano, Rebecca Willett, Robert Nowak

Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features.

Structured Sparse Regression via Greedy Hard-Thresholding

no code implementations19 Feb 2016 Prateek Jain, Nikhil Rao, Inderjit Dhillon

Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups.

Sparse and Low-Rank Tensor Decomposition

no code implementations NeurIPS 2015 Parikshit Shah, Nikhil Rao, Gongguo Tang

Our method relies on a reduction of the problem to sparse and low-rank matrix decomposition via the notion of tensor contraction.

Tensor Decomposition

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

2 code implementations NeurIPS 2015 Nikhil Rao, Hsiang-Fu Yu, Pradeep K. Ravikumar, Inderjit S. Dhillon

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.

 Ranked #1 on Recommendation Systems on Flixster (using extra training data)

Collaborative Filtering Low-Rank Matrix Completion +1

High-dimensional Time Series Prediction with Missing Values

no code implementations28 Sep 2015 Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon

High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology.

Matrix Completion Time Series Prediction

Learning Single Index Models in High Dimensions

no code implementations30 Jun 2015 Ravi Ganti, Nikhil Rao, Rebecca M. Willett, Robert Nowak

Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression.

General Classification

Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice

no code implementations15 May 2015 Parikshit Shah, Nikhil Rao, Gongguo Tang

This motivates us to consider the problem of low rank tensor recovery from a class of linear measurements called separable measurements.

Matrix Completion Tensor Decomposition

Forward - Backward Greedy Algorithms for Atomic Norm Regularization

no code implementations23 Apr 2014 Nikhil Rao, Parikshit Shah, Stephen Wright

CoGEnT combines a greedy selection scheme based on the conditional gradient approach with a backward (or "truncation") step that exploits the quadratic nature of the objective to reduce the basis size.

Classification with Sparse Overlapping Groups

no code implementations18 Feb 2014 Nikhil Rao, Robert Nowak, Christopher Cox, Timothy Rogers

In this paper, we are interested in a less restrictive form of structured sparse feature selection: we assume that while features can be grouped according to some notion of similarity, not all features in a group need be selected for the task at hand.

Classification General Classification +1

Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis

no code implementations NeurIPS 2013 Nikhil Rao, Christopher Cox, Robert Nowak, Timothy Rogers

In this paper, we are interested in a less restrictive form of multitask learning, wherein (1) the available features can be organized into subsets according to a notion of similarity and (2) features useful in one task are similar, but not necessarily identical, to the features best suited for other tasks.

Cannot find the paper you are looking for? You can Submit a new open access paper.