Lise Getoor | DeepAI

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∙ 01/30/2023

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation

The ability to accurately locate and navigate to a specific object is a ...

0 Kaiwen Zhou, et al. ∙

∙ 09/10/2021

D-REX: Dialogue Relation Extraction with Explanations

Existing research studies on cross-sentence relation extraction in long-...

16 Alon Albalak, et al. ∙

∙ 06/11/2021

Local Explanation of Dialogue Response Generation

In comparison to the interpretation of classification models, the explan...

12 Yi-Lin Tuan, et al. ∙

∙ 09/05/2020

HyperFair: A Soft Approach to Integrating Fairness Criteria

Recommender systems are being employed across an increasingly diverse se...

0 Charles Dickens, et al. ∙

∙ 04/07/2020

Causal Relational Learning

Causal inference is at the heart of empirical research in natural and so...

20 Babak Salimi, et al. ∙

∙ 01/16/2020

Estimating Aggregate Properties In Relational Networks With Unobserved Data

Aggregate network properties such as cluster cohesion and the number of ...

24 Varun Embar, et al. ∙

∙ 01/05/2020

User Profiling Using Hinge-loss Markov Random Fields

A variety of approaches have been proposed to automatically infer the pr...

6 Golnoosh Farnadi, et al. ∙

∙ 06/10/2019

Estimating Causal Effects of Tone in Online Debates

Statistical methods applied to social media posts shed light on the dyna...

0 Dhanya Sridhar, et al. ∙

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

∙ 09/13/2018

A Fairness-aware Hybrid Recommender System

Recommender systems are used in variety of domains affecting people's li...

0 Golnoosh Farnadi, et al. ∙

∙ 07/03/2018

Scalable Structure Learning for Probabilistic Soft Logic

Statistical relational frameworks such as Markov logic networks and prob...

4 Varun Embar, et al. ∙

∙ 11/16/2017

Using Noisy Extractions to Discover Causal Knowledge

Knowledge bases (KB) constructed through information extraction from tex...

0 Dhanya Sridhar, et al. ∙

∙ 07/04/2016

Generic Statistical Relational Entity Resolution in Knowledge Graphs

Entity resolution, the problem of identifying the underlying entity of r...

0 Jay Pujara, et al. ∙

∙ 07/02/2016

Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks

A neighborhood graph, which represents the instances as vertices and the...

0 Shobeir Fakhraei, et al. ∙

∙ 05/17/2015

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

A fundamental challenge in developing high-impact machine learning techn...

0 Stephen H. Bach, et al. ∙

∙ 01/16/2014

Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition

We address the cost-sensitive feature acquisition problem, where misclas...

0 Mustafa Bilgic, et al. ∙

∙ 09/26/2013

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

Graphical models for structured domains are powerful tools, but the comp...

0 Stephen Bach, et al. ∙

∙ 08/30/2013

A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization

In modern data science problems, techniques for extracting value from bi...

0 Hui Miao, et al. ∙

∙ 03/28/2013

Scalable Text and Link Analysis with Mixed-Topic Link Models

Many data sets contain rich information about objects, as well as pairwi...

0 Yaojia Zhu, et al. ∙

∙ 01/30/2013

Utility Elicitation as a Classification Problem

We investigate the application of classification techniques to utility e...

0 Urszula Chajewska, et al. ∙

∙ 05/09/2012

Bisimulation-based Approximate Lifted Inference

There has been a great deal of recent interest in methods for performing...

0 Prithviraj Sen, et al. ∙

∙ 03/15/2012

Probabilistic Similarity Logic

Many machine learning applications require the ability to learn from and...

0 Matthias Brocheler, et al. ∙

∙ 07/25/2011

Lifted Graphical Models: A Survey

This article presents a survey of work on lifted graphical models. We re...

0 Lilyana Mihalkova, et al. ∙

∙ 11/16/2010

A Probabilistic Approach for Learning Folksonomies from Structured Data

Learning structured representations has emerged as an important problem ...

0 Anon Plangprasopchok, et al. ∙

∙ 05/27/2010

Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata

Many social Web sites allow users to annotate the content with descripti...

0 Anon Plangprasopchok, et al. ∙

∙ 05/26/2010

Integrating Structured Metadata with Relational Affinity Propagation

Structured and semi-structured data describing entities, taxonomies and ...

0 Anon Plangprasopchok, et al. ∙

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