no code implementations • 26 Mar 2024 • Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i. e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog.
2 code implementations • 17 Jan 2024 • Charles Dickens, Changyu Gao, Connor Pryor, Stephen Wright, Lise Getoor
We address a key challenge for neuro-symbolic (NeSy) systems by leveraging convex and bilevel optimization techniques to develop a general gradient-based framework for end-to-end neural and symbolic parameter learning.
no code implementations • 30 Jan 2023 • Kaiwen Zhou, Kaizhi Zheng, Connor Pryor, Yilin Shen, Hongxia Jin, Lise Getoor, Xin Eric Wang
Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments.
1 code implementation • 20 Dec 2022 • Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon, Connor Pryor, Lise Getoor, William Yang Wang
Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans.
no code implementations • 14 Jul 2022 • Eriq Augustine, Pegah Jandaghi, Alon Albalak, Connor Pryor, Charles Dickens, William Wang, Lise Getoor
Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community.
1 code implementation • 27 May 2022 • Connor Pryor, Charles Dickens, Eriq Augustine, Alon Albalak, William Wang, Lise Getoor
In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks.
1 code implementation • 12 May 2022 • Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor, Luke Yoffe, Deepak Ramachandran, Lise Getoor, Jay Pujara, William Yang Wang
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
1 code implementation • NLP4ConvAI (ACL) 2022 • Alon Albalak, Varun Embar, Yi-Lin Tuan, Lise Getoor, William Yang Wang
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.
Ranked #7 on Dialog Relation Extraction on DialogRE
1 code implementation • NeurIPS 2021 • Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang
To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences.
no code implementations • 5 Sep 2020 • Charles Dickens, Rishika Singh, Lise Getoor
In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system.
no code implementations • 7 Apr 2020 • Babak Salimi, Harsh Parikh, Moe Kayali, Sudeepa Roy, Lise Getoor, Dan Suciu
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.
no code implementations • AKBC 2020 • Varun Embar, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Christos Faloutsos, Lise Getoor
However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent.
no code implementations • 16 Jan 2020 • Varun Embar, Sriram Srinivasan, Lise Getoor
In this paper, we study the effectiveness of these approaches in estimating aggregate properties on networks with missing attributes.
no code implementations • 5 Jan 2020 • Golnoosh Farnadi, Lise Getoor, Marie-Francine Moens, Martine De Cock
In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile.
1 code implementation • 10 Jun 2019 • Dhanya Sridhar, Lise Getoor
In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses.
no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
no code implementations • 3 Jul 2018 • Varun Embar, Dhanya Sridhar, Golnoosh Farnadi, Lise Getoor
We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways.
no code implementations • 16 Nov 2017 • Dhanya Sridhar, Jay Pujara, Lise Getoor
Knowledge bases (KB) constructed through information extraction from text play an important role in query answering and reasoning.
1 code implementation • EMNLP 2017 • Jay Pujara, Eriq Augustine, Lise Getoor
Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations.
no code implementations • 11 Feb 2017 • Angelika Kimmig, Alex Memory, Renee J. Miller, Lise Getoor
In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping."
no code implementations • 4 Jul 2016 • Jay Pujara, Lise Getoor
A common theme in this research has been the importance of incorporating relational features into the resolution process.
no code implementations • 2 Jul 2016 • Shobeir Fakhraei, Dhanya Sridhar, Jay Pujara, Lise Getoor
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction.
no code implementations • 17 May 2015 • Stephen H. Bach, Matthias Broecheler, Bert Huang, Lise Getoor
In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data.
no code implementations • 16 Jan 2014 • Mustafa Bilgic, Lise Getoor
We address the cost-sensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features.
no code implementations • 26 Sep 2013 • Stephen Bach, Bert Huang, Ben London, Lise Getoor
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable.
no code implementations • 30 Aug 2013 • Hui Miao, Xiangyang Liu, Bert Huang, Lise Getoor
In modern data science problems, techniques for extracting value from big data require performing large-scale optimization over heterogenous, irregularly structured data.
no code implementations • 28 Mar 2013 • Yaojia Zhu, Xiaoran Yan, Lise Getoor, Cristopher Moore
The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm.
no code implementations • 7 Mar 2013 • Ben London, Theodoros Rekatsinas, Bert Huang, Lise Getoor
For the typical cases of real-valued functions and binary relations, we propose several loss functions and derive the associated parameter gradients.
no code implementations • 21 Feb 2013 • Ben London, Bert Huang, Lise Getoor
We investigate the generalizability of learned binary relations: functions that map pairs of instances to a logical indicator.
no code implementations • NeurIPS 2012 • Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'Leary
In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains.
no code implementations • NeurIPS 2010 • Matthias Broecheler, Lise Getoor
Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes.