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William Cohen

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15 papers

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JAIR Journal 2020 Journal Article

TensorLog: A Probabilistic Database Implemented Using Deep-Learning Infrastructure

William Cohen
Fan Yang
Kathryn Rivard Mazaitis

We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neural-network infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. The integration with these frameworks enables use of GPU-based parallel processors for inference and learning, making TensorLog the first highly parallellizable probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.

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NeurIPS Conference 2019 Conference Paper

Game Design for Eliciting Distinguishable Behavior

Fan Yang
Liu Leqi
Yifan Wu
Zachary Lipton
Pradeep Ravikumar
Tom Mitchell
William Cohen

The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such traits range from surveys to manually-constructed experiments and games. However, these traditional games are limited because they are typically designed based on heuristics. In this paper, we formulate the task of designing behavior diagnostic games that elicit distinguishable behavior as a mutual information maximization problem, which can be solved by optimizing a variational lower bound. Our framework is instantiated by using prospect theory to model varying player traits, and Markov Decision Processes to parameterize the games. We validate our approach empirically, showing that our designed games can successfully distinguish among players with different traits, outperforming manually-designed ones by a large margin.

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NeurIPS Conference 2018 Conference Paper

GLoMo: Unsupervised Learning of Transferable Relational Graphs

Zhilin Yang
Jake Zhao
Bhuwan Dhingra
Kaiming He
William Cohen
Russ Salakhutdinov
Yann LeCun

Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e. g. , words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels.

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NeurIPS Conference 2018 Conference Paper

Semi-Supervised Learning with Declaratively Specified Entropy Constraints

Haitian Sun
William Cohen
Lidong Bing

We propose a technique for declaratively specifying strategies for semi-supervised learning (SSL). SSL methods based on different assumptions perform differently on different tasks, which leads to difficulties applying them in practice. In this paper, we propose to use entropy to unify many types of constraints. Our method can be used to easily specify ensembles of semi-supervised learners, as well as agreement constraints and entropic regularization constraints between these learners, and can be used to model both well-known heuristics such as co-training, and novel domain-specific heuristics. Besides, our model is flexible as to the underlying learning mechanism. Compared to prior frameworks for specifying SSL techniques, our technique achieves consistent improvements on a suite of well-studied SSL benchmarks, and obtains a new state-of-the-art result on a difficult relation extraction task.

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NeurIPS Conference 2017 Conference Paper

Differentiable Learning of Logical Rules for Knowledge Base Reasoning

Fan Yang
Zhilin Yang
William Cohen

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.

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NeurIPS Conference 2017 Conference Paper

Good Semi-supervised Learning That Requires a Bad GAN

Zihang Dai
Zhilin Yang
Fan Yang
William Cohen
Russ Salakhutdinov

Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically we show that given the discriminator objective, good semi-supervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets.

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AAAI Conference 2016 Conference Paper

Distant IE by Bootstrapping Using Lists and Document Structure

Lidong Bing
Mingyang Ling
Richard Wang
William Cohen

Distant labeling for information extraction (IE) suffers from noisy training data. We describe a way of reducing the noise associated with distant IE by identifying coupling constraints between potential instance labels. As one example of coupling, items in a list are likely to have the same label. A second example of coupling comes from analysis of document structure: in some corpora, sections can be identiﬁed such that items in the same section are likely to have the same label. Such sections do not exist in all corpora, but we show that augmenting a large corpus with coupling constraints from even a small, well-structured corpus can improve performance substantially, doubling F1 on one task.

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IJCAI Conference 2016 Conference Paper

Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs

Zhilin Yang
Jie Tang
William Cohen

We study the extent to which online social networks can be connected to open knowledge bases. The problem is referred to as learning social knowledge graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings. GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities - i. e. , social network users and knowledge concepts - in a shared latent topic space. Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods. We then deploy the method on AMiner, a large-scale online academic search system with a network of 38, 049, 189 researchers with a knowledge base with 35, 415, 011 concepts. Our method significantly decreases the error rate in an online A/B test with live users.

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NeurIPS Conference 2016 Conference Paper

Review Networks for Caption Generation

Zhilin Yang
Ye Yuan
Yuexin Wu
William Cohen
Russ Salakhutdinov

We propose a novel extension of the encoder-decoder framework, called a review network. The review network is generic and can enhance any existing encoder- decoder model: in this paper, we consider RNN decoders with both CNN and RNN encoders. The review network performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a thought vector after each review step; the thought vectors are used as the input of the attention mechanism in the decoder. We show that conventional encoder-decoders are a special case of our framework. Empirically, we show that our framework improves over state-of- the-art encoder-decoder systems on the tasks of image captioning and source code captioning.

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AAAI Conference 2015 Conference Paper

Never-Ending Learning

Tom Mitchell
William Cohen
Estevam Hruschka
Partha Talukdar
Justin Betteridge
Andrew Carlson
Bhavana Dalvi Mishra
Matthew Gardner

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reﬂect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never- Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million conﬁdence-weighted beliefs (e. g. , servedWith(tea, biscuits)), while learning continually to improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs from old ones, and is now beginning to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http: //rtw. ml. cmu. edu, and followed on Twitter at @CMUNELL.

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NeurIPS Conference 2010 Conference Paper

Efficient Relational Learning with Hidden Variable Detection

Ni Lao
Jun Zhu
Liu Liu
Yandong Liu
William Cohen

Markov networks (MNs) can incorporate arbitrarily complex features in modeling relational data. However, this flexibility comes at a sharp price of training an exponentially complex model. To address this challenge, we propose a novel relational learning approach, which consists of a restricted class of relational MNs (RMNs) called relation tree-based RMN (treeRMN), and an efficient Hidden Variable Detection algorithm called Contrastive Variable Induction (CVI). On one hand, the restricted treeRMN only considers simple (e. g. , unary and pairwise) features in relational data and thus achieves computational efficiency; and on the other hand, the CVI algorithm efficiently detects hidden variables which can capture long range dependencies. Therefore, the resultant approach is highly efficient yet does not sacrifice its expressive power. Empirical results on four real datasets show that the proposed relational learning method can achieve similar prediction quality as the state-of-the-art approaches, but is significantly more efficient in training; and the induced hidden variables are semantically meaningful and crucial to improve the training speed and prediction qualities of treeRMNs.

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AAAI Conference 2010 Conference Paper

Integrating Transfer Learning in Synthetic Student

Nan Li
William Cohen
Ken Koedinger

Building an intelligent agent, which simulates human-level learning appropriate for learning math, science, or a second language, could potentially benefit both education in understanding human learning, and artificial intelligence in creating human-level intelligence. Recently, we have proposed an efficient approach to acquiring procedural knowledge using transfer learning. However, it operated as a separate module. In this paper, we describe how to integrate this module into a machine-learning agent, SimStudent, that learns procedural knowledge from examples and through problem solving. We illustrate this method in the domain of algebra, after which we consider directions for future research in this area.

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NeurIPS Conference 2004 Conference Paper

Semi-Markov Conditional Random Fields for Information Extraction

Sunita Sarawagi
William Cohen

We describe semi-Markov conditional random ﬁelds (semi-CRFs), a con- ditionally trained version of semi-Markov chains. Intuitively, a semi- CRF on an input sequence x outputs a “segmentation” of x, in which labels are assigned to segments (i. e. , subsequences) of x rather than to individual elements xi of x. Importantly, features for semi-CRFs can measure properties of segments, and transitions within a segment can be non-Markovian. In spite of this additional power, exact learning and inference algorithms for semi-CRFs are polynomial-time—often only a small constant factor slower than conventional CRFs. In experiments on ﬁve named entity recognition problems, semi-CRFs generally outper- form conventional CRFs.

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NeurIPS Conference 2002 Conference Paper

Improving a Page Classifier with Anchor Extraction and Link Analysis

William Cohen

Most text categorization systems use simple models of documents and document collections. In this paper we describe a technique that im- proves a simple web page classiﬁer’s performance on pages from a new, unseen web site, by exploiting link structure within a site as well as page structure within hub pages. On real-world test cases, this technique signiﬁcantly and substantially improves the accuracy of a bag-of-words classiﬁer, reducing error rate by about half, on average. The system uses a variant of co-training to exploit unlabeled data from a new site. Pages are labeled using the base classiﬁer; the results are used by a restricted wrapper-learner to propose potential “main-category anchor wrappers”; and ﬁnally, these wrappers are used as features by a third learner to ﬁnd a categorization of the site that implies a simple hub structure, but which also largely agrees with the original bag-of-words classiﬁer.

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NeurIPS Conference 1997 Conference Paper

Learning to Order Things

William Cohen
Robert Schapire
Yoram Singer

There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i. e. , statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a preference Junction, of the form PREF( u, v), which indicates whether it is advisable to rank u before v. New instances are then ordered so as to maximize agreements with the learned preference func(cid: 173) tion. We show that the problem of finding the ordering that agrees best with a preference function is NP-complete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an on-line learning algorithm, based on the "Hedge" algorithm, for finding a good linear combination of ranking "experts. " We use the ordering algorithm combined with the on-line learning algorithm to find a combination of "search experts, " each of which is a domain-specific query expansion strategy for a WWW search engine, and present experimental results that demonstrate the merits of our approach.

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