Christopher Meek

FoundWright: A System to Help People Re-find Pages from Their Web-history
Haekyu Park, Gonzalo Ramos, Jina Suh, Christopher Meek, Rachel Ng, Mary Czerwinski
Re-finding information is an essential activity, however, it can be difficult when people struggle to express what they are looking for. Through a need-finding…

Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Oleksandr Polozov, Christopher Meek, Dragomir Radev, Jianfeng Gao
Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning…

Structure-Grounded Pretraining for Text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, Matthew Richardson
Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns…

Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani
Large pre-trained language models have been used to generate code,providing a flexible interface for synthesizing programs from natural language specifications…

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, Jianfeng Gao
Most recent progress in natural language understanding (NLU) has been driven, in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU…

NL-EDIT: Correcting semantic parse errors through natural language interaction
Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah
We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting…

Embedded Bayesian Network Classifiers
David Heckerman, Chris Meek
Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence…

Machine Teaching: A New Paradigm for Building Machine Learning Systems
Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, John Wernsing
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number…

A Characterization of Prediction Errors
Christopher Meek
Understanding prediction errors and determining how to fix them is critical to building effective predictive systems. In this paper, we delineate four types of…

Analysis of a Design Pattern for Teaching with Features and Labels
Christopher Meek, Patrice Simard, Xiaojin Zhu
We study the task of teaching a machine to classify objects using features and labels. We introduce the Error-Driven-Featuring design pattern for teaching…

Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
David Maxwell Chickering, Christopher Meek
We introduce Selective Greedy Equivalence Search (SGES), a restricted version of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of…

Asymptotic Model Selection for Directed Networks with Hidden Variables
Dan Geiger, David Heckerman, Christopher Meek
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This…

Structure and Parameter Learning for Causal Independence and Causal Interaction Models
Christopher Meek, David Heckerman
This paper discusses causal independence models and a generalization of these models called causal interaction models. Causal interaction models are models…

A Bayesian Approach to Learning Bayesian Networks with Local Structure
David Maxwell Chickering, David Heckerman, Christopher Meek
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional…

Learning Mixtures of DAG Models
Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs)…

CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
Carl Kadie, Christopher Meek, David Heckerman
We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence. In experiments on real data,…

Regularized Minimax Conditional Entropy for Crowdsourcing
Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah
There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost…

Causal Inference in the Presence of Latent Variables and Selection Bias
Peter L. Spirtes, Christopher Meek, Thomas S. Richardson
We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent…

Strong Completeness and Faithfulness in Bayesian Networks
Christopher Meek
A completeness result for d-separation applied to discrete Bayesian networks is presented and it is shown that in a strong measure-theoretic sense almost all…

Causal Inference and Causal Explanation with Background Knowledge
Christopher Meek
This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation consistent with a set of background…

Models and Selection Criteria for Regression and Classification
David Heckerman, Christopher Meek
When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of…

Graphical Models and Exponential Families
Dan Geiger, Christopher Meek
We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no…

Quantifier Elimination for Statistical Problems
Dan Geiger, Christopher Meek
Recent improvement on Tarski's procedure for quantifier elimination in the first order theory of real numbers makes it feasible to solve small instances of the…

Dependency Networks for Collaborative Filtering and Data Visualization
David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie
We describe a graphical model for probabilistic relationships---an alternative to the Bayesian network---called a dependency network. The graph of a dependency…

Perfect Tree-Like Markovian Distributions
Ann Becker, Dan Geiger, Christopher Meek
We show that if a strictly positive joint probability distribution for a set of binary random variables factors according to a tree, then vertex separation…

Using Temporal Data for Making Recommendations
Andrew Zimdars, David Maxwell Chickering, Christopher Meek
We treat collaborative filtering as a univariate time series estimation problem: given a user's previous votes, predict the next vote. We describe two families…

Staged Mixture Modelling and Boosting
Christopher Meek, Bo Thiesson, David Heckerman
In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic…

Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a…

Finding Optimal Bayesian Networks
David Maxwell Chickering, Christopher Meek
In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use…

Practically Perfect
Christopher Meek, David Maxwell Chickering
The property of perfectness plays an important role in the theory of Bayesian networks. First, the existence of perfect distributions for arbitrary sets of…

Large-Sample Learning of Bayesian Networks is NP-Hard
David Maxwell Chickering, Christopher Meek, David Heckerman
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the…

ARMA Time-Series Modeling with Graphical Models
Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher Meek
We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic relationships in the model make it…

Inference for Multiplicative Models
Ydo Wexler, Christopher Meek
The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models,…

Finding a Path is Harder than Finding a Tree
C. Meek
I consider the problem of learning an optimal path graphical model from data and show the problem to be NP-hard for the maximum likelihood and minimum…

On the toric algebra of graphical models
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a…