Search Results for author: Christopher Meek

Found 29 papers, 3 papers with code

Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions

1 code implementation28 May 2022 Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Oleksandr Polozov, Christopher Meek, Dragomir Radev, Jianfeng Gao

We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space.

Arithmetic Reasoning Efficient Exploration +2

Synchromesh: Reliable code generation from pre-trained language models

no code implementations ICLR 2022 Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani

Then, Synchromesh feeds the examples to a pre-trained language model and samples programs using Constrained Semantic Decoding (CSD): a general framework for constraining the output to a set of valid programs in the target language.

Code Generation Language Modelling +1

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

1 code implementation4 Nov 2021 Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, Jianfeng Gao

We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks.

Few-Shot Learning Natural Language Understanding

NL-EDIT: Correcting semantic parse errors through natural language interaction

1 code implementation NAACL 2021 Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah

We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors.

Semantic Parsing Text-To-SQL

SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing

no code implementations NeurIPS Workshop CAP 2020 Tao Yu, Rui Zhang, Alex Polozov, Christopher Meek, Ahmed Hassan Awadallah

Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e. g., SQL, SPARQL) that can be executed against a structured ontology (e. g. databases, knowledge bases).

Ranked #3 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)

Dialogue State Tracking Language Modelling +4

Structure-Grounded Pretraining for Text-to-SQL

no code implementations NAACL 2021 Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, Matthew Richardson

Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation.


Machine Teaching: A New Paradigm for Building Machine Learning Systems

no code implementations21 Jul 2017 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

This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them.

BIG-bench Machine Learning

Analysis of a Design Pattern for Teaching with Features and Labels

no code implementations18 Nov 2016 Christopher Meek, Patrice Simard, Xiaojin Zhu

We analyze the potential risks and benefits of this teaching pattern through the use of teaching protocols, illustrative examples, and by providing bounds on the effort required for an optimal machine teacher using a linear learning algorithm, the most commonly used type of learners in interactive machine learning systems.

A Characterization of Prediction Errors

no code implementations18 Nov 2016 Christopher Meek

Understanding prediction errors and determining how to fix them is critical to building effective predictive systems.

Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations

no code implementations6 Jun 2015 David Maxwell Chickering, Christopher Meek

We introduce Selective Greedy Equivalence Search (SGES), a restricted version of Greedy Equivalence Search (GES).

Recursive Inversion Models for Permutations

no code implementations NeurIPS 2014 Christopher Meek, Marina Meila

We develop a new exponential family probabilistic model for permutations that can capture hierarchical structure, and that has the well known Mallows and generalized Mallows models as subclasses.

Asymptotic Model Selection for Directed Networks with Hidden Variables

no code implementations13 Feb 2013 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.

Model Selection

A Bayesian Approach to Learning Bayesian Networks with Local Structure

no code implementations6 Feb 2013 David Maxwell Chickering, David Heckerman, Christopher Meek

The majority of this work has concentrated on using decision-tree representations for the CPDs.

Structure and Parameter Learning for Causal Independence and Causal Interaction Models

no code implementations6 Feb 2013 Christopher Meek, David Heckerman

This paper discusses causal independence models and a generalization of these models called causal interaction models.

Learning Mixtures of DAG Models

no code implementations30 Jan 2013 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).

Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (2003)

no code implementations19 Jan 2013 Christopher Meek, Uffe Kjaerulff

This is the Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003

CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence

no code implementations12 Dec 2012 Carl Kadie, Christopher Meek, David Heckerman

We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence.

Collaborative Filtering

A Model for Temporal Dependencies in Event Streams

no code implementations NeurIPS 2011 Asela Gunawardana, Christopher Meek, Puyang Xu

We introduce the Piecewise-Constant Conditional Intensity Model, a model for learning temporal dependencies in event streams.

Improving Existing Fault Recovery Policies

no code implementations NeurIPS 2009 Guy Shani, Christopher Meek

In this paper we explain how to use data gathered from the interactions of the hand-made controller with the system, to create an optimized controller.

Decision Making Decision Making Under Uncertainty +1

MAS: a multiplicative approximation scheme for probabilistic inference

no code implementations NeurIPS 2008 Ydo Wexler, Christopher Meek

We show how to optimize $\epsilon$-decompositions and provide a fast closed-form solution for an $L_2$ approximation.

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