
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models
Supervised learning models often make systematic errors on rare subsets ...
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The Perils of Learning Before Optimizing
Formulating realworld optimization problems often begins with making pr...
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Mechanical TA 2: A System for Peer Grading with TA Support
Mechanical TA 2 (MTA2) is an open source webbased peer grading applicat...
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Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates
We study a dynamic nonbipartite matching problem. There is a fixed set ...
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Exemplar Guided Active Learning
We consider the problem of wisely using a limited budget to label a smal...
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PMIMasking: Principled masking of correlated spans
Masking tokens uniformly at random constitutes a common flaw in the pret...
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Valid Causal Inference with (Some) Invalid Instruments
Instrumental variable methods provide a powerful approach to estimating ...
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Learning under Invariable Bayesian Safety
A recent body of work addresses safety constraints in exploreandexploi...
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Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver
Onstreet parking is convenient, but has many disadvantages: onstreet s...
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ReportSensitive SpotChecking in PeerGrading Systems
Peer grading systems make large courses more scalable, provide students ...
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Fiduciary Bandits
Recommendation systems often face explorationexploitation tradeoffs: th...
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Procrastinating with Confidence: NearOptimal, Anytime, Adaptive Algorithm Configuration
Algorithm configuration methods optimize the performance of a parameteri...
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A Formal Separation Between Strategic and Nonstrategic Behavior
It is common to make a distinction between `strategic' behavior and othe...
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Formalizing the Boundary Between Strategic and Nonstrategic Reasoning
Research in multiagent systems often does not draw a clear distinction b...
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Deep Models of Interactions Across Sets
We use deep learning to model interactions across two or more sets of ob...
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Deep Optimization for Spectrum Repacking
Over 13 months in 201617 the FCC conducted an "incentive auction" to re...
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Efficient Benchmarking of Algorithm Configuration Procedures via ModelBased Surrogates
The optimization of algorithm (hyper)parameters is crucial for achievin...
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Counterfactual Prediction with Deep Instrumental Variables Networks
We are in the middle of a remarkable rise in the use and capability of a...
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ASlib: A Benchmark Library for Algorithm Selection
The task of algorithm selection involves choosing an algorithm from a se...
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The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for differ...
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Computational Analysis of PerfectInformation Position Auctions
After experimentation with other designs, the major search engines conve...
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ParamILS: An Automatic Algorithm Configuration Framework
The identification of performanceoptimizing parameter settings is an im...
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Bayesian Optimization With Censored Response Data
Bayesian optimization (BO) aims to minimize a given blackbox function us...
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Algorithm Runtime Prediction: Methods & Evaluation
Perhaps surprisingly, it is possible to predict how long an algorithm wi...
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Temporal ActionGraph Games: A New Representation for Dynamic Games
In this paper we introduce temporal action graph games (TAGGs), a novel ...
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SATzilla: Portfoliobased Algorithm Selection for SAT
It has been widely observed that there is no single "dominant" SAT solve...
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Kevin LeytonBrown
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Professor at The University of British Columbia, Department of Computer Science, Affiliate at Auctionomics, Consultant at Zynga, Associate Professor at The University of British Columbia from 20092014, Scientific Advisor at Zite, Inc.from 20072011