Search Results for author: Kevin Leyton-Brown

Found 27 papers, 7 papers with code

The Perils of Learning Before Optimizing

no code implementations18 Jun 2021 Chris Cameron, Jason Hartford, Taylor Lundy, Kevin Leyton-Brown

When prediction targets are stochastic, a two-stage solution must make an a priori choice about which statistics of the target distribution to model -- we consider expectations over prediction targets -- while an end-to-end solution can make this choice adaptively.

Stochastic Optimization

Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates

no code implementations1 Dec 2020 Natalie Collina, Nicole Immorlica, Kevin Leyton-Brown, Brendan Lucier, Neil Newman

The value of a match is determined by the types of the matched agents.

Computer Science and Game Theory Data Structures and Algorithms

Exemplar Guided Active Learning

no code implementations NeurIPS 2020 Jason Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz

The challenge is that we are not informed which labels are common and which are rare, and the true label distribution may exhibit extreme skew.

Active Learning Word Sense Disambiguation

PMI-Masking: Principled masking of correlated spans

no code implementations ICLR 2021 Yoav Levine, Barak Lenz, Opher Lieber, Omri Abend, Kevin Leyton-Brown, Moshe Tennenholtz, Yoav Shoham

Specifically, we show experimentally that PMI-Masking reaches the performance of prior masking approaches in half the training time, and consistently improves performance at the end of training.

Valid Causal Inference with (Some) Invalid Instruments

no code implementations19 Jun 2020 Jason Hartford, Victor Veitch, Dhanya Sridhar, Kevin Leyton-Brown

The technique is simple to apply and is "black-box" in the sense that it may be used with any instrumental variable estimator as long as the treatment effect is identified for each valid instrument independently.

Causal Inference

Learning under Invariable Bayesian Safety

no code implementations8 Jun 2020 Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz

A recent body of work addresses safety constraints in explore-and-exploit systems.

Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver

no code implementations21 Mar 2020 Devon Graham, Satish Kumar Sarraf, Taylor Lundy, Ali MohammadMehr, Sara Uppal, Tae Yoon Lee, Hedayat Zarkoob, Scott Duke Kominers, Kevin Leyton-Brown

To see where this might be true in downtown Vancouver, we used artificial intelligence techniques to estimate the amount of time it would take drivers to both park on and off street for destinations throughout the city.

Fiduciary Bandits

no code implementations ICML 2020 Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown, Moshe Tennenholtz

Recommendation systems often face exploration-exploitation tradeoffs: the system can only learn about the desirability of new options by recommending them to some user.

Recommendation Systems

Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration

1 code implementation NeurIPS 2019 Robert Kleinberg, Kevin Leyton-Brown, Brendan Lucier, Devon Graham

Unfortunately, Structured Procrastination is not $\textit{adaptive}$ to characteristics of the parameterized algorithm: it treats every input like the worst case.

Deep Models of Interactions Across Sets

1 code implementation ICML 2018 Jason Hartford, Devon R Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh

In experiments, our models achieved surprisingly good generalization performance on this matrix extrapolation task, both within domains (e. g., new users and new movies drawn from the same distribution used for training) and even across domains (e. g., predicting music ratings after training on movies).

Matrix Completion Recommendation Systems

Deep IV: A Flexible Approach for Counterfactual Prediction

1 code implementation ICML 2017 Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy

Counterfactual prediction requires understanding causal relationships between so-called treatment and outcome variables.

Deep Optimization for Spectrum Repacking

no code implementations11 Jun 2017 Neil Newman, Alexandre Fréchette, Kevin Leyton-Brown

A crucial element of the auction design was the construction of a solver, dubbed SATFC, that determined whether sets of stations could be "repacked" in this way; it needed to run every time a station was given a price quote.

Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates

no code implementations30 Mar 2017 Katharina Eggensperger, Marius Lindauer, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown

In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures.

Hyperparameter Optimization

Counterfactual Prediction with Deep Instrumental Variables Networks

no code implementations30 Dec 2016 Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy

We are in the middle of a remarkable rise in the use and capability of artificial intelligence.

Deep Learning for Predicting Human Strategic Behavior

no code implementations NeurIPS 2016 Jason S. Hartford, James R. Wright, Kevin Leyton-Brown

Predicting the behavior of human participants in strategic settings is an important problem in many domains.

ASlib: A Benchmark Library for Algorithm Selection

2 code implementations8 Jun 2015 Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Frechette, Holger Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren

To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature.

Platform

The Configurable SAT Solver Challenge (CSSC)

no code implementations5 May 2015 Frank Hutter, Marius Lindauer, Adrian Balint, Sam Bayless, Holger Hoos, Kevin Leyton-Brown

It is well known that different solution strategies work well for different types of instances of hard combinatorial problems.

Computational Analysis of Perfect-Information Position Auctions

no code implementations4 Aug 2014 David R. M Thompson, Kevin Leyton-Brown

After experimentation with other designs, the major search engines converged on the weighted, generalized second-price auction (wGSP) for selling keyword advertisements.

Empirically Evaluating Multiagent Learning Algorithms

no code implementations31 Jan 2014 Erik Zawadzki, Asher Lipson, Kevin Leyton-Brown

Most such claims in the literature are based on small experiments, which has hampered understanding as well as the development of new multiagent learning (MAL) algorithms.

Q-Learning

ParamILS: An Automatic Algorithm Configuration Framework

no code implementations15 Jan 2014 Frank Hutter, Thomas Stuetzle, Kevin Leyton-Brown, Holger H. Hoos

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms.

Hyperparameter Optimization

Bayesian Optimization With Censored Response Data

no code implementations7 Oct 2013 Frank Hutter, Holger Hoos, Kevin Leyton-Brown

Bayesian optimization (BO) aims to minimize a given blackbox function using a model that is updated whenever new evidence about the function becomes available.

Algorithm Runtime Prediction: Methods & Evaluation

no code implementations5 Nov 2012 Frank Hutter, Lin Xu, Holger H. Hoos, Kevin Leyton-Brown

We also comprehensively describe new and existing features for predicting algorithm runtime for propositional satisfiability (SAT), travelling salesperson (TSP) and mixed integer programming (MIP) problems.

Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms

1 code implementation18 Aug 2012 Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown

Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.

Feature Selection General Classification +1

Sequential Model-Based Optimization for General Algorithm Configuration

1 code implementation LION 2011 2011 Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown

State-of-the-art algorithms for hard computational problems often expose many parameters that can be modified to improve empirical performance.

Hyperparameter Optimization

Bayesian Action-Graph Games

no code implementations NeurIPS 2010 Albert X. Jiang, Kevin Leyton-Brown

Games of incomplete information, or Bayesian games, are an important game-theoretic model and have many applications in economics.

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