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Found 21 papers, 0 papers with code
Bandit Profit-maximization for Targeted Marketing
Specifically, we aim to maximize profit over an arbitrary sequence of multiple demand curves, each dependent on a distinct ancillary variable, but sharing the same price.
From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection
We approach this problem by introducing a notion of size generalization for clustering algorithm accuracy.
Disincentivizing Polarization in Social Networks
We present a model for content curation and personalization that avoids filter bubbles, along with algorithmic guarantees and nearly matching lower bounds.
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values.
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
These guarantees apply to infinite families of cutting planes, such as the family of Gomory mixed integer cuts, which are responsible for the main breakthrough speedups of integer programming solvers.
No-Regret Learning in Partially-Informed Auctions
We formalize this problem as an online learning task where the goal is to have low regret with respect to a myopic oracle that has perfect knowledge of the distribution over items and the seller's masking function.
Revenue maximization via machine learning with noisy data
We conclude with an application of our guarantees to multi-task mechanism design, where there are multiple distributions over buyers' values and the goal is to learn a high-revenue mechanism per distribution.
Improved Sample Complexity Bounds for Branch-and-Cut
If the training set is too small, a configuration may have good performance over the training set but poor performance on future integer programs.
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
We first bound the sample complexity of learning cutting planes from the canonical family of Chv\'atal-Gomory cuts.
Generalization in portfolio-based algorithm selection
This algorithm configuration procedure works by first selecting a portfolio of diverse algorithm parameter settings, and then, on a given problem instance, using an algorithm selector to choose a parameter setting from the portfolio with strong predicted performance.
Private Optimization Without Constraint Violations
We also prove a lower bound demonstrating that the difference between the objective value of our algorithm's solution and the optimal solution is tight up to logarithmic factors among all differentially private algorithms.
Refined bounds for algorithm configuration: The knife-edge of dual class approximability
We answer this question for algorithm configuration problems that exhibit a widely-applicable structure: the algorithm's performance as a function of its parameters can be approximated by a "simple" function.
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
We provide a broadly applicable theory for deriving generalization guarantees that bound the difference between the algorithm's average performance over the training set and its expected performance.
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
Our algorithm can help compile a configuration portfolio, or it can be used to select the input to a configuration algorithm for finite parameter spaces.
Learning to Prune: Speeding up Repeated Computations
We present an algorithm that learns to maximally prune the search space on repeated computations, thereby reducing runtime while provably outputting the correct solution each period with high probability.
Learning to Branch
Tree search algorithms recursively partition the search space to find an optimal solution.
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
We present general techniques for online and private optimization of the sum of dispersed piecewise Lipschitz functions.
Generalization Guarantees for Multi-item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms
We study multi-item profit maximization when there is an underlying distribution over buyers' values.
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
We address this problem for clustering, max-cut, and other partitioning problems, such as integer quadratic programming, by designing computationally efficient and sample efficient learning algorithms which receive samples from an application-specific distribution over problem instances and learn a partitioning algorithm with high expected performance.
Sample Complexity of Automated Mechanism Design
In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution.
Learning Combinatorial Functions from Pairwise Comparisons
However, for real-valued functions, cardinal labels might not be accessible, or it may be difficult for an expert to consistently assign real-valued labels over the entire set of examples.
