68 citations · 135 across the 16 of their papers we have counts for
10 papers · 1 filter
Primal-Dual Neural Algorithmic Reasoning
Yu He, Ellen Vitercik
Neural Algorithmic Reasoning (NAR) trains neural networks to simulate classical algorithms, enabling structured and interpretable reasoning over complex data. While prior research…
Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures
Yu He, Yingxi Li, Colin White +1
Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making. Understanding their algorithmic reasoning abilities is therefore cr…
Bandit Profit-maximization for Targeted Marketing
Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
We study a sequential profit-maximization problem, optimizing for both price and ancillary variables like marketing expenditures. Specifically, we aim to maximize profit over an ar…
Improved Sample Complexity Bounds for Branch-and-Cut
Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm +1
Branch-and-cut is the most widely used algorithm for solving integer programs, employed by commercial solvers like CPLEX and Gurobi. Branch-and-cut has a wide variety of tunable pa…
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
Maria-Florina Balcan, Dan DeBlasio, Travis Dick +3
Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit mea…
Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
Algorithms typically come with tunable parameters that have a considerable impact on the computational resources they consume. Too often, practitioners must hand-tune the parameter…
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
Maria-Florina Balcan, Travis Dick, Ellen Vitercik
Data-driven algorithm design, that is, choosing the best algorithm for a specific application, is a crucial problem in modern data science. Practitioners often optimize over a para…
Generalization Guarantees for Multi-item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
We study multi-item profit maximization when there is an underlying distribution over buyers' values. In practice, a full description of the distribution is typically unavailable,…
Sample Complexity of Automated Mechanism Design
Maria-Florina Balcan, Tuomas Sandholm, Ellen Vitercik
The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolve…
Learning Combinatorial Functions from Pairwise Comparisons
Maria-Florina Balcan, Ellen Vitercik, Colin White
A large body of work in machine learning has focused on the problem of learning a close approximation to an underlying combinatorial function, given a small set of labeled examples…