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Siddharth Prasad

9 accepted papers

2026

Weakest Bidder Types and New Core-Selecting Combinatorial Auctions

AAAI 2026technical

Core-selecting combinatorial auctions are popular auction designs that constrain prices to eliminate the incentive for any group of bidders---with the seller---to renegotiate for a better deal. They help overcome the low-revenue issues of classical combinatorial auctions. We introduce a new class of

Cited by 0SourcePDFScholar
2025

Increasing Revenue in Efficient Combinatorial Auctions by Learning to Generate Artificial Competition

AAAI 2025technical

The design of multi-item, multi-bidder auctions involves a delicate balancing act of economic objectives, bidder incentives, and real-world complexities. Efficient auctions, that is, auctions that allocate items to maximize total bidder value, are practically desirable since they promote the most ec…

Cited by 0SourcePDFScholar
2025

New Sequence-Independent Lifting Techniques for Cover Inequalities and When They Induce Facets

IJCAI 2025

Sequence-independent lifting is a procedure for strengthening valid inequalities of an integer program. We generalize the sequence-independent lifting method of Gu, Nemhauser, and Savelsbergh (GNS lifting) for cover inequalities and correct an error in their proposed generalization. We obtain a new

Cited by 0SourcePDFScholar
2023

Bicriteria Multidimensional Mechanism Design with Side Information

NeurIPS 2023poster

We develop a versatile new methodology for multidimensional mechanism design that incorporates side information about agent types to generate high social welfare and high revenue simultaneously. Prominent sources of side information in practice include predictions from a machine-learning model train…

Cited by 11SourcePDFScholar
2022

Maximizing Revenue under Market Shrinkage and Market Uncertainty

NeurIPS 2022accept

A shrinking market is a ubiquitous challenge faced by various industries. In this paper we formulate the first formal model of shrinking markets in multi-item settings, and study how mechanism design and machine learning can help preserve revenue in an uncertain, shrinking market. Via a sample-based…

Cited by 3SourcePDFScholar
2022

Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts

NeurIPS 2022accept

The incorporation of cutting planes within the branch-and-bound algorithm, known as branch-and-cut, forms the backbone of modern integer programming solvers. These solvers are the foremost method for solving discrete optimization problems and thus have a vast array of applications in machine learnin…

Cited by 27SourcePDFScholar
2021

Learning Within an Instance for Designing High-Revenue Combinatorial Auctions

IJCAI 2021poster

We develop a new framework for designing truthful, high-revenue (combinatorial) auctions for limited supply. Our mechanism learns within an instance. It generalizes and improves over previously-studied random-sampling mechanisms. It first samples a participatory group of bidders, then samples severa…

Cited by 8SourcePDFScholar
2021

Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond

NeurIPS 2021spotlight

Cutting-plane methods have enabled remarkable successes in integer programming over the last few decades. State-of-the-art solvers integrate a myriad of cutting-plane techniques to speed up the underlying tree-search algorithm used to find optimal solutions. In this paper we provide sample complexit…

Cited by 42SourcePDFScholar
2020

Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs

IJCAI 2020poster

A two-part tariff is a pricing scheme that consists of an up-front lump sum fee and a per unit fee. Various products in the real world are sold via a menu, or list, of two-part tariffs---for example gym memberships, cell phone data plans, etc. We study learning high-revenue menus of two-part tariffs…

Cited by 0SourcePDFScholar