activity
20192026
most citedPrivate Interdependent Valuations: New Bounds for Single-Item Auctions and Matroids

2 citations · 3 across the 6 of their papers we have counts for

collaborators
Showing cs.GTShow all

9 papers · 1 filter

cs.GT2026

Knowing Who, Not How Much: Learning-Augmented Mechanisms for Consumer Utility Maximization

Kira Goldner, Divyarthi Mohan, Thodoris Tsilivis

We study consumer utility maximization in an online random-order model where strategic agents arrive sequentially. To circumvent strong impossibility results for utility maximizati…

cs.GT2025

Online Combinatorial Allocation with Interdependent Values

Michal Feldman, Simon Mauras, Divyarthi Mohan +1

We study online combinatorial allocation problems in the secretary setting, under interdependent values. In the interdependent model, introduced by Milgrom and Weber (1982), each a…

cs.GT2024

Mechanism Design via the Interim Relaxation

Kshipra Bhawalkar, Marios Mertzanidis, Divyarthi Mohan +1

We study revenue maximization for agents with additive preferences, subject to downward-closed constraints on the set of feasible allocations. In seminal work, Alaei~\cite{alaei201…

cs.GT20241 cited

Optimal Stopping with Interdependent Values

Simon Mauras, Divyarthi Mohan, Rebecca Reiffenhäuser

We study online selection problems in both the prophet and secretary settings, when arriving agents have interdependent values. In the interdependent values model, introduced in th…

cs.GT20242 cited

Private Interdependent Valuations: New Bounds for Single-Item Auctions and Matroids

Alon Eden, Michal Feldman, Simon Mauras +1

We study auction design within the widely acclaimed model of interdependent values, introduced by Milgrom and Weber [1982]. In this model, every bidder has a private signal $s_…

cs.GT2023

Constant Approximation for Private Interdependent Valuations

Alon Eden, Michal Feldman, Kira Goldner +2

The celebrated model of auctions with interdependent valuations, introduced by Milgrom and Weber in 1982, has been studied almost exclusively under private signals $s_1, \ldots, s_…