19 citations · 74 across the 51 of their papers we have counts for
12 papers · 1 filter
Optimal and Efficient Online Inverse Optimization
Anupam Gupta, Guru Guruganesh, Honghao Lin +3
In online inverse linear optimization, a learner recommends an action and then observes the choice of an expert who maximizes a fixed, unknown linear objective on ;…
Efficient Online Inverse Optimization with Regret
Yang Cai, Anupam Gupta, Vineet Gupta +7
We give a deterministic algorithm for online inverse linear optimization with regret , uniform in the horizon and time per round. A bound of this order was obtaine…
Quota Marketplace: Dynamic Pricing for Efficient Allocation of ML Training Resources
Balasubramanian Sivan, Renato Paes Leme, Mihai Tiuca +6
The escalating demand for Machine Learning (ML) training resources in recent years has resulted in a substantial gap between the high demand and the available supply. Efficient all…
Allocating Variance to Maximize Expectation
Renato Purita Paes Leme, Cliff Stein, Yifeng Teng +1
We design efficient approximation algorithms for maximizing the expectation of the supremum of families of Gaussian random variables. In particular, let $\mathrm{OPT}:=\max_{σ_1,\c…
Full Swap Regret and Discretized Calibration
Maxwell Fishelson, Robert Kleinberg, Princewill Okoroafor +3
We study the problem of minimizing swap regret in structured normal-form games. Players have a very large (potentially infinite) number of pure actions, but each action has an embe…
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms
Khashayar Khosravi, Renato Paes Leme, Chara Podimata +1
We propose a model for learning with bandit feedback while accounting for deterministically evolving and unobservable states that we call Bandits with Deterministically Evolving St…