7 citations · 17 across the 11 of their papers we have counts for
12 papers
The Good, the Bad and the Submodular: Fairly Allocating Mixed Manna Under Order-Neutral Submodular Preferences
Cyrus Cousins, Vignesh Viswanathan, Yair Zick
We study the problem of fairly allocating indivisible goods (positively valued items) and chores (negatively valued items) among agents with decreasing marginal utilities over item…
Dividing Good and Better Items Among Agents with Bivalued Submodular Valuations
Cyrus Cousins, Vignesh Viswanathan, Yair Zick
We study the problem of fairly allocating a set of indivisible goods among agents with {\em bivalued submodular valuations} -- each good provides a marginal gain of either or $…
Into the Unknown: Assigning Reviewers to Papers with Uncertain Affinities
Cyrus Cousins, Justin Payan, Yair Zick
A successful peer review process requires that qualified and interested reviewers are assigned to each paper. Most automated reviewer assignment approaches estimate a real-valued a…
Regret Pruning for Learning Equilibria in Simulation-Based Games
Bhaskar Mishra, Cyrus Cousins, Amy Greenwald
In recent years, empirical game-theoretic analysis (EGTA) has emerged as a powerful tool for analyzing games in which an exact specification of the utilities is unavailable. Instea…
Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games
Cyrus Cousins, Bhaskar Mishra, Enrique Areyan Viqueira +1
Empirical game-theoretic analysis (EGTA) is primarily focused on learning the equilibria of simulation-based games. Recent approaches have tackled this problem by learning a unifor…
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds
Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins +1
We present a novel method for reducing the computational complexity of rigorously estimating the partition functions (normalizing constants) of Gibbs (Boltzmann) distributions, whi…
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
Cyrus Cousins
We address an inherent difficulty in welfare-theoretic fair machine learning by proposing an equivalently axiomatically-justified alternative and studying the resulting computation…
Learning Competitive Equilibria in Noisy Combinatorial Markets
Enrique Areyan Viqueira, Cyrus Cousins, Amy Greenwald
We present a methodology to robustly estimate the competitive equilibria (CE) of combinatorial markets under the assumption that buyers do not know their precise valuations for bun…
Making mean-estimation more efficient using an MCMC trace variance approach: DynaMITE
Cyrus Cousins, Shahrzad Haddadan, Eli Upfal
We introduce a novel statistical measure for MCMC-mean estimation, the inter-trace variance , which depends on a Markov chain and a fu…
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin +1
We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structur…
Learning Equilibria of Simulation-Based Games
Enrique Areyan Viqueira, Cyrus Cousins, Eli Upfal +1
We tackle a fundamental problem in empirical game-theoretic analysis (EGTA), that of learning equilibria of simulation-based games. Such games cannot be described in analytical for…
Uniform Convergence Bounds for Codec Selection
Clayton Sanford, Cyrus Cousins, Eli Upfal
We frame the problem of selecting an optimal audio encoding scheme as a supervised learning task. Through uniform convergence theory, we guarantee approximately optimal codec selec…