Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Kash, Ian A."'
Fair division is typically framed from a centralized perspective. We study a decentralized variant of fair division inspired by the dynamics observed in community-based targeting, mutual aid networks, and community resource management paradigms. We d
Externí odkaz:
http://arxiv.org/abs/2408.07821
Graph Neural Networks (GNNs) have been a powerful tool for node classification tasks in complex networks. However, their decision-making processes remain a black-box to users, making it challenging to understand the reasoning behind their predictions
Externí odkaz:
http://arxiv.org/abs/2402.06030
Autor:
Narayana, Sushirdeep, Kash, Ian A.
Publikováno v:
PMLR 216 (2023) : 1499-1509
We study the design of caching policies in applications such as serverless computing where there is not a fixed size cache to be filled, but rather there is a cost associated with the time an item stays in the cache. We present a model for such cachi
Externí odkaz:
http://arxiv.org/abs/2309.03521
Reinforcement learning generalizes multi-armed bandit problems with additional difficulties of a longer planning horizon and unknown transition kernel. We explore a black-box reduction from discounted infinite-horizon tabular reinforcement learning t
Externí odkaz:
http://arxiv.org/abs/2205.09056
Data and algorithms are essential and complementary parts of a large-scale decision-making process. However, their injudicious use can lead to unforeseen consequences, as has been observed by researchers and activists alike in the recent past. In thi
Externí odkaz:
http://arxiv.org/abs/2108.05523
Autor:
Blandin, Jack, Kash, Ian
Group fairness definitions such as Demographic Parity and Equal Opportunity make assumptions about the underlying decision-problem that restrict them to classification problems. Prior work has translated these definitions to other machine learning en
Externí odkaz:
http://arxiv.org/abs/2108.05315
Autor:
Narayana, Sushirdeep, Kash, Ian A.
We study the fair division problem of allocating multiple resources among a set of agents with Leontief preferences that are each required to complete a finite amount of work, which we term "limited demands". We examine the behavior of the classic Do
Externí odkaz:
http://arxiv.org/abs/2103.00391
We consider a model where an agent has a repeated decision to make and wishes to maximize their total payoff. Payoffs are influenced by an action taken by the agent, but also an unknown state of the world that evolves over time. Before choosing an ac
Externí odkaz:
http://arxiv.org/abs/2101.07304
Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall. We seek to understand how to relax these requirements. As a first step, we introduce a simple algorithm, local no-re
Externí odkaz:
http://arxiv.org/abs/1910.03094
Recent work shows that we can use partial verification instead of money to implement truthful mechanisms. In this paper we develop tools to answer the following question. Given an allocation rule that can be made truthful with payments, what is the m
Externí odkaz:
http://arxiv.org/abs/1812.07312