Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Padala, Manisha"'
Blockchains deploy Transaction Fee Mechanisms (TFMs) to determine which user transactions to include in blocks and determine their payments (i.e., transaction fees). Increasing demand and scarce block resources have led to high user transaction fees.
Externí odkaz:
http://arxiv.org/abs/2401.13262
Fair resource allocation is an important problem in many real-world scenarios, where resources such as goods and chores must be allocated among agents. In this survey, we delve into the intricacies of fair allocation, focusing specifically on the cha
Externí odkaz:
http://arxiv.org/abs/2307.10985
Civic Crowdfunding (CC) uses the ``power of the crowd'' to garner contributions towards public projects. As these projects are non-excludable, agents may prefer to ``free-ride,'' resulting in the project not being funded. For single project CC, resea
Externí odkaz:
http://arxiv.org/abs/2211.13941
We formalize a framework for coordinating funding and selecting projects, the costs of which are shared among agents with quasi-linear utility functions and individual budgets. Our model contains the classical discrete participatory budgeting model a
Externí odkaz:
http://arxiv.org/abs/2206.05966
Autor:
Padala, Manisha, Gujar, Sujit
Fairness is well studied in the context of resource allocation. Researchers have proposed various fairness notions like envy-freeness (EF), and its relaxations, proportionality and max-min share (MMS). There is vast literature on the existential and
Externí odkaz:
http://arxiv.org/abs/2112.07255
Neural networks have shown state-of-the-art performance in designing auctions, where the network learns the optimal allocations and payment rule to ensure desirable properties. Motivated by the same, we focus on learning fair division of resources, w
Externí odkaz:
http://arxiv.org/abs/2112.05436
Autor:
Kanaparthy, Samhita, Padala, Manisha, Damle, Sankarshan, Sarvadevabhatla, Ravi Kiran, Gujar, Sujit
Fairness across different demographic groups is an essential criterion for face-related tasks, Face Attribute Classification (FAC) being a prominent example. Apart from this trend, Federated Learning (FL) is increasingly gaining traction as a scalabl
Externí odkaz:
http://arxiv.org/abs/2109.02351
Most of the existing algorithms for fair division do not consider externalities. Under externalities, the utility an agent obtains depends not only on its allocation but also on the allocation of other agents. An agent has a positive (negative) value
Externí odkaz:
http://arxiv.org/abs/2108.12806
Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work
Externí odkaz:
http://arxiv.org/abs/2108.09932
Generative Adversarial Networks (GANs) are by far the most successful generative models. Learning the transformation which maps a low dimensional input noise to the data distribution forms the foundation for GANs. Although they have been applied in v
Externí odkaz:
http://arxiv.org/abs/2004.06882