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Optimal transport (OT) has an important role in transforming data distributions in a manner which engenders fairness. Typically, the OT operators are learnt from the unfair attribute-labelled data, and then used for their repair. Two significant limi
Externí odkaz:
http://arxiv.org/abs/2410.02840
Autor:
Shorten, Connor, Pierse, Charles, Smith, Thomas Benjamin, Cardenas, Erika, Sharma, Akanksha, Trengrove, John, van Luijt, Bob
The ability of Large Language Models (LLMs) to generate structured outputs, such as JSON, is crucial for their use in Compound AI Systems. However, evaluating and improving this capability remains challenging. In this work, we introduce StructuredRAG
Externí odkaz:
http://arxiv.org/abs/2408.11061
An optimal randomized strategy for design of balanced, normalized mass transport plans is developed. It replaces -- but specializes to -- the deterministic, regularized optimal transport (OT) strategy, which yields only a certainty-equivalent plan. T
Externí odkaz:
http://arxiv.org/abs/2408.02701
As consumer flexibility becomes expected, it is important that the market mechanisms which attain that flexibility are perceived as fair. We set out fairness issues in energy markets today, and propose a market design to address them. Consumption is
Externí odkaz:
http://arxiv.org/abs/2407.20814
Controlled charging of electric vehicles, EVs, is a major potential source of flexibility to facilitate the integration of variable renewable energy and reduce the need for stationary energy storage. To offer system services from EVs, fleet aggregato
Externí odkaz:
http://arxiv.org/abs/2406.07454
We present a novel class of proof-of-position algorithms: Tree-Proof-of-Position (T-PoP). This algorithm is decentralised, collaborative and can be computed in a privacy preserving manner, such that agents do not need to reveal their position publicl
Externí odkaz:
http://arxiv.org/abs/2405.06761
Vaccination campaigns have both direct and indirect effects that act to control an infectious disease as it spreads through a population. Indirect effects arise when vaccinated individuals block disease transmission in any infection chains they are p
Externí odkaz:
http://arxiv.org/abs/2405.03707
Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Control Regularization (RL
Externí odkaz:
http://arxiv.org/abs/2404.15199
With the advent of the AI Act and other regulations, there is now an urgent need for algorithms that repair unfairness in training data. In this paper, we define fairness in terms of conditional independence between protected attributes ($S$) and fea
Externí odkaz:
http://arxiv.org/abs/2403.13864
We derive iterative scaling algorithms of the Sinkhorn-Knopp (SK) type for constrained optimal transport. The constraints are in the form of prior-imposed zeroes in the transport plan. Based on classical Bregman arguments, we prove asymptotic converg
Externí odkaz:
http://arxiv.org/abs/2404.00003