1 citations · 2 across the 13 of their papers we have counts for
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Probing Dec-POMDP Reasoning in Cooperative MARL
Kale-ab Tessera, Leonard Hinckeldey, Riccardo Zamboni +2
Cooperative multi-agent reinforcement learning (MARL) is typically framed as a decentralised partially observable Markov decision process (Dec-POMDP), a setting whose hardness stem…
Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking
Vaisakh Shaj, Cameron Barker, Aidan Scannell +3
State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but their linear state updates limi…
Rationality Measurement and Theory for Reinforcement Learning Agents
Kejiang Qian, Amos Storkey, Fengxiang He
This paper proposes a suite of rationality measures and associated theory for reinforcement learning agents, a property increasingly critical yet rarely explored. We define an acti…
Adapting Time Series Foundation Models through Data Mixtures
Thomas L. Lee, Edoardo M. Ponti, Amos Storkey
Time series foundation models (TSFMs) have become increasingly popular for zero-shot forecasting. However, for a new time series domain not fully covered by the pretraining set, pe…
Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning
Weipu Zhang, Adam Jelley, Trevor McInroe +2
While deep reinforcement learning (RL) from pixels has achieved remarkable success, its sample inefficiency remains a critical limitation for real-world applications. Model-based R…
Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
Benjamin R. Redhead, Thomas L. Lee, Peng Gu +2
Probabilistic forecasting is increasingly critical across high-stakes domains, from finance and epidemiology to climate science. However, current evaluation frameworks lack a conse…