A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Agents collaborate to reduce regret in a multi-agent linear bandit problem with side information.
problem Reducing regret in a multi-agent stochastic linear bandit with side information.
method A decentralized algorithm where agents communicate subspace indices and each plays a projected LinUCB on the corresponding low-dimensional subspace.
result Per-agent finite-time regret is much smaller when agents communicate compared to non-communicating case.
New algorithm minimizes regret in multi-agent bandit problem with probabilistic communication.
problem Minimizing group regret in multi-agent multi-armed bandit problem with probabilistic communication.
method Proposes a new UCB-based algorithm for decentralized multi-agent multi-armed bandit problem on d-regular graphs with probabilistic communication.
result The proposed algorithm outperforms state-of-the-art algorithms in minimizing group regret.
A new algorithm reduces frequentist regret in multi-agent bandit problems with sparse hypergraphs.
problem Deriving a frequentist regret bound for Thompson sampling in multi-agent settings with sparse hypergraphs.
method Proposed ε-exploring Multi-Agent Thompson Sampling (ε-MATS) algorithm that combines exploration and exploitation strategies.
result Achieves a worst-case frequentist regret bound sublinear in time horizon and local arm size, optimal up to constants and logarithms for sparse hypergraphs.
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a limited set of neighbouring agents. Leveraging such loose couplings amon…
This thesis analyzes MACL systems with low-regret learning algorithms for sequential decision making.
problem Designing efficient learning algorithms for multi-agent cooperative systems to minimize regret.
method Analyzes and develops algorithms for cooperative multi-agent multi-armed bandit problems and online convex optimization in distributed settings.
result Presented regret lower bounds and efficient algorithms for achieving these bounds, providing guidance on communication protocols.
New learning methods for open systems with variable agents.
problem Learning in open systems with dynamic agent arrivals and departures.
method Formulated a unified open-system bandit problem with general dynamics, introducing new concepts like pre-training degree and stability.
result Certified global-UCB learning methodologies with provable guarantees, revealing dependencies between entry uncertainty, stability, and agent patterns.
The paper uncovers the mathematical structure enabling value decomposition in multi-agent systems.
problem Theoretical justification for why value decomposition works effectively in multi-agent systems remains underexplored.
method The paper introduces the concept of Markov entanglement to measure the underlying structure and demonstrates how it can be used to bound the decomposition error.
result The widely-used class of index policies is weakly entangled and enjoys a sublinear O(N) scale of decomposition error for N-agent systems.
Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majority of the rich literature available for saddle-point optimization has focused on the offline setting. In this paper, we study nonstationar…
Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…
As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual bandit framework arises as a natural candidate for such a task, its extension to a parallel setting is not straightforward: one needs to care…
We consider a decentralized multi-agent Multi Armed Bandit (MAB) setup consisting of N agents, solving the same MAB instance to minimize individual cumulative regret. In our model, agents collaborate by exchanging messages through pairwise gossip style communications on an arbitrary connected graph. We develop two no…