Characterizes preferences for decision-making under uncertainty using a leader-follower game model.
problem Decision-making under uncertainty and ambiguity aversion.
method Characterizes niveloidal preferences through a leader-follower game model, satisfying specific axioms.
result The leader's strategy space can serve as an ambiguity aversion index.
Study leader-follower games with terminal state constraints using McKean-Vlasov SDEs.
problem Leader-follower games with terminal state constraints.
method Linear McKean-Vlasov forward-backward SDEs, existence and uniqueness results, convergence results.
result Existence and uniqueness of solutions for leader-follower games with constraints.
LEASGD improves privacy-preserving decentralized learning with lower communication costs.
problem Achieving efficient and private decentralized learning.
method Proposes LEASGD, a Leader-Follower Elastic Averaging Stochastic Gradient Descent algorithm.
result LEASGD outperforms state-of-the-art algorithms in terms of lower loss and reduced communication costs.
We introduce CSE for MLSF games and devise online learning algorithms for achieving no-external Stackelberg-regret.
problem Learning equilibrium in leader-follower games with noisy bandit feedback.
method Proposed Correlated Stackelberg Equilibrium (CSE) and online learning algorithms balancing exploration and exploitation.
result Achieves no-external Stackelberg-regret, converging to approximate CSE.
Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities and ability to scale with the size of the data. In this work, we provide three contributions. First, we show t…
New algorithm reduces individual regret and communication costs in cooperative bandits.
problem Optimal individual and group regret in cooperative multi-agent bandits.
method Integrates a new communication policy into a learning algorithm.
result Achieves optimal individual regret and constant communication costs.
Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…
Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in prac…
The online problem of computing the top eigenvector is fundamental to machine learning. In both adversarial and stochastic settings, previous results (such as matrix multiplicative weight update, follow the regularized leader, follow the compressed leader, block power method) either achieve optimal regret but run slow,…
AMM finds optimal contract for LPs to maximize order flow.
problem Maximizing order flow in AMMs with LPs.
method Leader-follower stochastic game, closed-form equilibrium solutions.
result LPs incentivized to add liquidity when external price attracts more noise trading.
New mechanism designs regulate herding in financial markets.
problem Herding causes irrational market decisions and volatility.
method A trilateral game framework based on optimal control theory.
result Effective mechanisms improve social welfare.
Estimates network structure and interaction rules from multiple agent trajectories.
problem Modeling multi-agent systems on networks from data.
method Jointly infers network topology and interaction kernels using non-convex optimization.
result ORALS estimator is consistent and asymptotically normal under coercivity conditions.
The paper analyzes reinsurance strategies in peer-to-peer insurance schemes.
problem Strategic interaction between plan managers and reinsurers in P2P insurance.
method Develops two game-theoretic contract designs: Pareto and Bowley designs, deriving optimal contracts and analyzing their welfare effects.
result The Bowley design yields a unique optimal contract, while the Pareto design allows for multiple Pareto-optimal contracts.
Hybrid method uses LLM to filter lead-lag relationships in prediction markets.
problem Challenges in discovering robust lead-lag relationships in prediction markets due to spurious correlations.
method Two-stage approach: statistical Granger causality followed by LLM semantic re-ranking.
result LLM-based method outperforms statistical baseline, increasing win rate and reducing average loss magnitude.
New algorithm optimizes multi-armed bandits with low computational cost.
problem Optimizing multi-armed bandits with low computational cost.
method Proposes a new FTPL algorithm with optimistic principle for ambiguity.
result Unified regret analysis and low computational costs.
A new algorithm learns policies from batch data in hierarchical RL.
problem Learning policies from fixed batches of data without full exploration.
method Modeling RL as a two-player game with a leader-follower structure, proposing StackelbergLearner.
result StackelbergLearner achieves competitive performance in batch RL and real-world datasets.
New RL algorithms learn QSE from strategic feedbacks with sample efficiency.
problem Learning QSE in Markov games with strategic feedbacks.
method Proposes sample-efficient algorithms for online and offline settings, combining quantal response model learning and RL.
result Achieves sublinear regret bounds and quantifies model uncertainty.