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arXiv research

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.

168,742 papers · 148 categories

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223447670893 · Jun 202019922001200920172026
48 results for commitment problem

The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.

problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.

StakeBench evaluates language understanding by linking comments to market commitments, improving model alignment with real-world outcomes.

problem Existing financial NLP benchmarks measure perceived language rather than market commitments.
method StakeBench uses observable market behavior to supervise models, testing their ability to detect commitments, identify sides, and project odds.
result Models partially recover position-side signals but struggle with later tasks, highlighting structural failures.

New algorithm tackles high-dimensional contextual bandits without sparsity.

problem High-dimensional linear contextual bandit problem with large feature space.
method Proposes explore-then-commit (EtC) and adaptive explore-then-commit (AEtC) algorithms.
result Derives optimal rate for ETC algorithm and shows adaptive AEtC achieves it.

In this paper, we study multi-armed bandit problems in explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the hig…

2019-04-30abs ↗pdf ↗

Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.

problem Optimizing long-term Defined Contribution plans with realistic constraints and dynamic dynamics.
method Formulates and solves bilevel optimization problems for pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization.
result Time-consistent Mean-bPoE strategies maintain investor preferences for minimum terminal wealth, unlike Mean-CVaR.

This paper solves the best arm identification problem with both quick commitment and reward maximization.

problem Simultaneously identifying the best arm and minimizing regret in a stochastic Multi-Armed Bandit problem.
method Introduces Regret Optimal Best Arm Identification (ROBAI) and presents algorithms EOCP and its variants.
result Achieves asymptotic optimal regret and quick commitment to the optimal arm in both pre-determined and adaptive stopping times.

The discrete-time mean-variance portfolio selection formulation, a representative of general dynamic mean-risk portfolio selection problems, does not satisfy time consistency in efficiency (TCIE) in general, i.e., a truncated pre-committed efficient policy may become inefficient when considering the corresponding trunc…

2014-03-04abs ↗pdf ↗

The study analyzes when Bayesian averaging over decision trees is reliable.

problem When do Bayesian model averaging weights over decision trees provide reliable information?
method Closed-form solution for Bayesian decision trees with Catalan-exponential priors.
result Established a complete non-asymptotic theory of rational commitment thresholds.

New algorithm for decentralized matching markets without prior preference rankings.

problem Decentralized two-sided matching markets without known preference rankings.
method Epoch-based CA-ETC algorithm for decentralized matching markets.
result Achieves player optimal expected regret of O(T_0 (K log T / T_0 Δ^2)^(1/γ) + T_0 (T / T_0)^γ).

Algorithm reduces regret in partially observable systems by learning dynamics and using optimistic control.

problem Minimizing regret in partially observable linear quadratic control systems with unknown dynamics.
method ExpCommit algorithm that learns model parameters and uses optimism in uncertainty.
result End-to-end sublinear regret upper bound of O~(T2/3)\tilde{\mathcal{O}}(T^{2/3}) for ExpCommit.

In this paper, we investigate the Merton portfolio management problem in the context of non-exponential discounting. This gives rise to time-inconsistency of the decision-maker. If the decision-maker at time t=0 can commit his/her successors, he/she can choose the policy that is optimal from his/her point of view, and …

2007-08-03abs ↗pdf ↗

New framework tackles submodular welfare with multi-agent combinatorial bandits.

problem Maximizing total welfare among agents with shared constraints and submodular utilities under bandit feedback.
method Proposes an explore-then-commit strategy with randomized assignments for multi-agent combinatorial bandits.
result Achieves ildeO(T2/3) ilde{\mathcal{O}}(T^{2/3}) regret, first for partition-based submodular welfare problem under bandit feedback.

Study of 2imes22 imes 2 zero-sum games with noisy observations and commitments.

problem Analyzing 2imes22 imes 2 zero-sum games with noisy observations and commitments.
method Modeling a 2imes22 imes 2 zero-sum game with a leader committing to a strategy and a follower observing a noisy version of the leader's action.
result Observing the leader's action is either beneficial or immaterial for the follower, and the equilibrium payoff is bounded.

Continuous-time Kyle model shows privacy subsidy from noise-perturbed order flow.

problem Quantifying break-even fees for committed-AMM exchanges under privacy-aggregated information.
method Extended Nakamura's (2026) single-period result to continuous-time, observing order flow perturbed by Brownian noise.
result Cumulative privacy subsidy is identified as equivalent to Loss-Versus-Rebalancing in price observation gap.

Investors benefit from long horizons in a market with mean-reverting equity returns.

problem Optimal portfolio choice in a market with mean-reverting risk-free rate and equity risk-premium.
method Mean-variance optimization, Euler-Lagrange equation, Calculus of Variations, spectral problem.
result Optimal policies are characterized by eigenvalues of the lambda-matrix, leading to better risk-return trade-offs for long-term investors.

The paper minimizes Borda regret in dueling bandits models.

problem Minimizing Borda regret in dueling bandits models.
method Proposes explore-then-commit and EXP3-type algorithms for stochastic and adversarial settings respectively.
result Achieves nearly matching regret upper bounds of O(d2/3T2/3)O(d^{2/3} T^{2/3}) for both settings.

Scaling clustering algorithms to massive data sets is a challenging task. Recently, several successful approaches based on data summarization methods, such as coresets and sketches, were proposed. While these techniques provide provably good and small summaries, they are inherently problem dependent - the practitioner …

2017-11-27abs ↗pdf ↗

This paper solves a financial portfolio selection problem in incomplete markets.

problem Portfolio selection in incomplete financial markets with ambiguity.
method Constructing an efficient frontier, simplifying the problem, introducing a new distorted Legendre transformation, and proving the bipolar relation and distorted duality theorem.
result The existence and uniqueness of optimal strategies are shown for different utility functions under specific conditions.

Settings such as lending and policing can be modeled by a centralized agent allocating a resource (loans or police officers) amongst several groups, in order to maximize some objective (loans given that are repaid or criminals that are apprehended). Often in such problems fairness is also a concern. A natural notion of…

2018-08-30abs ↗pdf ↗

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

problem Stochastic linear bandits with high-dimensional sparse features.
method Derivation of minimax regret lower and upper bounds for explore-then-commit algorithm.
result Optimal rate of Θ(n2/3)Θ(n^{2/3}) for data-poor regime, complemented by O(n)O(\sqrt{n}) under signal magnitude assumption.

Study integrates reliability constraints into generation planning models.

problem Challenges in integrating reliability constraints with generation planning models.
method Leverages a weighted oblique decision tree (WODT) technique to embed reliability verification constraints.
result Demonstrates effectiveness in achieving reliable and optimal planning solutions.

This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite…

2013-03-06abs ↗pdf ↗

This paper introduces modal epistemic tools for risk management.

problem Identifying and certifying risk claims when institutions lack the necessary epistemic stance.
method Develops crisp and fuzzy modal semantics for assurance and working commitment, distinguishing between object-level risk claims and meta-level epistemic diagnostics.
result Risk governance should model evidential incompleteness and failures of escalation, not just hazards and losses.

Paper proposes DG-ETC for online submodular maximization with stochastic bandit feedback.

problem Online unconstrained submodular maximization with stochastic bandit feedback.
method Double-Greedy - Explore-then-Commit (DG-ETC) approach.
result DG-ETC achieves logarithmic regret O(dlog(dT))O(d\log(dT)) for 1/21/2-approximate pseudo-regret.

New method shows multi-objective bandits are not harder than single-objective ones.

problem Comparing multi-objective bandits to single-objective ones.
method Upper and lower confidence-bound estimators for every arm-objective pair, using top-two races and uncertainty-greedy rule.
result Achieves Pareto regret of \(O( icefrac{\log T}{g^\dagger})\), matching lower bound of \(Ω( icefrac{\log T}{g^\dagger})\).

Study on investment strategy for agents with periodic preferences and discounting.

problem Investment decisions by agents with periodic S-shaped preferences and present bias.
method Infinite-horizon, continuous-time portfolio selection problem with quasi-hyperbolic discounting.
result Time-consistent planning strategy can be formulated as an equilibrium to a static mean field game.

Paper studies zero-sum games with noisy observations and identifies equilibrium conditions.

problem Zero-sum games with noisy observations of the leader's actions.
method Analyzes the equilibrium of games with noisy action observability, identifies necessary conditions for uniqueness, and investigates the cardinality of best responses.
result The noisy observations significantly impact the cardinality of the follower's set of best responses, and under certain conditions, this set becomes a singleton almost surely.

Non-availability of reliable and sustainable electric power is a major problem in the developing world. Renewable energy sources like solar are not very lucrative in the current stage due to various uncertainties like weather, storage, land use among others. There also exists various other issues like mis-commitment of…

2017-11-08abs ↗pdf ↗

In this paper we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose …

2010-06-22abs ↗pdf ↗

Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we dev…

2017-11-28abs ↗pdf ↗

Transfer learning techniques have been widely used in the reality that it is difficult to obtain sufficient labeled data in the target domain, but a large amount of auxiliary data can be obtained in the relevant source domain. But most of the existing methods are based on offline data. In practical applications, it is …

2019-10-20abs ↗pdf ↗