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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.

168,657 papers · 148 categories

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90181271361 · Jun 202019922001200920172026
48 results for class commitment

Background: Some developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such auto…

2020-03-02abs ↗pdf ↗

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.

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.

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 ↗

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.

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.

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.

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.

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.

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 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.

Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "…

2020-02-27abs ↗pdf ↗

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 ↗

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 ↗

We study the multi-armed bandit problem with subgaussian rewards. The explore-then-commit (ETC) strategy, which consists of an exploration phase followed by an exploitation phase, is one of the most widely used algorithms in a variety of online decision applications. Nevertheless, it has been shown in Garivier et al. (…

2020-02-21abs ↗pdf ↗

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).

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.

We analyze the practices of reservoir computing in the framework of statistical learning theory. In particular, we derive finite sample upper bounds for the generalization error committed by specific families of reservoir computing systems when processing discrete-time inputs under various hypotheses on their dependenc…

2019-10-30abs ↗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 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.

Framework insures AI actions with reserve capital, preventing loss.

problem Ensuring safety and accountability for AI actions with varying side effects.
method Developed Actuarial Action Interface (AAI) and Authority Frontier to price and gate AI actions.
result Found common refusal and release patterns across domains, with varying required reserve capital.

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.

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 ↗

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.

New explanation of reservoir computing using random projections.

problem Understanding the randomness in reservoir computing.
method Constructing strongly universal reservoir systems as random projections of state-space systems.
result Approximation of any fading memory filters class by training a linear readout for each filter.

In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-ter…

2010-02-11abs ↗pdf ↗

New protocol identifies impossible edge orientations in causal graphs.

problem Causal-discovery algorithms cannot distinguish edge directions without assumptions.
method Discrete impossibility certificates and oracle queries.
result Upper bound of 1+K1+K expert interactions for DAG recovery.

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 ↗

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 \(Ω( icefrac{\log T}{g^\dagger})\).

Before a person can be prosecuted and convicted for insider trading, he must first execute the overt act of trading. If no sale of security is consummated, no crime is also consummated. However, through a complex and insidious combination of various financial instruments, one can capture the same amount of gains from i…

2018-05-16abs ↗pdf ↗

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 ↗