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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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8162331 · Jun 202019922001200920172026
48 results for zero-alpha violations

Bayesian methods detect significant IIA violations in similarity choice data.

problem Detecting IIA violations in similarity choice data complicates classical models.
method Proposed two statistical methods: classical goodness-of-fit test and Bayesian PPC.
result Significant IIA violations confirmed in both datasets, driven by context effects.

The correspondence between Riemann-Finsler geometries and effective field theories with spin-independent Lorentz violation is explored. We obtain the general quadratic action for effective scalar field theories in any spacetime dimension with Lorentz-violating operators of arbitrary mass dimension. Classical relativist…

2018-09-14abs ↗pdf ↗

Detecting faults and SLA violations in a timely manner is critical for telecom providers, in order to avoid loss in business, revenue and reputation. At the same time predicting SLA violations for user services in telecom environments is difficult, due to time-varying user demands and infrastructure load conditions. In…

2015-09-04abs ↗pdf ↗

We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification f…

2018-11-17abs ↗pdf ↗

CVTMLE improves statistical inference in settings of positivity or Donsker class violations.

problem Inference issues in causal inference due to data sparsity or near-positivity violations.
method Cross-validation of TMLE (CVTMLE) to improve performance in settings of positivity or Donsker class violations.
result CVTMLE vastly improves confidence interval coverage without affecting bias, especially in small sample sizes and near-positivity violations.

The study proves strong cosmic censorship violation for spherically symmetric dust clouds.

problem Violation of strong cosmic censorship for spherically symmetric dust clouds.
method Derived an ordinary differential equation for light rays and used it to prove strong cosmic censorship violation.
result Generic violation of strong cosmic censorship for spherically symmetric dust clouds.

New algorithm reduces sample complexity for safe reinforcement learning.

problem Safe reinforcement learning in constrained MDPs with performance and safety constraints.
method Model-based primal-dual algorithm balancing regret and bounded constraint violations.
result Proves near-optimal policies with bounded violations or zero violations in CMDPs.

This paper considers online convex optimization over a complicated constraint set, which typically consists of multiple functional constraints and a set constraint. The conventional online projection algorithm (Zinkevich, 2003) can be difficult to implement due to the potentially high computation complexity of the proj…

2016-04-08abs ↗pdf ↗

NucleusDiff models atomic nuclei interactions to prevent separation violations in drug design.

problem Maintaining minimum pairwise distance between atoms to avoid separation violations in drug design.
method Enforces distance constraint between atomic nuclei and manifolds in a diffusion model.
result Reduces separation violations by up to 100.00% and enhances binding affinity by up to 22.16%.

New algorithm reduces regret and constraint violation in online convex optimization with complex constraints.

problem Online convex optimization with multiple functional constraints and a simple constraint set.
method Instance-dependent bound using online primal-dual mirror-prox algorithm in general normed spaces.
result Achieves an O(√V*(T)) regret and O(1) constraint violation, improving over previous works.

Optimistic algorithm reduces regret and constraint violations in online convex optimization with adversarial constraints.

problem Online convex optimization with adversarial constraints.
method Improved algorithm using accurate predictions of loss and constraint functions.
result Improved bounds on regret and cumulative constraint violations.

Algorithm minimizes loss and constraint violations in online convex optimization with smooth penalties.

problem Minimizing loss and constraint violations in online convex optimization with smooth penalties.
method Projected gradient descent over a set around the current action.
result Both dynamic regret and constraint violation are bounded by the path-length.

CausalCompass evaluates TSCD robustness under violations of modeling assumptions.

problem Widespread adoption of TSCD is hindered by untestable causal assumptions and lack of robustness evaluation.
method CausalCompass is a flexible benchmark framework for assessing TSCD robustness under violations of modeling assumptions.
result No single method consistently attains optimal performance across all settings, but deep learning-based methods perform well.

Paper tackles constrained bandit problems with a new learning framework.

problem Optimizing a black-box reward function subject to a black-box constraint function over a continuous space.
method Rectified Pessimistic-Optimistic Learning (RPOL) framework, incorporating optimistic and pessimistic GP bandit learning.
result RPOL achieves sublinear regret and minimal cumulative constraint violation.

Causality violations are typically seen as unrealistic and undesirable features of a physical model. The following points out three reasons why causality violations, which Bonnor and Steadman identified even in solutions to the Einstein equation referring to ordinary laboratory situations, are not necessarily undesirab…

2006-09-15abs ↗pdf ↗

Measures policy-violating content prevalence with ML-assisted sampling and LLM labeling.

problem Accurate measurement of content violations that are often rare and costly to label.
method Design-based measurement system using ML-assisted probability sampling and LLM labeling.
result Produces unbiased prevalence estimates with confidence intervals and dashboard drilldowns.

This paper detects Markov violations in RL with noise, improving policy development.

problem Partial observability and sensor/actuator noise invalidate Markovian assumptions in RL.
method Combines PCMCI causal discovery with Markov Violation score (MVS).
result Even substantial noise doesn't always disrupt multi-step dependencies.

A new method tests Expected Shortfall by analyzing both duration and severity of VaR violations.

problem Lack of separate testing for frequency and severity in ES backtesting.
method Uses bivariate orthogonal polynomials to derive moment conditions for durations and severities.
result Proposes a Wald test for identifying mis-specified components in ES models.

Effective field theories with explicit Lorentz violation are intimately linked to Riemann-Finsler geometry. The quadratic single-fermion restriction of the Standard-Model Extension provides a rich source of pseudo-Riemann-Finsler spacetimes and Riemann-Finsler spaces. An example is presented that is constructed from a …

2011-04-28abs ↗pdf ↗

Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.

problem Challenging traditional concepts of cause-effect relations in quantum mechanics.
method Introducing a general framework to estimate causal influences without interventions or classical/quantum assumptions.
result Every pure bipartite entangled state violates classical bounds on causal influence, negating the idea that Bell inequalities are the only signature of incompatibility.

Our work investigates how to identify privacy violations in models using finite adversaries.

problem Identifying privacy violations in models with limited adversary capabilities.
method Investigates requirements for finite adversaries to identify privacy violations.
result Parameters quantify the capabilities of finite adversaries.

Certain momentum-dependent terms in the fermion sector of the Lorentz-violating Standard Model Extension (SME) yield solvable classical lagrangians of a type not mentioned in the literature. These cases yield new relatively simple examples of Finsler and pseudo-Finsler structures. One of the cases involves antisymmetri…

2012-01-18abs ↗pdf ↗

TMLE improves causal effect estimation in missing data scenarios with various positivity violations.

problem Estimating causal effects in studies with missing data and positivity violations.
method Targeted Maximum Likelihood Estimation (TMLE) with various missing data methods.
result Complete cases with TMLE incorporating an outcome-missingness model exhibit lower bias and greater robustness against positivity violations.

The physics of classical particles in a Lorentz-breaking spacetime has numerous features resembling the properties of Finsler geometry. In particular, the Lagrange function plays a role similar to that of a Finsler structure function. A summary is presented of recent results, including new calculable Finsler structures…

2013-08-15abs ↗pdf ↗

Differentiable causal discovery methods perform robustly under model violations.

problem Causal discovery algorithms struggle with real-world data due to unverifiable causal assumptions.
method Benchmarked differentiable causal discovery methods under eight model assumption violations.
result Differentiable causal discovery methods exhibit robust performance under Structural Hamming Distance and Structural Intervention Distance metrics.

New RL algorithm achieves sublinear regret and constraint violation without simulators.

problem Maximizing reward under utility constraints in large-scale systems.
method Model-free, simulator-free algorithm using LSVI-UCB with primal-dual optimization and soft-max policy.
result Achieves ildeO(d3H3T) ilde{\mathcal{O}}(\sqrt{d^3H^3T}) regret and ildeO(d3H3T) ilde{\mathcal{O}}(\sqrt{d^3H^3T}) constraint violation bounds.

New methods reduce constraint violations to certainty in stochastic optimization.

problem Finding a point with certain constraint satisfaction and near-stationarity.
method Single-loop variance-reduced stochastic first-order methods with truncated momentum schemes.
result Achieves strong convergence guarantees for εε-stochastic stationary points with certain constraint satisfaction.

Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety…

2018-11-28abs ↗pdf ↗

The paper addresses causal estimation for text data with apparent overlap violations.

problem Estimating causal effects from text data with unknown confounders and apparent overlap.
method Uses supervised representation learning to create a representation that preserves confounding information while eliminating predictive information, satisfying overlap assumptions.
result Shows how to obtain robust causal estimation in the presence of apparent overlap violations.

Paper introduces MVS to detect non-Markovian observations in reinforcement learning.

problem Real-world sensors violate Markov property, leading to suboptimal reinforcement learning performance.
method Uses prediction-based Markov Violation Score (MVS) combining random forest and ridge regression.
result MVS detects non-Markovian structure in observation trajectories, quantifying its impact.

New algorithms control loss and constraints in uncertain, changing environments.

problem Adapting to adversarial constraints in uncertain, changing environments.
method Developed algorithms for constrained MAB problems with optimal rates of regret and positive constraint violation.
result Achieved optimal rates of regret and positive constraint violation under varying degrees of adversariality.

Algorithm ensures privacy while strictly adhering to constraints.

problem Differential privacy with linear constraints that must be strictly followed.
method Developed an algorithm that releases a nearly-optimal solution satisfying constraints with probability 1.
result Achieved nearly optimal performance while preserving privacy and strictly adhering to constraints.

Submodularity is studied for convex risk measures, including Expected Shortfall.

problem Characterizing submodularity in convex risk measures.
method Analyzing submodularity properties of law-invariant coherent risk measures, including Expected Shortfall and Value-at-Risk.
result AES is submodular only when it reduces to ES, and empirical analysis shows AES violations are less frequent than VaR and ES violations.

Canary optimizes VaR-constrained RL problems with a conservative bound using Cantelli's inequality.

problem Optimizing reinforcement learning policies under VaR constraints in dense cost regimes.
method Employing Cantelli's inequality to create a conservative and smooth bound on VaR constraints based on moments of cost returns. Extending trust-region framework for worst-case bounds on policy improvement and constraint violation.
result Canary reliably satisfies VaR constraints with fewest violations and earliest permanent satisfaction, while maintaining reward competitiveness.

Optimal bounds on regret and constraint violation in adversarial COCO.

problem Minimizing regret and cumulative constraint violation in adversarial COCO.
method New surrogate loss function and Follow-the-Regularized-Leader/Online Gradient Descent.
result Achieved optimal O(T)O(\sqrt{T}) bounds on both regret and cumulative constraint violation.