On-device research index

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.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

7 results for “PRMs”

On the Impact of Performative Risk Minimization for Binary Random Variablesstat.ML

Paper analyzes impact of PRM on binary random variables and distribution shifts.

problem Impact of performative risk minimization on binary random variables and distribution shifts.

method Formulated two measures of impact, derived explicit formulas for full information, and provided estimators for partial information.

result PRM can have amplified side effects compared to methods that do not model data shift.

performative risk minimization binary random variables distribution shifts performativity statistical estimators

2025-02-04abs ↗pdf ↗

Efficient Reinforcement Learning in Probabilistic Reward Machinesstat.ML

Efficient RL in PRMs with improved regret bound.

problem Reinforcement learning in probabilistic reward machines with non-Markovian rewards.

method Design of an algorithm with a new regret bound of

\widetilde{O}(\sqrt{HOAT} + H^2O^2A^{3/2} + H\sqrt{T})

result Improved regret bound over existing methods, matching lower bound up to a logarithmic factor.

Reinforcement Learning Probabilistic Reward Machines Non-Markovian Rewards Regret Bound Simulation Lemma

2024-08-19abs ↗pdf ↗

Know What You Don't Know: Uncertainty Calibration of Process Reward Modelsstat.ML

Calibrated PRMs improve inference efficiency for LLMs by dynamically adjusting compute budgets.

problem Poor calibration of PRMs leads to overestimation of success probabilities in partial reasoning steps.

method Quantile regression for calibration, instance-adaptive scaling (IAS) framework.

result Calibrated PRMs reduce inference costs while maintaining accuracy, especially on confident problems.

Process reward models Uncertainty calibration Large language models Scaling algorithms Quantile regression

2025-06-11abs ↗pdf ↗

Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learningcs.LG

Paper introduces PRMs to learn non-Markovian stochastic rewards for reinforcement learning.

problem Lack of structured representation for non-Markovian stochastic rewards in reinforcement learning.

method Introduces probabilistic reward machines (PRMs) and presents an algorithm to learn them from decision processes.

result Algorithm proves correct and convergent for learning PRMs from decision processes.

reinforcement learning probabilistic reward machines non-Markovian rewards stochastic rewards reinforcement algorithms

2021-07-09abs ↗pdf ↗

Probabilistic Rule Models as Diagnostic Layers: Interpreting Structural Concept Drift in Post-Crisis Financeq-fin.RM

Proposes PRMs for interpreting financial risk concept drift.

problem Concept drift in high-stakes predictions like credit risk.

method Probabilistic Rule Models (PRMs) using Markov Logic Networks.

result Interpretable rules explain borrower risk changes.

concept drift credit risk interpretability Markov Logic Networks PRMs

2025-10-30abs ↗pdf ↗

On Private and Robust Banditscs.LG

Study on private and robust multi-armed bandits with contaminated heavy-tailed rewards.

problem Private and robust multi-armed bandits with contaminated heavy-tailed rewards.

method Proposed a meta-algorithm with a private and robust mean estimation sub-routine exttt{PRM}.

result Achieved nearly-optimal regret for two heavy-tailed settings.

private bandits robust multi-armed bandits Huber's contamination differential privacy mean estimation

2023-02-06abs ↗pdf ↗

QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisationstat.ML

A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their…

2017-11-28abs ↗pdf ↗