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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

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0111 · Apr 201419922001200920172026
15 results for SPI

Study predicts droughts using ANN models and hydro-meteorological data.

problem Accurate prediction of short and long-term droughts.
method Employed Artificial Neural Network (ANN) models to predict droughts using SPI at different time scales and various hydro-meteorological variables.
result Hydro-meteorological variables significantly improve SPI prediction at different time scales.

Batch Reinforcement Learning (Batch RL) consists in training a policy using trajectories collected with another policy, called the behavioural policy. Safe policy improvement (SPI) provides guarantees with high probability that the trained policy performs better than the behavioural policy, also called baseline in this…

2019-07-11abs ↗pdf ↗

This paper considers Safe Policy Improvement (SPI) in Batch Reinforcement Learning (Batch RL): from a fixed dataset and without direct access to the true environment, train a policy that is guaranteed to perform at least as well as the baseline policy used to collect the data. Our approach, called SPI with Baseline Boo…

2017-12-19abs ↗pdf ↗

New method uses small perturbations to improve representation learning from few labels.

problem Stability issues and label scarcity in representation learning.
method Introduces small-perturbation ideology on representation probability distribution models.
result Proposed models show better performance in clustering compared to baseline methods.

New model solves complex SDEs with high-dimensional spatial and stochastic spaces.

problem Solving SDEs with high-dimensional spatial and stochastic spaces.
method Physics-informed deep generative model (sPI-GeM) combining PI-BasisNet and PI-GeM.
result Scalable solution for high-dimensional SDE problems.

We introduce a new unsupervised anomaly detection ensemble called SPI which can harness privileged information - data available only for training examples but not for (future) test examples. Our ideas build on the Learning Using Privileged Information (LUPI) paradigm pioneered by Vapnik et al. [19,17], which we extend …

2018-05-06abs ↗pdf ↗

This paper is concerned with deformations of Kundt metrics in the direction of type IIIIII tensors and nil-Killing vector fields whose flows give rise to such deformations. We find various characterizations within the Kundt class in terms of nil-Killing vector fields and obtain a theorem classifying algebraic stability …

2019-12-05abs ↗pdf ↗

Policy iteration is a family of algorithms that are used to find an optimal policy for a given Markov Decision Problem (MDP). Simple Policy iteration (SPI) is a type of policy iteration where the strategy is to change the policy at exactly one improvable state at every step. Melekopoglou and Condon [1990] showed an exp…

2019-11-28abs ↗pdf ↗

Proves the Kundt conjecture in arbitrary dimensions, confirming its validity.

problem Determining spacetimes not characterized by scalar polynomial curvature invariants.
method New bilinear map and analysis of covariant derivatives of the Riemann tensor.
result Confirms the Kundt conjecture in arbitrary dimensions, removing regularity assumptions.

New method for PU learning with instance-dependent propensity scores.

problem Learning from positive and unlabeled data with instance-dependent labeling.
method Empirical risk minimization of joint risk function, alternating optimization of posterior probability and propensity score.
result The method achieves comparable or better performance than state-of-the-art methods.