arXiv research

New method improves deep RL by combining emphatic weightings with replay data.

Unified reinforcement learning objective improves policy performance prediction.

New algorithms improve reinforcement learning stability and performance.

Recently, \citet{SuttonMW15} introduced the emphatic temporal differences (ETD) algorithm for off-policy evaluation in Markov decision processes. In this short note, we show that the projected fixed-point equation that underlies ETD involves a contraction operator, with a $\sqrtγ$-contraction modulus (where $γ$ is the …

Policy gradient methods are widely used for control in reinforcement learning, particularly for the continuous action setting. There have been a host of theoretically sound algorithms proposed for the on-policy setting, due to the existence of the policy gradient theorem which provides a simplified form for the gradien…

We consider the off-policy evaluation problem in Markov decision processes with function approximation. We propose a generalization of the recently introduced \emph{emphatic temporal differences} (ETD) algorithm \citep{SuttonMW15}, which encompasses the original ETD($λ$), as well as several other off-policy evaluation …

PER-ETD improves ETD by reducing variance to polynomial complexity.

New algorithms improve policy evaluation in reinforcement learning.

COF-PAC converges with novel critic and learning method.

Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their objective functions is optimal w.r.t approximating the true value function $V$. Two novel algorithms are proposed to approximate the true value…

This paper studies users' perception regarding a controversial product, namely self-driving (autonomous) cars. To find people's opinion regarding this new technology, we used an annotated Twitter dataset, and extracted the topics in positive and negative tweets using an unsupervised, probabilistic model known as topic …

For a 3-manifold M, McMullen derived from the Alexander polynomial of M a norm on H^1(M, R) called the Alexander norm. He showed that the Thurston norm on H^1(M, R), which measures the complexity of a dual surface, is an upper bound for the Alexander norm. He asked if these two norms were equal on all of H^1(M,R) when …

Paper develops a new multi-agent reinforcement learning algorithm.

The problem of on-line off-policy evaluation (OPE) has been actively studied in the last decade due to its importance both as a stand-alone problem and as a module in a policy improvement scheme. However, most Temporal Difference (TD) based solutions ignore the discrepancy between the stationary distribution of the beh…

Neural networks and linear systems linked, revealing training loss and kernel limitations.

The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimat…

Geometric approach combines asset returns and investor views for better portfolio optimization.

Paper proposes an alternative method to price American options using HJM approach.

We study inference and learning based on a sparse coding model with `spike-and-slab' prior. As in standard sparse coding, the model used assumes independent latent sources that linearly combine to generate data points. However, instead of using a standard sparse prior such as a Laplace distribution, we study the applic…

Quantum machine learning: Adiabatic quantum SVM outperforms classical methods.

We develop a semi-analytic approach to the valuation of auto-callable structures with accrual features subject to barrier conditions. Our approach is based on recent studies of multi-assed binaries, present in the literature. We extend these studies to the case of time-dependent parameters. We compare numerically the s…

Two ML approaches learn local volatility surfaces from option prices, with GP being arbitrage-free.

This paper critiques the Standardized Measurement Approach (SMA) for operational risk and recommends maintaining Advanced Measurement Approach (AMA).

A new Euclidean approach reveals the pentagram map's beauty.

Two approaches extend knowledge distillation to Gaussian Processes, showing relationships to existing methods.

We discuss the relative merits of optimistic and randomized approaches to exploration in reinforcement learning. Optimistic approaches presented in the literature apply an optimistic boost to the value estimate at each state-action pair and select actions that are greedy with respect to the resulting optimistic value f…

Online boosting method improves weak to strong learner.

Predicting potential credit default accounts in advance is challenging. Traditional statistical techniques typically cannot handle large amounts of data and the dynamic nature of fraud and humans. To tackle this problem, recent research has focused on artificial and computational intelligence based approaches. In this …

In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide mo…

Classical approaches to isometric embedding simplified.

Two new methods for option pricing without or with a riskless asset.

An integrated and extendable approach for stress-testing loan portfolios

Bayesian symbolic regression automates model discovery from data.

Paper compares neural network approaches to Optimal Transport.

New approach interprets Nyström for kernel machines with geometric insight.

Common Representation Learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, is receiving a lot of attention recently. Two popular paradigms here are Canonical Correlation Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA based approaches learn …

Two approaches detect EV charging patterns at stations.

Deep learning outperforms classic machine learning in DAS event detection.

Survey on methods to learn graph data representations.

Proposes ACP for efficient inference in noisy-or models.

Paper evaluates CNN-based facial landmark detection methods.

The floating body approach to affine surface area is adapted to a holomorphic context providing an alternate approach to Fefferman's invariant hypersurface measure.

This paper provides a comprehensive benchmark and taxonomy for certifiably robust DNN defenses.

Unified approach for Bayesian optimal experiment design using stochastic gradients.

VB approach for dynamic network models improves efficiency and accuracy.

New approach for prudent risk evaluation using model aggregation.

Study examines the scenario approach for robust optimization, focusing on nonconvex cases.

Study proposes a new approach for deep hedging using artificial market simulations.