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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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48 results for What You See Is What You Get (WYSIWYG)

New DP training ensures models behave similarly at training and test time.

problem Standard SGD training leads to inconsistent model behavior at training and test time.
method Differentially-Private (DP) training ensures WYSIWYG property through distributional generalization.
result DP training guarantees high-level WYSIWYG property, improving model robustness and privacy.

This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this materia…

2018-02-05abs ↗pdf ↗

New definition reveals encoding explanations that retain predictive power.

problem Challenges in evaluating and identifying encoding explanations.
method Developed a definition of encoding based on conditional dependence.
result Existing evaluation scores do not rank non-encoding explanations correctly, but STRIPE-X does.

The hidden tail of empirical distributions is analyzed using extreme value theory.

problem Understanding the bias between in-sample mean and true statistical mean for large nn.
method Extreme value theory applied to empirical distributions and their moments.
result The hidden moment of order 0 for power law distributions follows an exponential distribution with expectation 1/n1/n.

With the dawn of the Big Data era, data sets are growing rapidly. Data is streaming from everywhere - from cameras, mobile phones, cars, and other electronic devices. Clustering streaming data is a very challenging problem. Unlike the traditional clustering algorithms where the dataset can be stored and scanned multipl…

2019-02-07abs ↗pdf ↗

The paper explores the limits of tight PAC-Bayes bounds for cheap models in robust statistics.

problem The challenge of obtaining meaningful bounds on the error of learning algorithms without prior assumptions.
method Investigates tight PAC-Bayes bounds for robust models with minimal cost.
result Demonstrates the limits of obtaining tight PAC-Bayes bounds for cheap models.

Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results.…

2017-11-29abs ↗pdf ↗

Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and s…

2016-06-10abs ↗pdf ↗

What return should you expect when you take on a given amount of risk? How should that return depend upon other people's behavior? What principles can you use to answer these questions? In this paper, we approach these topics by exploring the consequences of two simple hypotheses about risk. The first is a common-sense…

2002-01-18abs ↗pdf ↗

Suppose you have one unit of stock, currently worth 1, which you must sell before time TT. The Optional Sampling Theorem tells us that whatever stopping time we choose to sell, the expected discounted value we get when we sell will be 1. Suppose however that we are able to see aa units of time into the future, and ba…

2016-01-22abs ↗pdf ↗

We consider two well known constructions of link invariants. One uses skein theory: you resolve each crossing of the link as a linear combination of things that don't cross, until you eventually get a linear combination of links with no crossings, which you turn into a polynomial. The other uses quantum groups: you con…

2010-02-02abs ↗pdf ↗

We construct a pair of transverse genuine laminations on an atoroidal 3-manifold admitting transversely orientable uniform 1-cochain. The laminations are induced by the uniform 1-cochain and they are indeed the "straightening" of the coarse laminations defined in [Ca], by using minimal surface techniques. Moreover, whe…

2003-04-07abs ↗pdf ↗

F.: Good morning Hermann, I would like to talk with you about infinitesimals. G.: Tell me Pierre. F.: I'm fed up of all these slanders about my attitude to be non rigorous, so I've started to study nonstandard analysis (NSA) and synthetic differential geometry (SDG). G.: Yes, I've read something ... F.: Ok, no problem …

2009-07-10abs ↗pdf ↗

Have you ever looked at a machine learning classification model and thought, I could have made that? Well, that is what we test in this project, comparing XGBoost trained on human engineered features to training directly on data. The human engineered features do not outperform XGBoost trained di- rectly on the data, bu…

2016-09-04abs ↗pdf ↗

In this report, we talked about a new quantitative strategy for choosing the optimal(s) stock(s) to trade. The basic notions are generally very known by the financial community. The key here is to understand 1) the standard score applied to a sample and 2) the correlation factor applied to different time series in real…

2012-12-30abs ↗pdf ↗

Take a torus with a Riemannian metric. Lift the metric on its universal cover. You get a distance which in turn yields balls. On these balls you can look at the Laplacian. Focus on the spectrum for the Dirichlet or Neumann problem. We describe the asymptotic behaviour of the eigenvalues as the radius of the balls goes …

2002-02-28abs ↗pdf ↗

Study uses chatbot to understand users' needs for ML model explanations.

problem Lack of understanding of user needs for model explanations.
method Developed a conversational system (dr_ant) to collect user questions about a machine learning model trained on Titanic data.
result Collected a corpus of 1000+ dialogues to identify common user questions.

The paper defines and studies the category of Z-graded manifolds, including their intrinsic structure and formal properties.

problem Understanding the categorical properties and intrinsic structure of Z-graded manifolds.
method Describing local models, explaining formality, and formulating analogues of theorems.
result Proper definitions of objects and morphisms in the category of Z-graded manifolds, and formulation of Batchelor's theorem.

This paper takes stock of megaproject management, an emerging and hugely costly field of study. First, it answers the question of how large megaprojects are by measuring them in the units mega, giga, and tera, concluding we are presently entering a new "tera era" of trillion-dollar projects. Second, total global megapr…

2014-08-29abs ↗pdf ↗

Paper proposes a modified uncertainty sampling method to speed up preference learning from noisy humans.

problem Learning preferences from humans with limited queries and noisy responses.
method Modified uncertainty sampling using expected output value to speed up preference learning.
result The modified method outperforms the baseline uncertainty sampling in preference learning.

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning. Inferring an agent's internal model is a crucial ingredient in social interactions (theory of mind), for imitation learning, and for interpreting neural activities of behaving agen…

2018-05-24abs ↗pdf ↗

Explains agent behavior through intended outcomes in reinforcement learning.

problem Proving impossibility of general post-hoc explanations in reinforcement learning.
method Derives local explanations based on intention for Q-function approximations, proving consistency with learned Q-values.
result Demonstrates the necessity of collecting information during training for accurate explanations.

The paper introduces a new insurance pricing model based on driving mileage.

problem Weak link between insurance premiums and mileage, leading to overdriving and accidents.
method Developed a Pay-As-You-Drive insurance pricing model using a counting process and non-homogeneous Poisson distribution.
result The model provides theoretical results for better insurance pricing based on driving behavior.

As technology become more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assura…

2017-08-01abs ↗pdf ↗

We reconsider the su(3) link homology theory defined by Khovanov in math.QA/0304375 and generalized by Mackaay and Vaz in math.GT/0603307. With some slight modifications, we describe the theory as a map from the planar algebra of tangles to a planar algebra of (complexes of) `cobordisms with seams' (actually, a `canopo…

2006-12-26abs ↗pdf ↗

Active-memory mechanisms can replace self-attention in Transformers, but optimal results often require both.

problem Replacing self-attention with active-memory mechanisms in Transformers.
method Evaluation of various active-memory mechanisms in a Transformer model.
result Active-memory mechanisms can achieve comparable results to self-attention for language modeling, but optimal results are often achieved by combining both mechanisms.

We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…

2010-07-24abs ↗pdf ↗