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.
Groups with specific properties have vanishing -Betti numbers.
Survey on handlebody groups and their properties.
Homology growth of specific mapping tori vanishes for certain groups.
New metrics help rebuild trust in Active Learning for industry practitioners.
Cheap permutation tests speed up distribution testing without sacrificing accuracy.
This contribution discusses in what respect Econophysics may be able to contribute to the rebuilding of economics theory. It focuses on aggregation, individual vs collective learning and functional wisdom of the crowds.
We construct invariants of four-dimensional piecewise-linear manifolds, represented as simplicial complexes, with respect to rebuildings that transform a cluster of three 4-simplices having a common two-dimensional face in a different cluster of the same type and having the same boundary. Our construction is based on t…
Cheap methods improve uncertainty in SGD solutions.
Paper proposes a new method to efficiently incorporate curvature information in stochastic optimization.
Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less avail…
We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through "cheap learning" with …
New Bayesian optimization models for efficient material screening.
Private anchors affect how information is communicated and can improve or distort transmission.
Deep-n-Cheap automates deep learning model search for low complexity.
The paper explores the limits of tight PAC-Bayes bounds for cheap models in robust statistics.
A scalable method for heavy-tailed gradients using cheap stochastic sub-processes.
We consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. We analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departu…
We apply numerical methods in combination with finite-difference-time-domain (FDTD) simulations to optimize transmission properties of plasmonic mirror color filters using a multi-objective figure of merit over a five-dimensional parameter space by utilizing novel multi-fidelity Gaussian processes approach. We compare …
In a recent paper, "Why does deep and cheap learning work so well?", Lin and Tegmark claim to show that the mapping between deep belief networks and the variational renormalization group derived in [arXiv:1410.3831] is invalid, and present a "counterexample" that claims to show that this mapping does not hold. In this …
Determinantal point processes (DPPs) are point process models that naturally encode diversity between the points of a given realization, through a positive definite kernel . DPPs possess desirable properties, such as exact sampling or analyticity of the moments, but learning the parameters of kernel through like…
Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning. Randomised value functions (RVF) can be viewed as a promising approach to scaling PSRL. However, we show that most contemporary algorithms combining RVF with neural network f…
We examine the correlation of the limit price with the order book, when a limit order comes. We analyzed the Rebuild Order Book of Stock Exchange Electronic Trading Service, which is the centralized order book market of London Stock Exchange. As a result, the limit price is broadly distributed around the best price acc…
We conclude the construction of the algebraic complex, consisting of spaces of differentials of Euclidean metric values, for four-dimensional piecewise-linear manifolds. Assuming that the complex is acyclic, we investigate how its torsion changes under rebuildings of the manifold triangulation. First, we write out form…
We present a simple one-parameter model for spatially localised evolving agents competing for spatially localised resources. The model considers selling agents able to evolve their pricing strategy in competition for a fixed market. Despite its simplicity, the model displays extraordinarily rich behavior. In addition t…
New results on homology torsion growth for various groups.
New framework allows selective removal of stale data in option calibration.
Method generates adversarial examples to improve classifier robustness.
New algorithm radVI improves variational inference by optimizing radial profiles.
In this brief we investigate the generalization properties of a recently-proposed class of non-parametric activation functions, the kernel activation functions (KAFs). KAFs introduce additional parameters in the learning process in order to adapt nonlinearities individually on a per-neuron basis, exploiting a cheap ker…
Study builds ML models to predict fuel properties accurately.
CJE calibrates cheap LLM judges against an oracle, achieving high accuracy at a fraction of the cost.
COMMOD debiases models with minimal and interpretable changes.
Multitask Gaussian process regression reduces data generation costs for molecular property prediction.
Efficient neural networks compute various differential operators cheaply.
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community. Ho…
The Cheap Gradient Principle (Griewank 2008) --- the computational cost of computing the gradient of a scalar-valued function is nearly the same (often within a factor of ) as that of simply computing the function itself --- is of central importance in optimization; it allows us to quickly obtain (high dimensional) …
Submodular function maximization finds application in a variety of real-world decision-making problems. However, most existing methods, based on greedy maximization, assume it is computationally feasible to evaluate F, the function being maximized. Unfortunately, in many realistic settings F is too expensive to evaluat…
Adapts EGOP to multi-class setting and proposes a simple rough estimator.
Factorized information criterion (FIC) is a recently developed approximation technique for the marginal log-likelihood, which provides an automatic model selection framework for a few latent variable models (LVMs) with tractable inference algorithms. This paper reconsiders FIC and fills theoretical gaps of previous FIC…
In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function . Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to may be obtainable. For example, the exp…
In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of classifier chains algorithms that are able to change label order of the chain without rebuilding the entire model. Such modes allows anticipating the instance-sp…
Dropout regularization of deep neural networks has been a mysterious yet effective tool to prevent overfitting. Explanations for its success range from the prevention of "co-adapted" weights to it being a form of cheap Bayesian inference. We propose a novel framework for understanding multiplicative noise in neural net…
We introduce a general framework to handle structured models (sparse and block-sparse with possibly overlapping blocks). We discuss new methods for their recovery from incomplete observation, corrupted with deterministic and stochastic noise, using block- regularization. While the current theory provides promis…
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. W…
Dead-Direction Signatures (DDS) provide a cheap, closed-form spectral reading of a network's singular complexity.
New theory explains how strong models can learn from weak ones.
PROBE optimizes best-arm identification with cheap proxies, improving sample complexity.
A new method solves optimization problems on the generalized Stiefel manifold using random estimates of B.