arXiv research

Wittgenstein's Rule Following evolves datasets by extrapolating structural descriptors.

Pareto's 80/20 rule follows a Gaussian distribution with twice the mean standard deviation.

Paper proves Jeffrey's update rule minimizes relative entropy.

Abstraction and realization are bilateral processes that are key in deriving intelligence and creativity. In many domains, the two processes are approached through rules: high-level principles that reveal invariances within similar yet diverse examples. Under a probabilistic setting for discrete input spaces, we focus …

Develops a new model for controllable and realistic traffic simulation.

Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. In this paper we propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations through a backtest engine. We…

Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our selection rules are based on the dual residual or the primal-du…

Axiomatizes the bid-ask market maker's quoting rule

Given a smooth distribution $\mathscr{D}$ of $m$-dimensional planes along a smooth regular curve $γ$ in $\mathbb{R}^{m+n}$, we consider the following problem: to find an $m$-dimensional rank-one submanifold of $\mathbb{R}^{m+n}$, that is, an $(m-1)$-ruled submanifold with constant tangent space along the rulings, such …

We consider the setting of sequential prediction of arbitrary sequences based on specialized experts. We first provide a review of the relevant literature and present two theoretical contributions: a general analysis of the specialist aggregation rule of Freund et al. (1997) and an adaptation of fixed-share rules of He…

A quantitative check of weak efficiency in US dollar/German mark exchange rates is developed using high frequency data. We show the existence of long term return anomalies. We introduce a technique to measure the available information and show it can be profitable following a particular trading rule.

The paper examines how macroeconomic control tools lost effectiveness, leading to a 'dark ages' period.

We study the problem of selling an asset near its ultimate maximum in the minimax setting. The regret-based notion of a perfect stopping time is introduced. A perfect stopping time is uniquely characterized by its optimality properties and has the following form: one should sell the asset if its price deviates from the…

New method improves model explainability and accuracy with low computational cost.

Signature kernel scoring rule improves weather forecasting by capturing temporal and spatial dependencies.

This paper solves aggregation of Pareto optimal models by using Bayesian priors and weighted averaging.

Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.

This paper analyzes voter coalitions in MakerDAO's decentralized governance.

The paper proposes multicalibration to improve matching in graphs with imperfect predictors.

We present a strikingly simple proof that two rules are sufficient to automate gradient descent: 1) don't increase the stepsize too fast and 2) don't overstep the local curvature. No need for functional values, no line search, no information about the function except for the gradients. By following these rules, you get…

Neural operators learn to solve LQ MFGs efficiently in infinite dimensions.

Optimal strategy found for identifying best arm in bandits with small gap.

This paper uses Bayesian models to analyze CTA returns across short and long-term trends.

The paper explores how to select data points for optimal learning performance.

We investigate the relationship between market efficiency of rice futures transaction in Osaka and the Japanese government intervention in rice distributions by directly buying and selling rice during the interwar period, from the middle 1910s to 1939, considering the context of "discretion versus rules." We use a time…

New rule reduces exploration regret to logarithmic, improving bad episode handling.

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independ…

This paper proposes a new method to approximate posterior distributions using generative neural networks trained via scoring rule minimization.

This thesis consists of two independent parts: random matrices, which form the first one-third of this thesis, and machine learning, which constitutes the remaining part. The main results of this thesis are as follows: a necessary and sufficient condition for the inverse moments of $(m,n,β)$-Laguerre matrices and compo…

The paper proves the existence of a special Kähler metric on a minimal ruled surface.

Proposes a fuzzy rule-based method for data visualization.

Assume (1) asset returns follow a stochastic multi-factor process with time-varying conditional expectations; (2) investments are linear functions of factors. This paper calculates asymptotic joint moments of the logarithm of investor's wealth and the factors. These formulas enable fast computation of a wide range of i…

A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph representation of the optimization problem. The definitive relation between chordal extension and the performance of the optimization algorithm that uses the extension …

While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what exten…

Study of symplectomorphisms on ruled surfaces under circle actions.

In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interp…

Using a model of wealth distribution where traders are characterized by quenched random saving propensities and trade among themselves by bipartite transactions, we mimic the enhanced rates of trading of the rich by introducing the preferential selection rule using a pair of continuously tunable parameters. The biparti…

We present a simple agent-based model of a financial system composed of leveraged investors such as banks that invest in stocks and manage their risk using a Value-at-Risk constraint, based on historical observations of asset prices. The Value-at-Risk constraint implies that when perceived risk is low, leverage is high…

EP learns like BPTT but with local weight updates.

New bounds prevent degradation in high-dimensional signal estimation.

Adaptive learning rate improves FTRL's performance in online learning.

New approach tackles decision-making under predictions that shape outcomes.

Regular subgroups of SL3(R) are identified and ruled out.

Survey of neurosymbolic AI methods for reasoning over knowledge graphs.

The transition of several East and Central European countries and the countries of the Former Soviet Union from the socialist economic system to the capitalist one is studied. A recently developed microeconomic model for the personal income distribution and its evolution and a simple functional relationship between the…

A new framework for paired-sample testing in high-dimensional data.

The aim of this paper is to compare the performances of the optimal strategy under parameters mis-specification and of a technical analysis trading strategy. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. For both strategies, we prov…

Trans-Ising combines auxiliary datasets to estimate high-dimensional Ising models.