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.
Auto-Surprise automates recommender system selection and optimization.
Unifies 18 definitions of surprise, classifies them into four categories.
Surprise describes a range of phenomena from unexpected events to behavioral responses. We propose a measure of surprise and use it for surprise-driven learning. Our surprise measure takes into account data likelihood as well as the degree of commitment to a belief via the entropy of the belief distribution. We find th…
Surprise-based learning allows agents to rapidly adapt to non-stationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting old observations and integrating them with the new ones. The mod…
DG separates successes and failures by gating updates with advantage and surprisal.
The paper uses Bayesian Surprise to identify unexpected structures in indoor environments.
Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). Recently, surprise has been used as an intrinsic reward that encourages systematic and efficient exploration. We introduce a new definition of surprise and its RL implementation named Variation…
EMIX minimizes surprise in multi-agent reinforcement learning.
The Surprise index assesses autonomous systems' competency in uncertain environments.
Derives time-averaged active inference from control principles.
DE is a new exploration method that limits resource usage based on expected improvement and surprise.
Paper uses surprisal to dynamically allocate computation between fast and slow models.
We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn. We propose a counter-intuitive solution that we call Mutual Information Minimising Exploration (MIME) where an agent learns a latent rep…
Curiosity-driven exploration using Bayesian surprise in latent space.
TradeR uses RL to execute trades in real markets, minimizing surprise and catastrophe.
Model compresses event-like contexts using gated surprise signals.
In this paper, we describe a new surprising example of a fibration of the Clifford torus S3 x S3 in the 7-sphere by great 3-spheres, which is fiberwise homogeneous but whose fibers are not parallel to one another. In particular it is not part of a Hopf fibration. A fibration is fiberwise homogeneous when for any two fi…
The purpose of the present paper is to introduce and explore two surprises that arise when we apply a standard procedure to study the number of finite type invariants of 3-manifolds introduced independently by M. Goussarov and K. Habiro based on surgery on claspers, Y-graphs or clovers, \cite{Gu,Ha,GGP}. One surprise i…
Surprising circles found in Coxeter group boundaries.
Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement …
SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.
A model simulates how different types of traders react to macroeconomic news.
We establish several new stylised facts concerning the intra-day seasonalities of stock dynamics. Beyond the well known U-shaped pattern of the volatility, we find that the average correlation between stocks increases throughout the day, leading to a smaller relative dispersion between stocks. Somewhat paradoxically, t…
Bitcoin reacts negatively to inflation surprises, contrary to belief.
New framework detects near vs. far out-of-distribution samples for AI safety.
Study shows surprising cobordism distances between certain torus knots.
Firms disclosing positive earnings surprises are more likely to disclose ESG information.
We discuss eight new(?) configuration theorems of classical projective geometry in the spirit of the Pappus and Pascal theorems.
A new -NN algorithm using surprisal for robust and interpretable nonparametric learning.
DG improves policy gradients by weighting actions with a sigmoid of advantage and surprisal.
Model financial markets using information theory with a single parameter.
The study explores how agents learn and adapt preferences in dynamic environments.
Early neural networks can be simplified to linear models, revealing surprising simplicity.
In this article we survey, and make a few new observations about, the surprising connection between sub-monoids of mapping class groups and interesting geometry and topology in low-dimensions.
This paper provides an alternative approach to Duffie and Lando [Econometrica 69 (2001) 633-664] for obtaining a reduced form credit risk model from a structural model. Duffie and Lando obtain a reduced form model by constructing an economy where the market sees the manager's information set plus noise. The noise makes…
Breakthrough discoveries and inventions involve unexpected combinations of contents including problems, methods, and natural entities, and also diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, we construct models that pred…
To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so? Here we derive an affirmative answer for a broad class of environments.
The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and…
We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output i…
We show that the Cartesian product of three hereditarily infinite dimensional compact metric spaces is never hereditarily infinite dimensional. It is quite surprising that the proof of this fact (and this is the only proof known to the author) essentially relies on algebraic topology.
In this note we survey some recent results for the Euler equations in compressible and incompressible fluid dynamics. The main point of all these theorems is the surprising fact that a suitable variant of Gromov's -principle holds in several cases.
L. Kauffman conjectured that a particular solution of the Chinese Rings puzzle is the simplest possible. We prove his conjecture by using low-dimensional topology and group theory. We notice also a surprising connection between the Chinese Rings and Habiro moves (related to Vassiliev invariants).
Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.
New framework predicts earnings announcements using press release content, surpassing earnings surprises.
Existence of convex body with prescribed generalized curvature measures is discussed, this result is obtained by making use of Guan-Li-Li's innovative techniques. In surprise, that methods has also brought us to promote Ivochkina's estimates for prescribed curvature equation in \cite{I1, I}.
New insights into simple kernel smoothing reveal surprising asymptotics.
Study hyperbolic geometry to find Fibonacci numbers.
This study shows how monetary uncertainty affects stock market reactions to macroeconomic news.