arXiv research

Study applies market microstructure to Cuban informal currency market, finding market makers improve liquidity.

In this paper, we examine the fundamental performance limits of prediction, with or without side information. More specifically, we derive generic lower bounds on the $\mathcal{L}_p$ norms of the prediction errors that are valid for any prediction algorithms and for any data distributions. Meanwhile, we combine the ent…

Paper models limit order book with informed traders and market makers.

Measuring mutual information from finite data is difficult. Recent work has considered variational methods maximizing a lower bound. In this paper, we prove that serious statistical limitations are inherent to any method of measuring mutual information. More specifically, we show that any distribution-free high-confide…

Study sets limits for detecting a subhypergraph in uniform hypergraphs.

A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the futur…

New bounds show limitations of sample-wise information-theoretic generalization.

HLOB predicts mid-price changes in L.O.Bs using deep learning.

Study utility indifference pricing with delayed investment information in a Bachelier model.

Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.

This paper uses information theory to improve risk modeling in big data.

In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite number of samples. We show that the minimum number of samples required by any procedure to recover the correct structure grows as $Ω(m)$ and $Ω…

We study super--replication of European contingent claims in an illiquid market with insider information. Illiquidity is captured by quadratic transaction costs and insider information is modeled by an investor who can peek into the future. Our main result describes the scaling limit of the super--replication prices wh…

Study neural communication systems with bandwidth-limited channels.

In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number of node sequences changes. Second, in disequilibrium networks such as highly bi…

Study on limits of LLM-based multi-agent planning reliability.

OMGD algorithm optimizes online convex optimization with switching costs and delayed gradients.

A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artifici…

Harmonization schemes limit accuracy due to domain information.

New method uses KL-divergence to create non-informative priors for multivariate Gaussian.

Study on Privileged ERM showing limitations and providing capacity analysis.

Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks th…

Study shows how learning and analytical models affect reneging and jockeying in a dual M/M/1 system.

Financial markets, with their vast range of different investment opportunities, can be seen as a system of many different simultaneous games with diverse and often unknown levels of risk and reward. We introduce generalizations to the classic Kelly investment game [Kelly (1956)] that incorporates these features, and us…

New method detects information leakage using approximate Bayes predictor.

Physics-informed methods infer spatial dynamics from static snapshots, but limits exist.

Global graph structure improves GNN performance.

The recent trend for acquiring big data assumes that possessing quantitatively more and qualitatively finer data necessarily provides an advantage that may be critical in competitive situations. Using a model complex adaptive system where agents compete for a limited resource using information coarse-grained to differe…

Dynamic acquisition of features improves predictions with limited data.

RID framework quantifies and regularizes task-relevant knowledge in distillation.

This paper investigates a multi-terminal source coding problem under a logarithmic loss fidelity which does not necessarily lead to an additive distortion measure. The problem is motivated by an extension of the Information Bottleneck method to a multi-source scenario where several encoders have to build cooperatively …

Information-theoretic bounded rationality describes utility-optimizing decision-makers whose limited information-processing capabilities are formalized by information constraints. One of the consequences of bounded rationality is that resource-limited decision-makers can join together to solve decision-making problems …

Variational inference with a factorized Gaussian posterior estimate is a widely used approach for learning parameters and hidden variables. Empirically, a regularizing effect can be observed that is poorly understood. In this work, we show how mean field inference improves generalization by limiting mutual information …

In this paper, we utilize information theory to study the fundamental performance limitations of generic feedback systems, where both the controller and the plant may be any causal functions/mappings while the disturbance can be with any distributions. More specifically, we obtain fundamental $\mathcal{L}_p$ bounds on …

Modeling trading behavior with information signals and limit order books, showing market impact and equilibrium properties.

Shannon's mathematical theory of communication defines fundamental limits on how much information can be transmitted between the different components of any man-made or biological system. This paper is an informal but rigorous introduction to the main ideas implicit in Shannon's theory. An annotated reading list is pro…

Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predic…

The Information Plane theory predicts autoencoders do not compress input information.

To model modern large-scale datasets, we need efficient algorithms to infer a set of $P$ unknown model parameters from $N$ noisy measurements. What are fundamental limits on the accuracy of parameter inference, given finite signal-to-noise ratios, limited measurements, prior information, and computational tractability …

Two parallel samplers enhance image quality in limited denoising steps.

New research on limits of transfer learning, proving key selection and dependence requirements.

Combining the Information Bottleneck model with deep learning by replacing mutual information terms with deep neural nets has proved successful in areas ranging from generative modelling to interpreting deep neural networks. In this paper, we revisit the Deep Variational Information Bottleneck and the assumptions neede…

Proposes a new framework for resource-limited recommendation.

Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full …

Neural network learns low-dimensional polynomials with SGD near information-theoretic limit.

Adversarially-trained models transfer better in limited data scenarios.

Graph transformers outperform graph convolutions by preserving community information.

RLFA estimates misstated monetary fraction with weighted sampling without replacement.