AI agents on social networks rarely engage in extended conversations.
problem Understanding the persistence of interactions in AI-agent social networks.
method Analysis of Moltbook, a social network of AI agents, using interaction half-life and spectral tests.
result Most comments on Moltbook receive a direct reply within seconds, indicating a ``fast response or silence'' regime.
This paper demonstrates the flaws of co-persistence theory proposed by Bollerslev and Engle (1993) which cause the theory can hardly be applied. With the introduction of the half-life of decay coefficient as the measure of the persistence, and both the weak definition of persistence and co-persistence in variance, this…
The correlation coefficient between stocks depends on price history and includes information on hierarchical structure in financial markets. It is useful for portfolio selection and estimation of risk. I introduce the Life Time of Correlation between stocks prices to know how far we should investigate the price history…
AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
problem Excess returns from AI-driven investment strategies diminish at scale due to signal crowding and erosion.
method Theoretical model and empirical validation using SEC Form 13F filings and hedge fund return dynamics.
result The alpha half-life of signals decreases significantly with AI adoption, leading to diminishing returns.
In this paper we provide compelling evidence of cyclical mean reversion and multiperiod stock return predictability over horizons of about 30 years with a half-life of about 15 years. This implies that the US stock market follows a long-term rhythm where a period of above average returns tends to be followed by a perio…
Background: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, excretion prediction models still have limited accuracy. Aim: This study aims to construct an integrated transfer learning and multitask learning approach for deve…
Model calculates optimal trading time for derivatives orders.
problem Balancing execution costs and market risks in large order execution.
method Time Is Money model using Bachelier model and central limit order book.
result Demonstrates a continuous-time Arrival Price framework.
Enhances risk model with new statistical factors.
problem Missing information in existing risk models.
method Maximum likelihood estimation to refine and add new factors.
result Captures structure missed by original model.
Study shows how sentiment shocks affect equity markets, revealing asymmetries and state-dependent effects.
problem Understanding how sentiment shocks propagate through equity markets and their impact on different investor groups.
method Used four independent proxies with sign-aligned kappa-rho parameters, calibrated a structural model to link sentiment to returns.
result A one standard deviation sentiment shock has a 1.06 basis point impact, with effects amplified over 11.2 months and concentrated in retail-tilted stocks.
Model analyzes Proof-of-Stake network dynamics and speculative capital effects on token prices.
problem Understanding and managing price dynamics in Proof-of-Stake networks.
method Developed an open-economy macroeconomic model to analyze Proof-of-Stake dynamics and speculative capital effects.
result Speculative capital can shift staked-token ownership, potentially improving consensus decentralization.
Prediction markets and crypto options show persistent pricing gaps.
problem Comparing prediction markets and crypto options for identical payoffs.
method Comparing Polymarket Yes prices with Binance call option prices.
result Mean pricing gap of 5.6 percentage points across 214 hourly observations.
Paper evaluates deadline-ILS on insider trading contracts, finding it distinguishes signals from noise.
problem Deadlines in insider trading contracts and information leakage detection.
method Empirical evaluation using FFIC dataset, hazard-rate estimation, cross-market wallet analysis.
result Deadline-ILS distinguishes signal from proxy artefact, with a significant shift in magnitude.
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions
Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless featur…
The study analyzes river water quality using statistical and machine learning methods.
problem Analyzing spatio-temporal dynamics of dissolved oxygen in the River Thames.
method Superstatistical methods and machine learning (e.g., Light Gradient Boosting Machine, Informer model).
result The Informer model outperforms others in long-term dissolved oxygen concentration forecasting.
A new method detects interactions in neural networks using topological analysis.
problem Detecting interactions between input features in neural networks.
method Topological analysis of neural network connectivity to quantify interaction strength.
result The PID algorithm outperforms state-of-the-art baselines in interaction detection tasks.
iKF method uncovers complex variable interactions for scientific discovery.
problem Limited interpretability of existing models in decision-making applications.
method Iterative Kings' Forests (iKF) method to uncover multi-order interactions.
result iKF provides strong interpretive power for explainable modeling.
A graph neural network detects beneficial feature interactions for recommender systems.
problem Feature interactions are crucial but not all are beneficial for recommendation accuracy.
method Graph neural network with L0 activation regularization for edge prediction.
result The model outperforms baselines and automatically identifies beneficial feature interactions.
We describe and extract time-ordered multibody interactions from complex systems.
problem Complex systems with temporal and multibody dependencies.
method Decompose multivariate Markov chains into time-ordered multibody interactions. Algorithm to extract interactions from data. Measure complexity of interaction ensembles.
result Robust and efficient algorithm to infer time-ordered multibody interactions from data.
Dropout regularizes against high-order interactions by canceling interaction rates.
problem Overfitting to high-order interactions in neural networks.
method Analyzes Dropout through the lens of interaction effects, showing how it effectively cancels out the probability of surviving interactions of different orders.
result Dropout regularizes against high-order interactions by effectively canceling out the probability of surviving interactions of different orders.
Understanding how features interact with each other is of paramount importance in many scientific discoveries and contemporary applications. Yet interaction identification becomes challenging even for a moderate number of covariates. In this paper, we suggest an efficient and flexible procedure, called the interaction …
Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent latent functions, all current MTGPs including the salient linear model of coregionalization (LMC) and convolution frameworks cannot effectively…
SocialInteractionGAN generates realistic human interactions from low-dimensional data.
problem Generating realistic human interactions from limited data.
method Adversarial architecture with a recurrent encoder-decoder generator and dual-stream discriminator.
result SocialInteractionGAN produces high-quality action sequences of interacting people.
A new method detects interactions in machine learning models.
problem Interpreting non-linear and interaction effects in machine learning models.
method Regional effect plots with implicit interaction detection.
result The method quantifies and interprets feature effects reliably, less confounded by interactions.
xDeepInt learns both vector-wise and bit-wise feature interactions.
problem Learning feature interactions for CTR prediction and recommendation.
method Polynomial Interaction Network (PIN) architecture with subspace-crossing mechanism.
result xDeepInt outperforms state-of-the-art models in CTR prediction and recommendation.
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions. Specifically, we propose a graph neural network that jointly predicts the discrete interaction modes and 5-second future trajectories for all agents in the scene. Our model infers an inte…
Most existing knowledge graphs suffer from incompleteness, which can be alleviated by inferring missing links based on known facts. One popular way to accomplish this is to generate low-dimensional embeddings of entities and relations, and use these to make inferences. ConvE, a recently proposed approach, applies convo…
This study explores how feature graphs enhance GNNs' performance in modeling interactions.
problem Improving GNNs' ability to model feature interactions effectively.
method Investigates feature graphs and their importance in GNNs, using experiments and theoretical support.
result Edges between interacting features are crucial for GNNs, while non-interaction edges can degrade performance.
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a…
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structu…
Paper introduces IC-index to evaluate interaction prediction methods.
problem Evaluate interaction prediction methods using IC-index.
method IC-index measures interaction direction prediction performance.
result IC-index complements existing prediction performance estimators.
This paper uses Gaussian mixtures to mimic interactions in large language models.
problem Simulating interactions in large language models (LLMs) is computationally expensive.
method Developed an interacting Gaussian mixture model (GMM) system to mimic LLM interactions.
result The interacting Gaussian mixture model system can generate, exchange, and update data and parameters efficiently.
Study reveals self-attention's role in learning and generalizing interactions.
problem Understanding self-attention's theoretical role in neural architectures.
method Interacting entities analysis, including multi-agent RL and genetic sequences.
result Self-attention efficiently represents, learns, and generalizes pairwise interactions.
Archipelago provides interpretable explanations of feature interactions in machine learning models.
problem Interpreting the impact of feature interactions on predictions in machine learning models.
method Archipelago is a novel framework for extracting and attributing feature interactions in a scalable and interpretable manner.
result Archipelago provides significantly more interpretable explanations of feature interactions than comparable methods.
Automated tests detect interactions in unstructured data.
problem Detecting interactions between latent variables in low-dimensional systems.
method Derive two interaction tests based on pairwise interventions and integrate them into an active learning pipeline.
result Tests can identify more known biological interactions than random search and standard active learning baselines.
We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of exist…
PIN models feature interactions using a neural network that mimics decision trees.
problem Modeling feature interactions in tabular data for predictive modeling.
method Tree-like Pairwise Interaction Network (PIN) architecture that captures pairwise feature interactions through a shared feed-forward neural network.
result PIN outperforms traditional and modern neural networks benchmarks in predictive accuracy.
The paper explains DNNs by quantifying interactions among input variables.
problem Understanding and explaining the complex behavior of deep neural networks.
method The paper defines and quantifies the significance of interactions among multiple input variables using the Shapley value.
result The proposed method effectively explains the behavior of DNNs by assigning attribution values to input variables.
Modeling continuous movement of entities in latent space for interaction timing.
problem Analyzing timing and frequency of instantaneous interactions.
method Latent position model with continuous trajectories.
result Individual trajectories estimated from interaction data.
The paper improves dropout's utility by reducing interactions in deep neural networks.
problem Over-fitting problem in deep learning.
method Game-theoretic interactions analysis and interaction loss.
result Interaction loss improves dropout's utility and boosts DNN performance.
New model quantifies interactions' role in real-world phenomena.
problem Understanding the role of interactions in real-world phenomena.
method Interactive Mixed Membership Stochastic Block Model (IMMSBM).
result Interactions significantly improve model predictive power.
MEI model improves knowledge graph completion by efficiently modeling interactions between embeddings.
problem Efficiently modeling interactions between knowledge graph embeddings to predict missing links.
method MEI divides embeddings into partitions and uses Tucker and block term formats to model interactions efficiently.
result Achieves state-of-the-art performance on link prediction tasks.
Paper proposes FINN for better CTR prediction by modeling feature interactions.
problem Improving CTR prediction in advertising and recommendation systems.
method Proposes Feature Interaction based Neural Network (FINN) to model feature interactions.
result FINN outperforms other deep models in CTR prediction tasks.
We discuss possible relationships between geometric and topological interactions on one side and physical interactions on the other side.
Perfect clustering achieved in hypergraphs with enough interactions.
problem Complexity and lack of tractable models for analyzing hypergraphs.
method Introduced an interaction hypergraph model for analyzing hypergraphs, defined latent embeddings, and analyzed spectral estimators.
result A spectral estimate of interaction latent positions can achieve perfect clustering with enough interactions.
SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.
problem Non-additivity of hazard and survival functions limits standard additive explanation methods.
method SurvFD decomposes higher-order effects into time-dependent and time-independent components, extending Shapley interactions to time-indexed functions.
result SurvFD and SurvSHAP-IQ offer a new perspective on survival explanations, explicitly characterizing feature interactions.
iLOCO measures feature interactions without assumptions, providing statistical inference.
problem Lack of methods to statistically infer feature interactions.
method iLOCO metric and LOCO inference for distribution-free, efficient computation.
result First inferential approach to detecting feature interactions.