Defines reputation as a time-based index of mined opinions.
problem Lack of formal definitions for reputation.
method Formalizes reputation as a time-based index of mined opinions, validating against informal notions.
result Quantifies unexpressed positive sentiment.
Time-based model improves recommendation performance.
problem Efficiently handling sequences of varying lengths in recommendation systems.
method Embedding layer and TSL attention-like mechanism with inner products.
result Model outperforms complex models on Taobao User Behavior dataset.
We introduce a trade strategy representation theorem for performance measurement and portable alpha in high frequency trading, by embedding a robust trading algorithm that describe portfolio manager market timing behavior, in a canonical multifactor asset pricing model. First, we present a spectral test for market timi…
Hybrid approach improves reinforcement learning efficiency.
problem Improving sample efficiency in reinforcement learning.
method Approximate model from Model-Free RL, use Mean First Passage Times for reachability, design new algorithms.
result Hybrid approaches converge with fewer iterations and samples.
The paper solves a dynamic portfolio optimization problem using Riccati transformation.
problem Dynamic stochastic portfolio optimization involving expected and intertemporal utilities.
method Solving a fully nonlinear HJB equation through Riccati transformation into a quasi-linear parabolic equation.
result The numerical method based on semi-implicit scheme converges at second order.
LLMs struggle to outperform markets over long periods and diverse stocks.
problem Overstated effectiveness of LLM-based investing strategies due to biases.
method FINSABER framework for systematic backtests over two decades and 100+ symbols.
result Previously reported LLM advantages deteriorate significantly under broader evaluation.
Paper proposes a new reinforcement learning framework for cryptocurrency market making.
problem Improving profit and stability in cryptocurrency market making.
method Event-based reinforcement learning environment, training two policy-based agents with neural networks and various reward functions.
result Improved profit and stability demonstrated over time-based approach.
Paper introduces timing-based adversarial attacks on DRL-based navigation systems.
problem Vulnerability of DRL-based navigation systems to adversarial attacks.
method Timing-based adversarial strategies using physical noise patterns.
result Adversarial timing attacks significantly degrade DRL-based navigation performance.
An average instantaneous cross-correlation function is introduced to quantify the interaction of the financial market of a specific time. Based on the daily data of the American and Chinese stock markets, memory effect of the average instantaneous cross-correlations is investigated over different price return time inte…
We study the computational capacity of a model neuron, the Tempotron, which classifies sequences of spikes by linear-threshold operations. We use statistical mechanics and extreme value theory to derive the capacity of the system in random classification tasks. In contrast to its static analog, the Perceptron, the Temp…
Study shows naive recommendation models are flawed due to user feedback.
problem Flaws in naive recommendation models due to user feedback.
method Proposed a model with heterogeneous user preferences and proved the inconsistency of naive estimators.
result Consistent estimators are efficient in the presence of myopic agents.
The paper studies how convex shapes evolve over time based on their curvature.
problem Understanding how convex shapes change over time based on their curvature.
method Analyzes the evolution of strictly convex hypersurfaces in \(\mathbb{R}^{n+1}\) moving with a specific speed.
result The flow converges to a round sphere after rescaling for \(α > \frac{1}{n+2}\), and to an ellipsoid for \(α = \frac{1}{n+2}\).
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
An emerging way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph. Nevertheless, though state-of-the-art graph-based methods have been successful for many learning tasks, they do not consider time-evolving sig…
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
A machine learning model manages portfolio risk in high dimensions.
problem Managing risk in high-dimensional financial portfolios.
method A supervised learning approach using replicating martingales and polynomial/neural network bases.
result The model outperforms naive Monte Carlo and least-squares Monte Carlo methods.
The study proposes using TD error for selecting σ in Q(σ, λ).
problem Selecting the value of σ in Q(σ, λ) based on state characteristics.
method TD error as a heuristic for selecting σ.
result TD error can effectively select σ based on state characteristics.
Unsupervised method learns universal embeddings for variable-length multivariate time series.
problem Challenges in learning representations for time series data due to varying lengths and sparse labeling.
method Combines causal dilated convolutions with triplet loss for time-based negative sampling.
result Demonstrates quality, transferability, and practicability of learned representations.
Proposes a deep spectral Q-learning for mobile health data.
problem Personalized treatment assignment for patients with time-varying covariates.
method Integrates PCA with deep Q-learning for mixed frequency data.
result Mean return converges to optimal under estimated optimal policy.
We present analytical investigations of a multiplicative stochastic process that models a simple investor dynamics in a random environment. The dynamics of the investor's budget, x(t), depends on the stochasticity of the return on investment, r(t), for which different model assumptions are discussed. The fat-tail d…
Study shows cryptocurrency market efficiency changes over time.
problem Measuring cryptocurrency market efficiency over time.
method Used a generalized least squares-based time-varying model to measure efficiency without sample size dependence.
result Bitcoin's market efficiency is higher than Ethereum's over most periods.
Dynamic retirement glidepaths evolve over time based on some measure such as the retiree's funded status or current market valuations. Conversely, static glidepaths are fixed at a starting point and selected under the assumption that they will not change. In practice, new static glidepaths may be derived periodically m…
New approach for uninformed investors to optimize execution costs.
problem Optimizing execution costs for new investors with imperfect initial knowledge.
method Iterative derivation of OLS estimates of market parameters.
result Dynamic adjustment of trading strategies based on evolving market parameters.
In this work we study drawdowns and drawups of general diffusion processes. The drawdown process is defined as the current drop of the process from its running maximum, while the drawup process is defined as the current increase over its running minimum. The drawdown and the drawup are the first hitting times of the dr…
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
Agent optimizes risky asset trading times based on Prospect Theory.
problem Optimizing speculative trading times with transaction costs.
method Formulated as a sequential optimal stopping problem, characterized the solution.
result Trading patterns influenced by preference and market friction.
New method improves conditional covariance estimation using targeted groups of assets.
problem Improving conditional covariance estimation in financial time series.
method Introduces targeting in BEKK and DCC models for financial time series analysis.
result Encouraging results from empirical case study, especially with fewer assets.
Recent studies in the field of human vision science suggest that the human responses to the stimuli on a visual display are non-deterministic. People may attend to different locations on the same visual input at the same time. Based on this knowledge, we propose a new stochastic model of visual attention by introducing…
The study compares different models for predicting factor premiums and finds neural networks perform better but have unstable weights.
problem Predicting and timing the CMA factor premium using machine learning models.
method Compared regression models (OLS, Ridge, Random Forest, Neural Network) and tested factor timing strategies.
result Neural networks outperform linear models in explaining factor premium variance, but weights are unstable.
Paper proposes E/PD-Control for better neural network training.
problem Training efficiency and robustness of CNNs in online data flows.
method E/PD-Control combines feedback PD controller with exponential signal.
result Better learning efficiency and robustness demonstrated experimentally.
STAD adapts models to evolving time-based data shifts.
problem Gradual distribution shifts over time challenge existing test-time adaptation methods.
method Bayesian filtering method that learns time-varying dynamics in hidden features.
result STAD excels in handling small batch sizes and label shift on real-world data.
The paper suggests asset prices follow physical laws, allowing for accurate price movement forecasts.
problem Predicting extreme price movements in financial markets.
method Modeling asset price dynamics as a harmonic oscillator and applying the principle of stationary action.
result The theory can make accurate forecasts of price movements during market crashes and specific price displacements at other times.
We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…
Paper revisits FRAME model, explaining instability and proposing a new metric.
problem Unstable training energy in FRAME model.
method Theoretical analysis using particle physics, proposing a new Wasserstein distance.
result Proposed Wasserstein distance stabilizes energy dissipation and maintains statistical consistency.
The paper optimizes retirement spending considering habit formation and pension income.
problem Optimizing lifetime consumption under habit formation and pension income.
method Time inhomogeneous stochastic control problem with numerical solution using finite difference scheme.
result Consumption patterns change over time based on habit and optimal strategy.
Many users in online social networks are constantly trying to gain attention from their followers by broadcasting posts to them. These broadcasters are likely to gain greater attention if their posts can remain visible for a longer period of time among their followers' most recent feeds. Then when to post? In this pape…
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
Proposes ABIDES-Gym for financial markets simulation.
problem Training multi-agent systems in financial markets.
method Wrapping DEMAS into OpenAI Gym framework.
result Developed two financial markets OpenAI Gym environments.
Bayesian structure learning improved using GFlowNets.
problem Inferring Bayesian network structure from data.
method Using Generative Flow Networks (GFlowNets) for approximating posterior DAG distributions.
result DAG-GFlowNet provides an accurate approximation of the posterior over DAGs.
Online detection of abrupt changes in high-dimensional data streams.
problem Detecting abrupt changes in high-dimensional, streaming data with multiple subspaces.
method Dynamic sparse subspace learning approach with multiple structural change-point model, Bayesian information criterion for penalty coefficients selection, and Pruned Exact Linear Time algorithm.
result Effectiveness demonstrated through simulation and real gesture data studies.
SMURF-THP improves Transformer Hawkes process models by providing uncertainty quantification.
problem Uncertainty quantification for Transformer Hawkes process predictions.
method Score matching for learning the score function of event arrival times.
result SMURF-THP outperforms likelihood-based methods in confidence calibration.
A new clustering evaluation index based on density estimation.
problem Improving internal clustering evaluation indices.
method The index is a mixture of Ambiguous and Similarity sub-indices, calculated using density estimation.
result The new index significantly outperforms other internal clustering evaluation indices.
Self-Organizing Maps provide quick flood predictions for real-time decision making.
problem Computational demand of physically-based overland flow models limits their use in real-time applications.
method Developed a flood-simulation specific SOM using cellular automata flood model results and synthetic DEM.
result SOMs can generate accurate water depth and flood extent results in a short time.
Study on symmetric operators on non-compact manifolds, focusing on their index modulo 2.
problem Investigating elliptic operators with a specific symmetry and their index modulo 2.
method Analysis of Callias-type operators on non-compact manifolds, establishing mod 2 versions of index theorems.
result Established mod 2 versions of the Gromov-Lawson relative index theorem, Callias index theorem, and Boutet de Monvel's index theorem for Toeplitz operators.
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
New index formula connects numerical and K-theoretic indices.
problem Equivariant index for proper group actions on manifolds.
method Developed a trace on group conjugacy classes to relate numerical and K-theoretic indices. result Shows that numerical index equals K-theoretic index under certain conditions. This study analyzes AdaGrad's stability and convergence in non-convex optimization.
problem Lack of theoretical analysis for AdaGrad in non-convex optimization.
method Novel stopping time-based techniques from probability theory.
result Established stability and derived convergence rates for AdaGrad.
This study shows how DDPM can be represented by the OU process.
problem Designing optimal noise schedules for DDPM.
method Formal equivalence between DDPM and OU process, heuristic designs based on Fisher Information.
result Fisher-Information-motivated schedule corresponds to cosine noise schedule.