The paper improves neural network predictions by integrating process knowledge.
problem Improving neural network predictions for process execution data.
method Integrates background process knowledge into neural networks with attention mechanisms.
result Improves prediction accuracy for process execution data.
LEMs extend transformer-based architectures for complex execution problems.
problem Handling flexible time boundaries and multiple execution constraints in deep learning.
method Decouples market information processing from execution allocation decisions using TKANs, VSNs, and multi-head attention mechanisms.
result LEMs achieve superior execution performance compared to traditional benchmarks.
Deep neural net solves multi-agent optimal trading problem.
problem Optimal trade execution for multiple agents and assets.
method Residual U-net with self-attention for viscosity solution approximation.
result Neural network approach outperforms finite difference methods.
Study compares high-frequency trading vs. buy and hold in stock markets with and without execution delay.
problem Impact of trade execution delay on Kelly-based stock trading strategies.
method Comparison of high-frequency trading and buy and hold strategies using Kelly's criterion and simulation.
result Buy and hold can outperform high-frequency trading with execution delay, contrary to intuition.
Improved inference in probabilistic programs using attention mechanisms.
problem Inference failure in existing IC network architectures due to long-range dependency issues.
method Inference compilation with attention mechanism to model latent variables.
result Attention mechanism enhances proposal distributions to better match true posterior.
This study examines the execution phase of corporate share buy-backs, highlighting inefficiencies and costs.
problem Lack of research on share buy-back execution practices and associated costs.
method Comparative analysis of execution practices and fees charged to corporations and investors.
result Uncovered inefficiencies and frictional costs in share buy-back executions, advocating for transparency and fairness.
Corporate VC firms struggle with internal conflicts despite growth.
problem Internal conflicts among corporate VC investment teams.
method Conducted interviews with corporate VC firms to identify issues.
result Executive-level attention and commitment are lacking, leading to conflicts.
Detects corruption in agentic models during execution.
problem Inconsistent context, retrieval errors, or adversarial inputs corrupt intermediate steps of reasoning chains.
method Analyzes token graphs induced by attention and computes spectral statistics to emit accept/reject signals.
result A single threshold on the high frequency energy ratio optimally detects context inconsistency in agentic models.
Transformers learn to perform logistic regression in-context.
problem Understanding how transformers learn to perform specific tasks in-context.
method Constructed multi-layer transformers that perform in-context logistic regression through normalized gradient descent.
result Transformers can be trained to perform in-context logistic regression effectively.
DanSmp predicts stock movement using a hybrid-relational MKG and dual attention networks.
problem Predicting stock price trends in volatile financial markets.
method Constructs a bi-typed MKG with hybrid-relations and uses DanSmp, a dual attention network, to learn momentum spillover signals.
result DanSmp improves stock prediction accuracy using the MKG.
Neural model improves text segmentation accuracy.
problem Manual feature engineering and large memory requirements in text segmentation.
method Attention-based bidirectional LSTM model with CNN sentence embeddings and contextual prediction.
result Improves WinDiff score by ~7% on three benchmark datasets.
DeepHGNN detects program identity changes using system behaviors.
problem Detecting program identity changes to prevent malware.
method Attentional heterogeneous graph neural network (DeepHGNN) for graph classification.
result DeepHGNN effectively identifies program identities across various metrics.
Deep learning detects protective movement behavior in chronic pain patients.
problem Detecting protective movement behavior in chronic pain patients for intervention.
method End-to-end deep learning architecture named BodyAttentionNet (BANet) that learns temporal and bodily parts.
result Statistically significant improvements in detecting protective behavior using attention mechanisms.
A new algorithm reduces memory usage for long token attention in streaming applications.
problem Memory inefficiency in computing attention for long documents.
method One-pass streaming algorithm using sublinear space storage.
result Super-efficient memory usage for long token attention.
Study improves forecasting in betting markets using novel neural networks.
problem Improving short-term price movement predictions in betting exchanges.
method Innovative convolutional attention mechanisms applied to recurrent neural networks and bi-dimensional layers.
result All proposed innovations positively impact classification task performance.
The study provides precise asymptotic theory for in-context learning by Transformers.
problem Understanding the sample complexity, pretraining task diversity, and context length for successful in-context learning.
method An exactly solvable model of linear regression task by linear attention, deriving sharp asymptotics.
result Double-descent learning curve with increasing pretraining examples, phase transition between low and high task diversity regimes.
Transformers can implement reinforcement learning algorithms from data without updates.
problem Training reinforcement learning algorithms from data without parameter updates.
method Design a teacher-mimicking training procedure for transformers to implement policy-improvement methods.
result Gradient flow converges to an optimal parameter manifold corresponding to the desired RL update.
A two-network architecture learns intractable exponential family models.
problem Learning a model itself, not just optimizing parameters of a single distribution.
method Two-network architecture and optimization procedure for exponential family models.
result Accurately learns exponential family models, enabling generic operations.
LLMs improve parent-order execution in trading.
problem Improving execution costs in algorithmic trading.
method PACE (Plan-Ahead Controlled Execution) framework.
result LLMs outperform existing methods by 0.65 bps.
Study improves stock movement prediction using multimodal data.
problem Inaccurate stock movement prediction due to incomplete multimodal data integration.
method Introduces MSGCA framework for robust multimodal fusion.
result MSGCA framework outperforms existing methods by 21.7% on multimodal datasets.
Study improves self-driving safety in dynamic environments.
problem Safe self-driving in non-stationary urban settings.
method Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA).
result Self-driving agents can adapt safely in real-time.
Proposes a method to allocate time budgets in mixed criticality systems.
problem Managing execution time variability in mixed criticality systems.
method Quantifies execution time variability using statistical dispersion parameters and proposes a heuristic to allocate time budgets.
result The proposed heuristic reduces the probability of exceeding allocated budgets.
The paper uses machine learning to detect malicious executable files.
problem Detecting malicious executable files using static analysis.
method Pre-processing, cleaning, encoding, feature selection, and ensemble training of classifiers.
result An ensemble of classifiers effectively detects malicious executable files.
Optimal crypto order execution using cross-exchange signals.
problem Maximizing order execution efficiency in cryptocurrency markets.
method Reinforcement learning applied to cross-exchange data.
result Cross-exchange signals improve optimal execution outcomes.
This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.
problem Predictive models using biased historical data can perpetuate unfair behavior in new cases.
method Investigates independence through metrics like ΔDP and a composite loss function balancing predictive performance and fairness.
result Proposes and validates a composite loss function for training models that balance fairness and performance.
The paper tackles decision-oriented communications for energy-efficient resource allocation.
problem Maximizing utility functions under quantized information.
method Develops solutions for quantizing information to maximize utility functions under known and observed conditions.
result Quantizing the state roughly is optimal for sum-rate maximization but not for energy-efficiency metrics.
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
problem Analyzing trade execution strategies in a stochastic market with price impact.
method Formulated a Markov game model and used backward induction method of dynamic programming.
result Explicit closed-form execution strategy at Markov perfect equilibrium.
Study on-chain peak shaving to reduce Ethereum transaction costs.
problem Reducing transaction costs in blockchain networks, especially during congested periods.
method Analyzing transaction-level data from multiple firms across various industries to understand scheduling responses and cost management strategies.
result Firms' scheduling responses to congestion vary, leading to different fee savings and residual costs.
A decentralized routing framework for lunar exploration robots.
problem Routing data in intermittent connectivity lunar networks.
method Graph Attention-based Multi-Agent Reinforcement Learning (GAT-MARL).
result Higher delivery rates, no duplications, fewer packet losses.
Optimizes stock execution costs using stochastic control theory.
problem Minimizing execution costs in a market with discrete stock price movements.
method Discrete-time Stochastic Control Theory applied to a market model.
result Developed optimal allocation strategy for executing K stocks within T units.
Model for optimal execution with passive market impact.
problem Optimizing execution strategies in markets with passive price impact.
method Developed a mesoscopic model incorporating empirical price impact features.
result Obtained a passive impact rate that decays exponentially with quote distance.
Risk-averse trading policies learned from simulated market interactions.
problem Minimizing execution cost in limit order book markets with market impact.
method Risk-sensitive Q-learning applied to Markov Decision Process in a market simulator.
result Derived decision-tree-based execution policies that minimize cost variance.
Optimized execution model using interbank and internal liquidity.
problem Minimizing market impact in trading.
method Integrates interbank limit and market orders with internal market-making liquidity.
result Reduces market impact and improves execution efficiency.
Paper proposes a deep learning method to estimate fill probabilities of limit orders in LOBs.
problem Estimating the fill probabilities of limit orders in different levels of a limit order book.
method Survival analysis model using a convolutional-Transformer encoder and a monotonic neural network decoder.
result The proposed method significantly outperforms other approaches in survival analysis.
GOBO compresses 99.9% of BERT model parameters to 3 bits, improving inference efficiency.
problem Efficient execution of attention-based NLP models, especially in terms of latency and energy consumption.
method GOBO quantizes 32-bit floating-point parameters to 3 bits without fine-tuning, using hardware compression and co-designed architectures.
result GOBO maintains model accuracy while significantly reducing inference latency and energy consumption.
Paper analyzes the cost and execution of limit orders in a market with random walk price behavior.
problem Cost and execution of limit orders in markets with random walk price behavior.
method Exact solution for the cost of static passive slice execution, derivation of risk and execution probability functions.
result No optimal limit level for order execution in a market with random walk price behavior.
FastDeepIoT optimizes neural network execution time on mobile devices.
problem Excessive neural network execution time on low-end devices.
method Automatically learns execution time model and optimizes network configurations.
result Significant reduction in execution time and energy consumption.
Paper uses DDPG to learn optimal execution strategies in dynamic markets.
problem Learning non-Markovian optimal execution strategies in dynamic financial markets.
method Introduces a novel actor-critic algorithm based on DDPG for transient price impact modeling.
result Successfully approximates optimal execution strategy through numerical experiments.
Develops a new model to optimize trading in markets.
problem Optimal execution of market securities with transaction costs.
method Introduces a utility function balancing market impact and transaction costs, incorporating existing optimal trading strategies.
result Demonstrates a new approach to balancing market impact and transaction costs.
Paper proposes a novel policy distillation method for better order execution in noisy markets.
problem Effective order execution in noisy and imperfect market conditions.
method Policy distillation method to guide reinforcement learning towards optimal trading strategies.
result Significant improvements over various baselines in order execution.
Unified theory for optimal execution through signal-adaptive quotes in limit order books.
problem Optimal execution in limit order books with signal-dependent factors.
method Develops a unified solution theory for four execution criteria, incorporating signal-dependent drift, price impact, inventory risk, and execution risk.
result Explicit formulas reveal optimal quoting strategies and show signal-dependent drift can significantly affect execution.
Dynamic VWAP execution improves by 10-15% in liquid markets.
problem Improving VWAP execution in dynamic markets.
method Recurrent Neural Networks (RNNs) for capturing temporal market dynamics, dynamic adjustment mechanism.
result Significant performance gains in liquid markets (10-15%) over traditional methods.
This work improves uncertainty estimates for LISTA estimators.
problem Lack of certainty estimates for LISTA estimators.
method Proposes a rigorous method to obtain confidence intervals for LISTA.
result Provides a way to quantify uncertainty for LISTA estimators.
Modeling trading costs for correlated instruments to improve execution strategies.
problem Incorrect estimation of liquidity and suboptimal execution strategies due to neglecting cross-impact effects.
method Extending the linear propagator model to the multivariate case for correlated instruments, calibrating a cost model free of arbitrage and manipulation.
result Synchronizing the execution of correlated contracts is crucial for accurate liquidity estimation and optimal execution strategies.
Paper uses DRL to optimize trade execution, outperforming VWAP and TWAP.
problem Optimizing returns while minimizing risk in order execution.
method Deep Reinforcement Learning (DRL) for holistic optimization.
result DRL-based approach outperforms VWAP and TWAP in ROI and risk management.
Optimal trading strategies in fluctuating financial markets are analyzed using complex mathematical models.
problem Optimal execution of trades in markets with fluctuating liquidity and order book depth.
method Continuous-time limit order book model with càdlàg semimartingale strategies, quadratic BSDEs.
result Characterization of minimal execution costs and existence of optimal strategies.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.
Many learning agents impact a financial market model, showing complex dynamics.
problem Understanding the dynamics of financial markets with multiple learning agents.
method Agent-based model of financial market with multiple reinforcement learning agents interacting.
result Inclusion of learning agents changes market dynamics to match empirical data.