New method extracts patterns from program logs and embeds them for detection.
problem Real-world malicious software detection.
method Extract patterns from behavior graph, embed into continuous space using autoencoder.
result Embedding captures interpretable structures in pattern parts.
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.
Pyro enables scalable AI models using probabilistic programming.
problem Developing complex probabilistic models for large datasets.
method Stochastic variational inference, PyTorch, Poutine.
result Pyro supports scalable AI models with high-dimensional data.
MatchGNet detects malware by learning program behavior graphs.
problem Malware evasion through obfuscation and high false positives in traditional detection methods.
method Heterogeneous Graph Matching Network model that learns graph representation and similarity metrics.
result MatchGNet reduces false positives by 50% while maintaining zero false negatives.
Improved genetic programming by optimizing mutation operators for continuous program search.
problem Small syntactic mutations in genetic programming can lead to unpredictable behavioral shifts.
method Learned a compact trading-strategy DSL, created a block-factorized embedding, and designed geometry-compiled mutation operators.
result Geometry-compiled mutation operators discover strong strategies using fewer evaluations and achieve higher Sharpe ratios.
MORL uses program synthesis to improve reinforcement learning policies.
problem Difficult to interpret and impose constraints on learned policies from black-box neural networks.
method Iterative framework combining program synthesis and behavior cloning.
result Programmatic representation allows for high-level modifications leading to improved learning.
Paper introduces a novel method to generate diverse inputs for neural programming by example.
problem Synthesizing programs from input/output pairs using machine learning.
method Uses an SMT solver to generate diverse input-output pairs.
result Generated inputs improve model performance and generalization.
The paper analyzes how behavioral investors make portfolio decisions using Markowitz Stochastic Dominance criteria.
problem Understanding how behavioral investors make portfolio decisions.
method Developed stochastic optimization problems and MILP models to capture subjective decision weights and probability weighting functions.
result The developed models can be used to formulate computationally tractable portfolio analysis problems.
Paper evolves a grandmaster-level chess evaluation function.
problem Creating a chess program that can play at grandmaster level.
method Combining supervised and unsupervised learning with genetic algorithms.
result The evolved program outperforms a two-time World Computer Chess Champion.
This paper solves steady-state planning for multichain MDPs.
problem Specifying constraints on the steady-state behavior of an agent.
method Linear programming solution for multichain MDPs.
result Optimal solutions yield stationary policies with rigorous guarantees.
Agents learn to execute programs grounded in their environment.
problem Executing programs with unknown objects.
method Deep RL agents that ground program terms in environment.
result Agents generalize to new, unseen objects.
A new algebra for probabilistic programming improves tail behavior accuracy.
problem Inaccurate tail behavior in probabilistic models based on neural networks.
method Developed a three-parameter tail asymptotics algebra based on the generalized Gamma distribution.
result Inference algorithms using the heavy-tailed algebra achieve superior performance.
Algorithm explains XGBoost models using LIME and ILP.
problem Explain XGBoost model behavior using logic programs.
method Use LIME to select features, then apply LIME-FOLD heuristic ILP to learn non-monotonic logic programs.
result Significant improvement in classification metrics with fewer rules.
New approach generates better synthetic data for neural program synthesis.
problem Current approaches to neural program synthesis generalize poorly to real data.
method Adversarial approach to control synthetic data distributions.
result Proposed method outperforms current approaches.
Genetic algorithms improve computer chess programs to grandmaster level.
problem Improving computer chess programs to match human grandmaster performance.
method Evolved a grandmaster-level evaluation function and search mechanism using genetic algorithms.
result The evolved program outperforms a world champion and matches other top programs.
A method based on wavelet transform and genetic programming is proposed for characterizing and modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. …
Paper proposes a two-stage ranking for personalized TV recommendations.
problem Improving TV recommendation accuracy and efficiency.
method First, identifies potential candidates using user viewing patterns. Then, ranks them based on user preferences and program textual information.
result The proposed model outperforms in recommendation accuracy and efficiency.
Develops a risk-sensitive reinforcement learning framework for uncertain environments.
problem Learning in uncertain environments with varying risk preferences.
method Integrates utility functions and risk measures into reinforcement learning, tuning risk preference with parameter β.
result Risk-averse, risk-neutral, and risk-taking behaviors can be achieved and compared.
New methodology controls synthetic data bias for neural program synthesis.
problem Deep networks generalize poorly to certain data distributions when trained on synthetic examples.
method Proposes a new methodology to control and evaluate the bias of synthetic data distributions over programs and specifications.
result Training deep networks on controlled synthetic data distributions leads to improved cross-distribution generalization performance.
Abstract perspective on quadratic programming for optimal portfolio allocation.
problem Optimal allocation problems in long portfolio theory.
method Using maximum principles and distinguished boundaries in reproducing kernel Hilbert spaces.
result Support of an optimal distribution lies in a variety intersecting a distinguished boundary.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.
New memory in neural networks mimics computer architectures.
problem Learning algorithms and complex tasks with neural networks.
method Introducing a new memory to store weights for a neural controller, similar to stored-program memory in computers.
result Neural Stored-program Memory enhances neural networks' adaptability and learning capabilities.
New model uses Half-Full/Half-Empty approach for better portfolio selection.
problem Improving portfolio selection through behavioral finance.
method Generalized Half-Full/Half-Empty approach to positive/negative lotteries, developing nonconvex optimization and mixed-integer linear programming models.
result The Half-Full/Half-Empty model outperforms other methods in risk and profitability.
Dynamic programming helps manage fisheries affected by random disturbances.
problem Managing fisheries affected by random disturbances.
method Dynamic programming approach to analyze and optimize management strategies.
result Dynamic programming is crucial for fully characterizing optimal harvest strategies.
Programs as explanations for complex models without sacrificing accuracy.
problem Finding interpretable explanations for complex machine learning models without sacrificing accuracy.
method Prototype program induction method based on simulated annealing.
result Small programs can be expressive and intuitive as explanations for black-box classifiers.
The paper presents a method to score patient engagement in care programs and predicts their response.
problem Improving health outcomes of high-need patients through better patient engagement.
method Data-driven behavioral engagement scoring pipeline for two aspects of patient engagement.
result The scoring method successfully predicts patient engagement and provides interpretable insights.
IReEn reveals functionality of black-box agents via iterative neural synthesis.
problem Revealing the functionality of a black-box agent without privileged information.
method Iterative refinement of candidate programs using neural program synthesis.
result The approach finds a functional equivalent program in 78% of cases, outperforming state-of-the-art.
Reinforcement learning has gained wide popularity as a technique for simulation-driven approximate dynamic programming. A less known aspect is that the very reasons that make it effective in dynamic programming can also be leveraged for using it for distributed schemes for certain matrix computations involving non-nega…
New method detects and measures malicious users in recommendation algorithms.
problem Identifying and quantifying malicious user activity in recommendation systems.
method Probabilistic programming for a disentangled model of malicious and regular user behavior.
result Simulation-based measure for quantifying malicious user effects.
Expert-driven genetic algorithms improve chess evaluation functions.
problem Creating efficient evaluation functions for complex systems.
method Using genetic algorithms with expert guidance to evolve evaluation functions.
result Evolved programs outperform top chess programs with fewer parameters.
This article reviews machine learning methods for behavioral sciences.
problem Applying machine learning to behavioral science data.
method Overview of classical and recent algorithms for tabular, behavioral, and textual data.
result Practical guidance for using machine learning in behavioral science.
Genetic algorithms optimize chess evaluation functions with mentor assistance.
problem Optimizing complex evaluation functions for superior performance.
method Genetic algorithms with mentor-assisted evolution.
result Programs evolved with mentors outperform top chess champions.
Paper detects proxies in linear regression models causing discrimination.
problem Discrimination in machine learning models using proxies for protected attributes.
method Formulated a definition of proxy use, identified proxies via second-order cone program, and extended to justified business necessity.
result Proxies in linear regression models can be efficiently identified and removed to reduce discrimination.
Algorithm removes units and layers of neural networks without losing accuracy.
problem Deploying large neural networks under limited resources.
method Mixed-Integer Linear Programming (MILP) and L1 regularization.
result Lossless compression of neural networks is achieved.
The paper proposes a new DR model to better predict EUCs' responses in real-time pricing.
problem Static demand functions fail to capture temporal correlation in EUC behaviors.
method Proposes a dynamical DR model using neural networks to learn from historical data.
result The dynamical DR model significantly outperforms static models in predicting EUC responses.
Unified analysis of SVRG and Katyusha using dissipativity theory.
problem Accelerating variance reduction in stochastic optimization.
method Dissipativity theory applied to SVRG and Katyusha.
result Unified convergence analysis of SVRG and Katyusha.
New approach allows deep learning to adapt to new tasks without explicit training.
problem How to reuse deep learning knowledge for new tasks without explicit training.
method Homoiconic Meta-Mapping (HoMM) that transforms task representations.
result Zero-shot remapping of behavior to adapt to new tasks.
Optimizes piecewise local-linear approximations for global model understanding.
problem Global model behavior interpretation for black-box models.
method Dynamic programming framework for piecewise local-linear approximations with fidelity guarantees.
result Polynomial time algorithm for optimal clustering.
Paper analyzes infinite-width attention layers using Tensor Programs.
problem Capturing the infinite-width limit of attention layers.
method Tensor Programs framework to rigorously identify the limit distribution.
result Derives exact form of infinite-width limit distribution without Gaussian approximations.
Derives mean field game equations from microscopic agent dynamics.
problem Modeling behavior of large interacting agents in games.
method Derives mean field game PDEs from deterministic agent dynamics using Nash equilibria and dynamic programming.
result Derives mean field game limits and scales from agent-based financial market model.
Variational inference improves hierarchical imitation learning of control programs.
problem Learning structured control policies from demonstrations.
method Variational inference for discovering hierarchical structure in observation-action traces.
result Variational inference leads to more efficient and generalized control policies.
The paper explores how neural networks make predictions using probabilistic programming.
problem Understanding how neural networks make individual predictions.
method Defining and sampling prediction level sets using probabilistic programming.
result The method can obtain examples that result in specified predictions by neural networks.
CoinDICE estimates confidence intervals for unknown behavior policies in reinforcement learning.
problem Estimating value of a target policy using only behavior policy data.
method Function space embedding, generalized empirical likelihood method, Lagrangian optimization.
result Valid confidence intervals with tighter and more accurate estimates than existing methods.
Study minimal surface immersions in hyperbolic 3-manifolds, proving existence and uniqueness.
problem Existence and uniqueness of minimal surface immersions in hyperbolic 3-manifolds.
method Analyzing the asymptotic behavior and using blow-up analysis.
result Determine conditions for unique and multiple solutions, analyze blow-up behavior.
Paper builds ML classifier to detect crypto-ransomware.
problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.
Optimizes decisions under strategic individual behavior.
problem Optimal decision-making in strategic environments.
method Characterizes strategic effort, identifies optimal policies under monotonic cost assumptions, develops iterative search algorithm.
result Demonstrates higher utility of decision policies accounting for strategic behavior.
A new portfolio model considers investor aversion to loss and risk.
problem Constructing a robust portfolio under uncertain asset returns and investor aversion.
method Distributional robust optimization (DRP) with a Wasserstein ball centered on empirical distribution, mixed-integer quadratic programming, and hybrid algorithm.
result Empirical testing shows superior performance in asset allocation compared to common strategies.
Different approaches to defining dynamic market risk measures are available in the literature. Most are focused or derived from probability theory, economic behavior or dynamic programming. Here, we propose an approach to define and implement dynamic market risk measures based on recursion and state economy representat…