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Arxiv
1d
42
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Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams
- Concept drift is extensively studied in stream learning, but the impact of model predictions on concept drift is often overlooked.
- Performative drift refers to situations where a model's predictions induce concept drift in a self-fulfilling or self-negating manner.
- A novel performative drift detection approach called CheckerBoard Performative Drift Detection (CB-PDD) is proposed.
- CB-PDD shows high efficacy, low false detection rates, and the ability to effectively detect performative drift in datasets.
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Arxiv
1d
347
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Provably-Safe Neural Network Training Using Hybrid Zonotope Reachability Analysis
- This work addresses the challenge of enforcing constraints on the output of neural networks, particularly for safety-critical control applications.
- The proposed method utilizes reachability analysis with scaled hybrid zonotopes, which allows for the exact image of a non-convex input set to be encouraged for a neural network with rectified linear unit (ReLU) nonlinearities.
- The method has shown to be effective and fast for networks with up to 240 neurons, with the computational complexity dominated by inverse operations on matrices that scale linearly in size with the number of neurons and complexity of input and unsafe sets.
- The practicality of the method has been demonstrated by training a forward-invariant neural network controller for a non-convex input set and generating safe reach-avoid plans for a black-box dynamical system.
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Arxiv
1d
3
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Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
- Researchers have developed a new method called SINDy-SHRED for modeling real-world spatio-temporal data.
- SINDy-SHRED utilizes Gated Recurrent Units to model sparse sensor measurements and a shallow decoder network to reconstruct the full spatio-temporal field.
- The algorithm introduces a SINDy-based regularization for converging to a linear Koopman-SHRED model.
- SINDy-SHRED outperforms current baseline deep learning models in accuracy, training time, and data requirements for video predictions.
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Arxiv
1d
119
Image Credit: Arxiv
Predicting human decisions with behavioral theories and machine learning
- Predicting human decisions under risk and uncertainty remains a fundamental challenge.
- BEAST Gradient Boosting (BEAST-GB) is a hybrid model integrating behavioral theory and machine learning.
- BEAST-GB predicts risky choice more accurately than neural networks and existing behavioral models.
- Integrating machine learning with behavioral theory improves the ability to predict and understand human behavior.
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Arxiv
1d
258
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Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
- Algorithmic pre-trial risk assessments in the US criminal justice system provide deterministic classification scores and recommendations to help judges in release decisions.
- A research study analyzes data from a field experiment on algorithmic pre-trial risk assessments to investigate the possibility of improving the scores and recommendations.
- Using a maximin robust optimization approach, the study aims to find a policy that maximizes the worst-case expected utility, ensuring the statistical safety of policy improvement.
- The analysis of the field experiment data shows certain components of the risk assessment instrument can be safely improved by classifying arrestees as lower risk under various utility specifications.
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Arxiv
1d
289
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Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data
- A new method called Causal DVAE (CDVAE) has been developed for estimating treatment effects over time in longitudinal data.
- CDVAE assumes the presence of unobserved risk factors that only affect the sequence of outcomes, targeting Individual Treatment Effect (ITE) estimation with unobserved heterogeneity.
- The model combines a Dynamic Variational Autoencoder (DVAE) framework with a weighting strategy using propensity scores to estimate counterfactual responses.
- Evaluations show that CDVAE outperforms existing state-of-the-art models in accurately estimating ITE and capturing heterogeneity in longitudinal data.
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Arxiv
1d
135
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Individualized Policy Evaluation and Learning under Clustered Network Interference
- Policy evaluation and learning often assume no interference among units, but this can lead to biased evaluation and learning outcomes.
- The paper focuses on individualized treatment rules (ITR) under clustered network interference.
- A semiparametric structural model is used to evaluate the performance of ITR.
- The proposed methodology improves the performance of learned policies through more efficient evaluation.
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Arxiv
1d
335
Image Credit: Arxiv
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation
- DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation
- Researchers propose using a powerful generalizing descriptor and augmentation to enable domain-generalized pre-training and test-time adaptation for high-quality segmentation in unseen domains.
- The method was evaluated on five different publicly available datasets, including 3D CT and MRI images, in abdominal, spine, and cardiac imaging scenarios.
- Results show significant improvements in cross-domain prediction for abdominal, spine, and cardiac scenarios, with increased Dice similarity scores ranging from 14.2% to 72.9%.
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Arxiv
1d
165
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DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs
- A new deep learning-based technique for detecting jammers in 5G Connected and Automated Vehicle (CAV) networks has been developed.
- The technique focuses on the Synchronization Signal Block (SSB) and leverages RF domain features to improve network robustness.
- By extracting PSS correlation and energy per null resource elements (EPNRE) characteristics, the method distinguishes between normal and jammed signals with high precision.
- The proposed technique achieves a 96.4% detection rate at extra low jamming power, specifically with SJNR between 15 to 30 dB.
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Arxiv
1d
150
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Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks
- Recent works in Task and Motion Planning (TAMP) show that training control policies on language-supervised robot trajectories with quality labeled data improves task success rates.
- A framework is presented to decompose trajectory data into temporally bounded and natural language-based sub-tasks.
- An algorithm named SIMILARITY is introduced to measure the temporal alignment and semantic fidelity of language descriptions in sub-task decompositions.
- The framework demonstrates high scores for both temporal similarity and semantic similarity, above 90%, compared to a randomized baseline of 30% in multiple robotic environments.
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Arxiv
1d
355
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A predictive machine learning force field framework for liquid electrolyte development
- A predictive machine learning force field framework for liquid electrolyte development.
- Introducing BAMBOO (ByteDance AI Molecular Simulation Booster), a predictive framework for molecular dynamics (MD) simulations for liquid electrolyte in lithium batteries.
- Utilizes a physics-inspired graph equivariant transformer architecture to learn from quantum mechanical simulations.
- Demonstrates state-of-the-art accuracy in predicting key electrolyte properties such as density, viscosity, and ionic conductivity.
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Arxiv
1d
262
Image Credit: Arxiv
FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
- We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting.
- Existing federated approaches using secure computation suffer from substantial overheads and do not offer output privacy.
- The work provides enhancements to both differentially private (DP) and secure computation components to achieve better speed, privacy, and accuracy.
- By utilizing the computational DP model, a lightweight, secure aggregation-based approach is designed, achieving significant speed improvement and improved utility.
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Arxiv
1d
339
Image Credit: Arxiv
GP-MoLFormer: A Foundation Model For Molecular Generation
- GP-MoLFormer is an autoregressive molecular string generator trained on over 1.1 billion chemical SMILES.
- It performs well on three different generative tasks: de novo generation, scaffold-constrained molecular decoration, and property-guided optimization.
- GP-MoLFormer demonstrates its general utility and compares favorably to existing baselines.
- The model shows strong memorization of training data, impacted by the quality and scale of the training data.
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Arxiv
1d
312
Image Credit: Arxiv
Hierarchical Procedural Framework for Low-latency Robot-Assisted Hand-Object Interaction
- Advances in robotics have led to the development of human-robot interaction (HRI) technologies.
- A hierarchical procedural framework is proposed to enable dynamic robot-assisted hand-object interaction.
- The framework leverages computer vision (CV) for 3D reconstruction of the human hand and motion primitives for robotic actions.
- Experimental validation demonstrates the effectiveness of the hierarchical control architecture, achieving a delay of ≤ 0.3 seconds in tele-interaction scenarios.
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Arxiv
1d
154
Image Credit: Arxiv
Eliminating Position Bias of Language Models: A Mechanistic Approach
- Position bias is a prevalent issue in modern language models.
- The bias leads to unexpected model failures and affects performance, robustness, and reliability.
- A mechanistic analysis identifies causal attention and relative positional encodings as the sources of bias.
- A training-free zero-shot approach called PINE (Position-INvariant inferencE) is proposed to eliminate the bias and improve performance in downstream tasks.
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