AI uses statistical PQRS workflow for human-machine collaboration.
problem Integrating AI with statistical principles for better results.
method PQRS workflow integrating statistical concepts with AI.
result Reproducibility and interpretability of AI algorithms and results.
HuMaINs combines human and machine strengths for better inference tasks.
problem Improving inference performance through human-machine collaboration.
method Novel signal processing and machine learning solutions for HuMaINs architecture.
result HuMaINs achieves higher performance than either humans or machines individually.
AI agents learn to cooperate with users of unknown type.
problem Designing AI agents that can cooperate with new users effectively.
method Modeling user behavior as parameters, observing user actions to infer type, and adapting policies.
result Adaptive AI agents perform significantly better than non-adaptive ones in real scenarios.
Pandora hybridizes human and machine methods to explain AI system failures.
problem Understanding and explaining failures in complex AI systems.
method Hybrid human-machine methods and tools for summarizing system malfunction.
result Detailed performance views help in analysis and debugging of AI systems.
Human+Machine models show no advantage in recidivism prediction.
problem Improving fairness in recidivism risk assessments.
method Constructed Human Risk Score and hybrid Human+Machine models.
result Human and COMPAS decision making differed but not in ways that improved prediction.
Study human-machine interaction with private info using offline RL.
problem Confounding bias and distributional mismatch in offline RL for human-guided interaction.
method Developed a novel identification result and OPE method to address confounding bias, and used pessimism to tackle distributional mismatch.
result Policy pair converges to optimal one at satisfactory rate under mild assumptions.
MCAL reduces labeling costs by 6x for auto-labeling data sets.
problem Expensive human annotation for ground-truth data sets.
method Iterative approach that trains a classifier to auto-label part of the data set, determining which samples to label using humans and which to label using the classifier at each step.
result 6x lower overall cost compared to human labeling the entire data set, always cheaper than competing strategies.
ShapleyBO explains BO's decisions, enhancing human-AI collaboration in robotics.
problem Lack of explainability in BO's black box nature, especially in human-in-the-loop applications.
method Uses game-theoretic Shapley values to quantify and interpret BO's parameter contributions.
result ShapleyBO identifies exploration and exploitation contributions, disentangling aleatoric and epistemic uncertainty.
This paper evaluates deferring systems using causal inference.
problem Evaluating the impact of deferring systems on model accuracy is challenging.
method The paper uses a causal inference framework to evaluate deferring systems, distinguishing between scenarios with and without access to human predictions.
result The approach allows identifying causal effects of deferring strategies on predictive accuracy.
Combines human and model predictions for improved accuracy.
problem Improving classification accuracy when both human and model predictions are imperfect.
method Uses confusion matrices and calibration to combine probabilistic model outputs with human class-level predictions.
result Human-model combinations consistently outperform either alone, with accuracy gains even with limited human input.
New AI assistant for power grid operators simplifies complex decision-making.
problem Complexity and uncertainty in power grid operations.
method Unified human-machine interface and AI integration.
result Development of a new assistant framework for power grid operators.
Improved speech emotion recognition using pre-trained language models.
problem Challenging task of speech emotion recognition for natural human-machine interaction.
method Fine-tuning pre-trained language models for text emotion recognition, combining with speech emotion recognition.
result 73.5% accuracy in speech emotion recognition on a subset of IEMOCAP dataset.
Study on predictable forward processes in trading without frequent evaluations.
problem Trading performance evaluation times not matching trading times.
method Solving a linear functional equation to construct predictable forward processes.
result Predictable forward processes are inherently myopic and optimal strategies do not use future information.
A model for human-machine decision-making with private info and opacity.
problem Optimizing decisions in a human-machine system with private info and opacity.
method Formulated as a two-player learning problem, proved lower and upper bounds on optimality.
result Simple coordination strategy is nearly minimax optimal, efficient learning possible under certain assumptions.
Framework ensures alignment between humans and machines in LLMs.
problem Human-machine misalignment in LLMs scoring mechanisms.
method Lightweight calibration framework for blackbox models.
result Provably guarantees alignment between humans and machines.
Develops methods for cooperative Bayesian inference.
problem Cooperation between learning agents.
method Sequential Bayesian inference approaches.
result Theoretical foundation for cooperative inference.
EdgeSpeechNets improve speech recognition on mobile devices.
problem Deploying deep learning for speech recognition on edge devices is challenging.
method Human-machine collaboration for designing efficient DNN architectures.
result EdgeSpeechNets achieve higher accuracy with smaller network size and lower computational cost.
Interfacing a kinetic action of a person to an action of a machine system is an important research topic in many application areas. One of the key factors for intimate human-machine interaction is the ability of the control algorithm to detect and classify different user commands with shortest possible latency, thus ma…
New algorithms reduce communication costs in collaborative learning.
problem Reducing communication costs in collaborative learning.
method Distributed boosting and adaptation to classification noise.
result Communication-efficient algorithms for collaborative PAC learning robust to noise.
Supply Chain Management often requires independent organizations to work together to achieve shared objectives. This collaboration is necessary when coordinated actions benefit the group more than the uncoordinated efforts of individual firms. Despite the commonly reported benefits that can be gained in close relations…
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies w…
Proposes MC-AE for better unsupervised clustering of unlabeled data.
problem Lack of consideration for multi-local collaborative relationships in autoencoders.
method Integrates LSH for multi-local cross blocks, mcrRBM and mcrGRBM models.
result MC-AE improves unsupervised clustering performance.
Paper tackles unobserved confounding in human-AI collaborations.
problem Unobserved confounding undermines human-AI collaboration effectiveness.
method Combines sensitivity analysis from causal inference with AI-driven statistical modeling.
result Enhances robustness and reliability of collaborative outcomes.
The paper identifies collaborations in codebases using commit activity and language usage.
problem Identifying organic team interactions and collaborations in large codebases.
method Embedding and clustering commit activity, language usage, and code identifier topics.
result Restores engineering organization and reveals hidden collaborations.
Firms' collaboration networks can decline but remain resilient.
problem Resilience of firms' collaboration networks during decline.
method Analysis of 21,500 R&D collaborations over 25 years, simulating drop-out cascades.
result Firms' collaboration networks can adapt to mitigate decline and recover.
Data science teams collaborate extensively, using various tools and stakeholders.
problem Lack of understanding in how data science workers collaborate in practice.
method Conducted an online survey with 183 data science workers.
result Data science teams are highly collaborative and use multiple tools and stakeholders.
A new model VCM improves collaborative filtering by synchronously linking two VAEs.
problem Cold start and data sparsity issues in CF-based recommender systems.
method Proposes a variational collaborative model (VCM) that synchronously links two VAEs.
result VCM outperforms state-of-the-art methods on real-life datasets.
Study collaborative learning among multi-agents in multi-armed bandits.
problem Minimizing group cumulative regret in a heterogeneous multi-agent setting.
method Developed decentralized algorithms for collaboration between N agents learning M stochastic multi-armed bandits. result Proved near-optimal behavior of proposed algorithms for group regret.
DEMVC improves multi-view clustering with collaborative training and deep autoencoders.
problem Existing multi-view clustering methods have high computation and space complexities or lack representation capability.
method DEMVC learns embedded representations of multiple views individually using deep autoencoders and collaboratively trains all views.
result DEMVC achieves significant improvements over state-of-the-art methods on multi-view datasets.
Improved collaborative PAC learning with reduced sample complexity.
problem Learning a target function collaboratively across multiple distributions.
method Multiplicative weights approach to reduce overhead from O(ln2k) to O(lnk). result Achieved an overhead of O(lnk), improving previous results. Advances in collaborative filtering and ranking methods.
problem Improving recommendation systems efficiency and accuracy.
method Graph information encoding, pairwise and listwise approaches, regularization techniques, personalization.
result New methods significantly improve recommendation system performance.
PCL tackles collaborative learning for diverse agents, reducing sample complexity.
problem Balancing collaborative speedup with personalization for heterogeneous agents.
method AffPCL, with bias and importance correction mechanisms.
result AffPCL reduces sample complexity by a factor of max{n−1,δ}, where n is the number of agents and δ∈[0,1] measures heterogeneity. In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…
Collaborative recommendation is an information-filtering technique that attempts to present information items (movies, music, books, news, images, Web pages, etc.) that are likely of interest to the Internet user. Traditionally, collaborative systems deal with situations with two types of variables, users and items. In…
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
Proposes a deep latent factor model for better recommendation systems.
problem Improving collaborative filtering in recommendation systems.
method Introduces a deeper latent factor model using deep learning.
result Significantly outperforms state-of-the-art techniques in experiments.
Optimal algorithm found for collaborative learning in bandits with optimal regret bounds.
problem Minimizing regret in collaborative multi-agent bandit problems.
method Proposed an algorithm with optimal regret bounds for collaborative multi-agent multi-armed bandit model.
result First algorithm with order optimal regret bounds for collaborative bandit model.
New algorithms reduce collaborative PAC learning sample complexity.
problem Collaborative PAC learning with reduced sample complexity.
method Design of new algorithms for both realizable and non-realizable settings.
result Sample complexity is O(ln(k)) times the worst-case sample complexity for learning a single task. A collaborative machine teaching method that improves learner performance with privacy and efficiency.
problem Improving learner performance with distributed teachers while maintaining privacy and scalability.
method Formulates collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.
result The proposed method delivers significantly more accurate teaching results with high speed compared to non-collaborative MINLP-based super teaching.
Paper compares dimensionality reduction methods for affect classification.
problem Difficulty in obtaining labeled training samples for affect classification.
method Five dimensionality reduction approaches are compared.
result No single approach universally outperforms others in affect classification.
Partner-aware algorithms improve AI collaboration in multi-agent settings.
problem Improving AI cooperation in teams with shared rewards.
method Proposed Partner-Aware strategy extending Upper Confidence Bound for decentralized MAB.
result Achieves logarithmic regret in collaborative decision-making.
A new privacy-preserving deep learning scheme for asymmetrically collaborative machine learning.
problem Privacy and efficiency in collaborative machine learning across different data owners.
method Decomposes neural network steps for privacy-preserving training; novel protocol for information leakage.
result Efficient training with stable performance and significant speedup.
A new method for online personalized learning reduces gradient variance by dynamically selecting peers.
problem Online personalized decentralized learning with statistically heterogeneous clients.
method Gradient-based collaboration criterion allowing clients to dynamically select peers with similar gradients.
result The method acts as a variance reduction method, achieving optimal performance in certain conditions.
New algorithms for collaborative learning in uncertain, decentralized environments.
problem Byzantine collaborative learning in uncertain, decentralized environments.
method Two asynchronous solutions to averaging agreement, each optimal according to some dimension.
result New algorithms achieve optimal Byzantine resilience and collaborative learning.
New algorithms balance collaboration and adversarial behavior in linear bandits.
problem Minimizing regret in a collaborative linear bandit problem with adversarial agents.
method Robust collaborative phased elimination algorithm with tight analyses.
result Achieves near-optimal regret bounds of $O\left(α+ 1/\sqrt{M}
ight) \sqrt{dT}$ for good agents.
Collaborative learning improves deep neural networks by combining multiple classifier heads.
problem Improving generalization and robustness to label noise in deep neural networks.
method Simultaneously training multiple classifier heads on the same data, using supplementary information and gradient aggregation.
result Deep neural networks perform better with collaborative learning, reducing generalization error and robustness to label noise.
Game theory models incentivizes honesty in collaborative learning among competitors.
problem Incentivizing honest updates among competitors in collaborative learning schemes.
method Formulated a game to model interactions, studied two learning tasks, proposed mechanisms to incentivize honest communication.
result Rational clients are incentivized to manipulate their updates, preventing learning; proposed mechanisms ensure comparable learning quality to full cooperation.
Enhances student diversity in collaborative learning.
problem Student homogenization in large groups.
method Random routing, diverse feature sets, and random subgroup imitation.
result Significantly outperforms state-of-the-art approaches.