We address challenges in collaborative black-box optimization through three frameworks.
problem Challenges in distributed experimentation, heterogeneity, and privacy in black-box optimization.
method Three unifying frameworks: global, local, and predictive.
result Shift from descriptive/predictive to prescriptive federated learning in black-box optimization.
This work advances collaborative decision making by combining human and AI strengths in uncertainty quantification.
problem Current AI lacks robust decision-making capabilities under uncertainty, especially in high-stakes contexts.
method Introduces Human AI Collaborative Uncertainty Quantification (HACUQ) framework, formalizing AI-human collaboration and developing calibration algorithms.
result Optimal collaborative prediction sets follow a two-threshold structure, and online adaptation algorithms can adapt to evolving human behavior.
Enhances financial analysis with multi-agent collaboration.
problem Limited use of AI-agent collaboration in financial research.
method Proposes a multi-agent system for financial investment research.
result Multi-agent system outperforms single-agent models.
A new algorithm reduces communication costs for collaborative decision-making across clients.
problem Collaborative decision-making with sparse rewards and heterogeneous contexts.
method Federated Lasso algorithm for sparse linear contextual bandits.
result Achieves near-optimal regret with logarithmic communication costs.
New method learns decisions from collective preferences without individual covariates.
problem Making decisions online without individual covariates.
method Collaborative filtering, matrix completion bandit, ε-greedy policy, online gradient descent, inverse propensity weighting.
result Method outperforms benchmarks and reveals new discoveries.
AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
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.
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.
Bayesian principles improve agentic AI decision-making.
problem Decision-making under uncertainty in agentic AI systems.
method Bayesian decision theory applied to the orchestration layer of agentic AI.
result Bayesian principles enhance agentic AI's ability to make decisions under uncertainty.
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
problem Clarifying human-AI collaboration in prediction and decision tasks.
method Integrates human judgment to distinguish algorithmically indistinguishable cases.
result Improves performance of any feasible algorithmic predictor.
This thesis tackles bias in AI decision-making in banking.
problem Bias in AI-driven banking decisions.
method Understanding, mitigating, and accounting for bias in AI systems.
result Establishment of Responsible AI practices for fair decision-making.
Optimal allocation of human effort to correct AI assessments in decision-making.
problem How to allocate costly human effort to correct noisy or biased AI-generated assessments.
method Decision-theoretic framework treating AI assessments as signals and human judgments as costly information. Developed estimation procedures under nonparametric and linear models.
result Our approach substantially outperforms LLM-only predictions and achieves performance comparable to full human review while using only 20-30% of the human information.
Develops NFCF to reduce gender bias in social media recommendation systems.
problem Reduces gender bias in collaborative filtering systems on social media data.
method Pre-training and fine-tuning neural collaborative filtering with bias correction techniques.
result Achieves better performance and fairness in gender de-biased recommendations.
New algorithm reduces regret in collaborative multi-agent bandit problems.
problem Optimizing decisions in a network of agents with communication delays.
method Follow-the-Regularized-Leader (FTRL) algorithm with suitable regularizers and communication protocols.
result Upper bound on individual regret matches lower bound up to a constant factor.
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…
Proposes a new machine learning problem for automated temporal decision-making.
problem Automating human involvement in temporal decision-making processes.
method Develops a deep Bayesian neural network, ForeClassNet, with Boltzmann convolutions.
result Achieves superior performance in real-world Foreclassing datasets.
The way developers collaborate inside and particularly across teams often escapes management's attention, despite a formal organization with designated teams being defined. Observability of the actual, organically formed engineering structure provides decision makers invaluable additional tools to manage their talent p…
New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.
problem Current fairness approaches assume perfect human compliance, but real-world compliance is often poor.
method Defines compliance-robustly fair algorithms and proposes an optimization strategy to improve fairness.
result Algorithmic recommendations can improve fairness even if humans do not fully comply with fair algorithms.
A novel method for efficient CDRL over wireless networks.
problem Challenges in collaborative deep reinforcement learning over wireless networks.
method Semantic-aware heterogeneous federated deep reinforcement learning (HFDRL) algorithm.
result Superior performance compared to state-of-the-art baselines.
AdaDKRR tackles data silos by combining autonomy, privacy, and collaboration.
problem Data silos caused by privacy and interoperability constraints.
method Adaptive distributed kernel ridge regression (AdaDKRR) with autonomy, privacy, and collaboration.
result AdaDKRR performs similarly to optimal learning algorithms on the whole data under mild conditions.
Study designs statistical inference for collaborative science teams.
problem Maintaining scientific rigor in distributed, collaborative research.
method Analyzes hypothesis testing with strategic agents and principals.
result Principal can design policies to control posterior probability of null.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
problem Fair, transparent, and explainable decision-making in Taekwondo.
method Pose-based action recognition, epistemic uncertainty modeling, interactive dashboards.
result 85% reduction in decision review time, 93% referee trust in AI-assisted decisions.
New algorithms tackle statistical heterogeneity in federated learning.
problem Statistical heterogeneity in distributed machine learning models.
method Introduces three novel methods: SuPerFed, AAggFF, and FedEvg.
result Mitigates statistical heterogeneity in federated learning.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
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…
The paper optimizes insurance strategies for two collaborating business lines.
problem Maximizing dividends and managing risk for two collaborating business lines.
method Closed-form solutions for optimal strategies, including dividend payout, reinsurance, and capital injection.
result Optimal strategies involve pure excess-of-loss reinsurance and transferring reserves to prevent ruin.
Statisticians contribute to LLMs for better trust and transparency.
problem Emerging statistical challenges in LLMs.
method Exploring statistical contributions to LLMs.
result Statisticians can enhance LLMs' trustworthiness and transparency.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
problem Heterogeneous multi-agent optimization challenges in resource use and information sharing.
method ARCO-BO integrates a consensus mechanism, budget-aware sampling, and partial input sharing for heterogeneous design spaces.
result ARCO-BO outperforms independent and collaborative BO methods in complex multi-agent settings.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
problem Challenges in deploying LLMs for high-stakes decisions, including data security and accountability.
method Interactive human involvement through multiple iterations, decentralized technologies, and automated auditing.
result Enhanced security and accountability in LLM deployment for financial decisions.
TradingAgents uses LLM-powered multi-agent framework for financial trading.
problem Lack of collaborative dynamics in multi-agent financial trading systems.
method Inspired by real-world trading firms, TradingAgents features specialized LLM-powered agents and a risk management team.
result Framework outperforms baseline models in trading performance metrics.
A model for collaborative learning with principal-agent interaction.
problem Optimizing parameter estimates in a collaborative learning setting.
method Decision-theoretic model with aggregation coefficients and Langevin dynamics.
result Advantages in stability and generalization due to cooperative behavior.
SBO uses dual voting to build consensus in noisy feedback settings.
problem Collective decision-making under social influence.
method Dual voting system with noisy public votes and private interviews.
result Efficient consensus-building through noisy public votes, debiased by estimated social graph.
The paper optimizes insurer's dividend, reinsurance, and capital injection strategies for two collaborating business lines.
problem Maximizing expected total dividend payments while managing risk and preventing ruin.
method Solving the problem using a closed-form value function for optimal strategies.
result Optimal strategies include threshold dividend payout, decreasing reinsured risk, and capital injection to prevent ruin.
AI system narrows human decision options for better outcomes.
problem Improving human decision-making in sequential tasks.
method Developed a decision support system using a pre-trained AI agent to limit human action choices.
result Participants outperformed AI and solo play in a wildfire mitigation game.
A new method for estimating uncertainty intervals in regression.
problem Lack of effective methods to estimate uncertainty intervals in regression.
method Collaborating Networks (CN) approach using two neural networks with distinct loss functions.
result CN method improves performance on various real-world datasets, including forecasting A1c values in diabetic patients.
Commentary on Rashomon Effect complicating model selection.
problem Many models equally predict data; hard to draw conclusions.
method Connections to recent ML literature exploring implications.
result Grasping Rashomon Effect can foster collaboration.
CCN estimates full potential outcome distributions without restrictive assumptions.
problem Estimating CATE is insufficient; full potential outcome distributions provide greater insights.
method Collaborating Causal Networks (CCN) learns full potential outcome distributions without restrictive assumptions.
result CCN learns distributions that asymptotically capture true potential outcome distributions.
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model devel…
New algorithm reduces complexity in multi-agent reinforcement learning.
problem High computational complexity in exact computations for multi-agent reinforcement learning.
method Design of a scalable algorithm based on Natural Policy Gradient, using local information and limited communication.
result Converges to globally optimal policy with dimension-free complexity and localization error.
Many IoT applications at the network edge demand intelligent decisions in a real-time manner. The edge device alone, however, often cannot achieve real-time edge intelligence due to its constrained computing resources and limited local data. To tackle these challenges, we propose a platform-aided collaborative learning…
We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption …
Unified RL survey for healthcare AI interventions.
problem Limited real-life application of RL in healthcare.
method Unified technical survey and case studies.
result Bridge between dynamic treatment regimes and mobile health.
Collaborative filtering is used to recommend items to a user without requiring a knowledge of the item itself and tends to outperform other techniques. However, collaborative filtering suffers from the cold-start problem, which occurs when an item has not yet been rated or a user has not rated any items. Incorporating …
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.
There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation a…
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose f…
This paper advances theory on the process of collaboration between entities and its implications on the quality of services, information, and/or products (SIPs) that the collaborating entities provide to each other. It investigates the scenario of outsourced IS projects (such as custom software development) where the e…
Defense against user shilling attacks in collaborative filtering using edge reweighting.
problem Vulnerability of collaborative filtering to profile injection attacks.
method Adversarial robustness based edge reweighting to attenuate non-robust edges.
result Effective defense against various types of attacks demonstrated through experiments.