Bayesian optimisation generates saliency maps for black-box models.
problem Generating saliency maps for models without access to parameters.
method Bayesian optimisation sampling method to find global salient regions.
result Approach outperforms grid-based methods and performs similarly to gradient-based methods.
Study investigates FL performance over a noisy downlink, showing analog approach outperforms digital.
problem Impact of bandwidth-limited downlink on federated learning performance.
method Modelled downlink and uplink channels; proposed digital and analog downlink approaches; analyzed convergence behavior.
result Analog downlink approach provides significant improvement over digital approach, especially with biased data distribution.
Multi-party machine learning leaks global dataset properties even with black-box access.
problem Leakage of global dataset properties in multi-party machine learning.
method Demonstrated leakage of sensitive attribute distributions in pooled data.
result A curious party can infer sensitive attribute distributions in other parties' data with high accuracy.
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geoloc…
Postprocessing reduces Bayesian optimization steps for global optima.
problem Slow convergence in Bayesian optimization for high-dimensional problems.
method Prohibits duplicated samples in the dataset postprocessing method.
result Significantly reduces the number of sequential steps to find the global optimum.
ModHiFi identifies critical components for model modification without gradients or loss function.
problem Modifying open weight models without access to training data or loss function.
method Theoretical analysis of Lipschitz-continuous networks, Subset Fidelity metric, and ModHiFi algorithm.
result ModHiFi-P and ModHiFi-U achieve significant performance improvements in model pruning and unlearning.
New diffusions help globally optimize non-convex functions.
problem Optimizing non-convex functions globally.
method Euler discretization of Langevin diffusion.
result Different diffusions optimize different convex and non-convex functions.
The large-scale organization of the world economies is exhibiting increasingly levels of local heterogeneity and global interdependency. Understanding the relation between local and global features calls for analytical tools able to uncover the global emerging organization of the international trade network. Here we an…
Study identifies spatial inequalities in urban services access based on income.
problem Spatial inequalities in urban services and accessibility based on income.
method Multidimensional approach using land use and public transportation data.
result Low-income population has low access to hospitals and cultural centers, while public schools and sports centers have intermediate accessibility.
We discuss how minimal financial market models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the tra…
Interactive book integrates probability models with big data analytics.
problem Lack of integration between classical loss data models and modern analytic tools.
method Combines classical loss data models with modern analytic tools and big data.
result Promotes deeper learning through interactive elements and multiple language support.
Global graph structure improves GNN performance.
problem Limited graph structure in GNNs leads to indistinguishable node embeddings.
method Empirically tested the impact of global graph information on GNN performance.
result Global information can significantly improve GNN performance by more than 5%.
Paper improves a method for fast global and local convergence in optimization.
problem Slow global convergence in optimization methods with noisy Hessian estimates.
method Stochastic Newton Proximal Extragradient method using HPE framework.
result Faster global linear rate and superlinear convergence in fewer iterations.
RLHC uses multiple critics at different levels to enhance RL performance.
problem Speed up RL learning and increase cumulative rewards in competition tasks.
method Introducing multiple cooperative critics at two levels of a hierarchy to provide global information.
result RLHC outperforms PPO in tennis and soccer competitions.
We consider the problem of global optimization of an unknown non-convex smooth function with zeroth-order feedback. In this setup, an algorithm is allowed to adaptively query the underlying function at different locations and receives noisy evaluations of function values at the queried points (i.e. the algorithm has ac…
Edge devices learn a global model collaboratively over wireless channels.
problem Learning a global model from edge devices with imperfect channel state information.
method Proposed analog aggregation scheme, receive beamforming at PS, and convergence analysis.
result Performance improvement with more PS antennas, even with imperfect CSI.
New method tackles inexact bilevel optimization for faster parameter learning.
problem Nested optimization problems in bilevel learning with computationally difficult exact solutions.
method Inexact derivative-free optimization algorithms for approximate lower-level solutions.
result Global convergence and worst-case complexity for the proposed approach.
Researchers create a framework to value player actions in CSGO.
problem Lack of accessible data and analytical frameworks for esports players.
method Data model, graph distance measure, context-aware framework.
result Demonstrated framework's consistency and independence compared to existing methods.
This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard t…
Worldcoin aims to make cryptocurrency transparent and accessible.
problem Lack of transparency and regulation in cryptocurrency markets.
method Hypothetical cryptocurrency model to establish transparency and equal access.
result Established a model for a regulated and trusted cryptocurrency infrastructure.
Study examines quotients of affine connection control systems.
problem Existence of quotients preserving mechanical structures.
method Local and global sufficient and necessary conditions for geodesically accessible systems.
result Quotients can be constructed for geodesically accessible systems.
Develops transparent global models consistent with local explanations.
problem Creating globally interpretable models that align with local explanations from black-box models.
method Custom boolean features from sparse local contrastive explanations are used to train a globally transparent model.
result Custom transparent models have higher local consistency compared to other strategies.
Federated learning models are analyzed through game theory to determine optimal model sharing.
problem Agents with different data distributions face a choice between local or global models in federated learning.
method The problem is analyzed using coalitional game theory and hedonic game theory, considering different degrees of customization in model sharing.
result Exact expected MSE values are derived for linear regression and mean estimation problems, and stable partitions of players into coalitions are analyzed.
We establish that first-order methods avoid saddle points for almost all initializations. Our results apply to a wide variety of first-order methods, including gradient descent, block coordinate descent, mirror descent and variants thereof. The connecting thread is that such algorithms can be studied from a dynamical s…
The paper proposes a new model for predicting and analyzing economic variables.
problem Predicting and analyzing economic variables in developed regions.
method Time-varying parameter global vector autoregressive (TVP-GVAR) framework combined with machine learning models.
result The proposed model provides high precision out-of-sample predictions and novel insights into economic variable connectedness.
A new method assesses regression models' global optimality.
problem Challenges in evaluating regression models without access to true data.
method Information Teacher framework based on Shannon mutual information.
result Demonstrates capability to detect global optimality.
FedBE aggregates local models into a robust global model via Bayesian inference.
problem Challenges in aggregating non-i.i.d. local models into a global model in federated learning.
method FedBE uses Bayesian inference to sample and combine higher-quality global models from local models.
result FedBE leads to more robust aggregation of local models into a global model, especially when data is non-i.i.d.
Novel proof technique for Gelfand-Fuks cohomology.
problem Comparing sheaf-like data over manifold Cartesian powers.
method Local-to-global analysis through generalized good covers and factorization algebras.
result Unified approach to Gelfand-Fuks cohomology.
Privacy preserving networks can be modelled as decentralized networks (e.g., sensors, connected objects, smartphones), where communication between nodes of the network is not controlled by an all-knowing, central node. For this type of networks, the main issue is to gather/learn global information on the network (e.g.,…
Shared workspace improves neural module coordination in deep learning.
problem Pairwise interactions in deep learning models lack global coordination.
method Introduce a shared global workspace with bandwidth limitations among neural modules.
result Capacity limitations encourage specialization and synchronization.
Efficient algorithms for planning in cooperative multi-agent reinforcement learning with combinatorial action spaces.
problem Planning in cooperative multi-agent reinforcement learning with a combinatorial action space.
method Efficient algorithms using local access to a simulator and linear function approximation, with improvements for additive feature decomposition and kernelized settings.
result Polynomial compute and query complexity in relevant problem parameters.
Simple algorithm samples graph nodes efficiently.
problem Sampling representative nodes from large graphs efficiently.
method Minimum inner product greedy selection rule, column-selective sampling.
result Achieves sampling proportional to cluster size, error decays with inter-cluster connectivity.
Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.
problem Optimizing personalized objectives in a distributed system with limited global information.
method Kernel-based bandit framework with surrogate Gaussian process models, sparse approximations.
result Order-optimal regret performance (up to polylogarithmic factors) and reduced communication overhead.
Unified framework for Sobolev spaces on vector bundles, including explicit integration by parts.
problem Developing a comprehensive theory for Sobolev spaces on vector bundles.
method Explicit higher-order geometric integration by parts formula on arbitrary Riemannian manifolds.
result Direct proofs of classical theorems in Sobolev spaces on vector bundles.
This work improves fairness in federated learning by using zero-shot data augmentation.
problem Statistical heterogeneity leads to biased and less uniform accuracy across clients in federated learning.
method Proposes a federated learning system with zero-shot data augmentation to mitigate statistical heterogeneity and improve fairness.
result Empirical results show improved test accuracy and fairness across clients.
Study creates a global living index to assess quality of life.
problem Long-term impacts of global economic changes on living conditions.
method Machine learning framework combining socio-economic factors.
result Developed a practical tool for policymakers to identify areas needing improvement.
Bayesian optimization improves efficiency with semi-supervised learning.
problem Efficiently find global optima of expensive functions.
method Density ratio estimation combined with semi-supervised learning.
result Improved accuracy in identifying global optima with unlabeled data.
New sampling and identity-testing methods for mixtures of distributions that don't satisfy approximate tensorization of entropy.
problem Sampling and identity-testing for mixtures of distributions that don't satisfy approximate tensorization of entropy.
method Fast mixing of Glauber dynamics and efficient identity-testers in the coordinate-conditional sampling access model.
result Efficient identity-testers for mixtures of ATE distributions in the coordinate-conditional sampling access model.
Dynamic defense against Byzantine poisoning in federated learning.
problem Byzantine poisoning attacks on federated learning models.
method Dynamic federated aggregation operator to filter out adversarial clients.
result Dynamic selection of clients improves global learning model performance.
WAFFLE embeds watermarks in federated learning models without access to training data.
problem Model theft in federated learning due to lack of centralized control.
method Retraining step at server after model aggregation, generating backdoor as watermark.
result Efficient embedding of resilient watermark with negligible accuracy loss.
Novel evolutionary strategy solves stochastic constrained optimization problems.
problem Optimizing objective functions with stochastic constraints in reinforcement learning.
method Design of a novel optimization algorithm with a sufficient decrease mechanism for stochastic constrained problems.
result Demonstrated convergence of the algorithm on control tasks and constrained optimization problems.
Deep generative models improve global precipitation forecasts.
problem Accurately forecasting extreme rainfall is challenging and costly.
method Trained a Conditional Generative Adversarial Network (CorrectorGAN) to correct and super-resolve global precipitation forecasts.
result CorrectorGAN produces high-resolution, bias-corrected forecasts in seconds.
Method finds compatible features for subsets of data.
problem Selecting relevant features for subsets of data.
method Reframe feature selection as finding sections of quiver representations, using quiver Laplacians.
result Eigenvectors of quiver Laplacian yield compatible features.
With the advent of Deep Learning (DL) techniques, especially Generative Adversarial Networks (GANs), data augmentation and generation are quickly evolving domains that have raised much interest recently. However, the DL techniques are data demanding and since, medical data is not easily accessible, they suffer from dat…
WassFFed addresses fairness in Federated Learning by ensuring consistency between local and global models.
problem Achieving fairness in Federated Learning where data is distributed among diverse user groups.
method WassFFed employs a Wasserstein barycenter calculation to aggregate local models' outputs, ensuring consistency and fairness.
result WassFFed outperforms existing approaches in balancing accuracy and fairness.
Federated learning improves keyboard suggestions without user data.
problem Improving search suggestion quality without direct user data access.
method Applied federated learning for global-scale model training and deployment.
result Significant quality increases in search suggestions.
Safe-M3-UCRL learns safe policies for multi-agent systems with global constraints.
problem Global constraints in mean-field reinforcement learning for multi-agent systems.
method Safe-M3-UCRL uses epistemic uncertainty and log-barrier approach to ensure constraints satisfaction. result Safe-M3-UCRL learns safe policies for multi-agent systems with global constraints. Coded Federated Learning speeds up training in edge computing networks.
problem Slow convergence in Federated Learning due to heterogeneity and stochastic fluctuations.
method Exploiting statistical properties of compute and communication delays, distributed kernel embedding, and random Fourier features.
result Significant performance gains for CodedFedL in distributed non-linear regression and classification problems.