Personalized federated learning for diverse client objectives.
problem Training a single global model across diverse local datasets is not optimal.
method Efficiently calculates optimal weighted model combinations for each client based on their specific objectives.
result Our method outperforms existing alternatives and enables new personalized features.
DFFL tackles federated learning with heterogeneous objectives and constraints.
problem Federated learning with clients having different objectives and feasible regions.
method Derived heterogeneity bounds for cost-vector distances and support-function/shape-distance terms. Lifted pointwise bounds to local-versus-federated excess-risk comparison.
result Federation is beneficial when the statistical advantage of pooling exceeds a client-specific heterogeneity penalty.
Boosting-inspired framework for federated learning with progressive model personalization.
problem Statistical heterogeneity across clients in federated learning.
method Construct an ensemble of personalized models, progressively increasing the depth of the personalized component while controlling its effective complexity.
result Consistently outperforms state-of-the-art PFL methods under heterogeneous data distributions.
Federated CycleGAN enables privacy-preserving image translation without central data.
problem Privacy and security issues in unsupervised image-to-image translation.
method Novel federated CycleGAN architecture that decomposes CycleGAN loss into client-specific local objectives.
result Federated CycleGAN achieves comparable performance to non-federated CycleGAN without central data exchange.
A new approach corrects bias in federated learning due to varying communication links.
problem Bias in federated learning due to non-uniform and time-varying communication links.
method Proposes Federated Postponed Broadcast (FedPBC) to correct bias in Federated Average (FedAvg).
result FedPBC converges to a stationary point of the global objective, overcoming bias caused by varying communication links.
Client adaptation improves federated learning performance with non-IID data.
problem Improving model performance in federated learning with non-identically and non-independently distributed data.
method Simulates heterogeneous clients to learn client-specific conditioning using a conditional gated activation unit.
result Client adaptation enhances model performance across balanced and imbalanced data sets from audio and image domains.
GC-FCP provides efficient federated CP with group-conditional coverage guarantees.
problem Uncertainty quantification in federated settings with distributed calibration data.
method Group-conditional federated conformal prediction (GC-FCP) using group-stratified coresets.
result GC-FCP offers efficient aggregation and calibration compared to centralized methods.
Paper tackles scalable VFL with data augmentation and amortized inference.
problem Collaborative model estimation across multiple clients with distinct covariates.
method Data augmentation, amortized variational approximation, factorized likelihoods.
result Scalable Bayesian VFL framework for various models.
Federated framework learns causal states to predict counterfactuals without centralizing data.
problem Decentralized counterfactual reasoning in coupled industrial systems with private data.
method Federated causal representation learning in state-space systems.
result Proves convergence to centralized oracle and provides privacy guarantees.
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means t…
A key factor in developing high performing machine learning models is the availability of sufficiently large datasets. This work is motivated by applications arising in Software as a Service (SaaS) companies where there exist numerous similar yet disjoint datasets from multiple client companies. To overcome the challen…
Federated learning is viewed as a hierarchical latent variable model for new algorithm development.
problem Training models privately across multiple clients while maintaining privacy and efficiency.
method Viewing federated learning as a hierarchical latent variable model and applying Expectation-Maximization (EM) algorithm.
result Proposes FedSparse, a federated learning algorithm that promotes sparsity and reduces communication and inference costs.
Study addresses covariate mismatch in federated learning, improving model accuracy.
problem Learning from clients with different feature sets in federated learning.
method Developed two approaches for linear prediction under covariate mismatch: plug-in estimator and impute-then-regress strategy.
result Proposed methods provide asymptotic and finite-sample learning rates, improving model accuracy.
SPIRE enables efficient federated learning for diffusion models by separating client-specific embeddings from a shared backbone.
problem Large diffusion models are impractical for federated learning due to their size.
method SPIRE separates the network into a global backbone and client-specific embeddings, enabling efficient finetuning.
result SPIRE achieves parameter-efficient finetuning, updating only a small fraction of weights.
Paper proposes CLAIR for efficient LLM fine-tuning across clients.
problem Fine-tuning large language models (LLMs) efficiently and collaboratively.
method Federated LoRA fine-tuning with Collaborative Low-rank Alignment and Identifiable Recovery (CLAIR).
result CLAIR achieves better performance and contamination detection compared to local fine-tuning.
Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.
problem Privacy and security concerns in traditional cloud-centric ML, especially in wearable devices.
method Develops a blockchain-enhanced federated edge learning (BFEL) framework based on FedCurv, incorporating fisher information matrix and public key encryption.
result Significant reduction in communication cost and high efficiency for federated training on non-iid and heterogeneous data.
Federated Learning prioritizes client data contributions for better model quality.
problem Privacy concerns and reluctance to share private data in machine learning.
method Prioritizes client data contributions in Federated Learning by assigning scores based on defined criteria.
result The proposed approach yields a higher quality global model compared to standard Federated Learning.
We present a novel approach to weakly supervised object detection. Instead of annotated images, our method only requires two short videos to learn to detect a new object: 1) a video of a moving object and 2) one or more "negative" videos of the scene without the object. The key idea of our algorithm is to train the obj…
A new method for optimizing hierarchical multi-objective problems.
problem Symmetry and neglect of objective hierarchy in existing multi-objective methods.
method Priority-Constrained Descent (PCD) framework exploiting hierarchical objective structures.
result Pareto dominance and better per-objective performance with secondary progress guarantees.
LORL learns object-centric representations from vision and language.
problem Learning disentangled, object-centric scene representations from vision and language.
method LORL integrates unsupervised object discovery and segmentation with language input to learn object-centric concepts.
result LORL improves unsupervised object discovery methods and aids downstream tasks.
The paper introduces a new metric to quantify uncertainty's impact on multiple objectives.
problem Quantifying the impact of uncertainty on multiple objectives in complex systems.
method Proposes the mean multi-objective cost of uncertainty (multi-objective MOCU) to quantify uncertainty.
result Demonstrates the effectiveness of the multi-objective MOCU in real-world applications.
A new Adamize method improves multi-objective recommender systems.
problem Improving recommendation systems with multiple conflicting objectives.
method Developed a multi-objective model-agnostic Adamize method that corrects and stabilizes gradients.
result Significant improvements in recommendation systems, measured by hypervolume, coverage, and spacing.
The paper proposes a framework to reason about object dynamics for faster reinforcement learning.
problem Current reinforcement learning approaches lack prior knowledge about the environment, limiting efficiency.
method Integrates object dynamics and behavior into reinforcement learning to improve efficiency.
result Demonstrates the need for reasoning about object behavior and dynamics, leading to faster learning.
ROOTS learns to represent and render 3D scenes with object-centric models.
problem Learning to represent and render 3D scenes with object-centric compositionality.
method Probabilistic generative model for learning object representations and scene rendering from partial observations.
result The model can infer 3D object representations and render scenes from arbitrary viewpoints.
Object-centric learning improves generalization and robustness in multi-object scenes.
problem Improving generalization and robustness in neural networks for scenes with multiple objects.
method Training state-of-the-art unsupervised models on multi-object datasets and evaluating segmentation metrics and downstream tasks.
result Object-centric representations are useful for downstream tasks and generally robust to most distribution shifts affecting objects, but less so for less structured shifts.
ADS explains object differences by quantifying and removing underlying properties.
problem Explaining differences between two object images.
method Align-Deform-Subtract (ADS) framework that uses semantic alignments and iterative quantification/removal of differences.
result ADS provides disentangled error measures explaining object differences in terms of underlying properties.
Proposes class-agnostic object detection to handle all objects without class labels.
problem Difficulty and cost in creating annotated datasets limit conventional object detection models to specific object types.
method Proposes class-agnostic object detection as a new problem and proposes training and evaluation protocols. Uses adversarial learning to exclude class-specific information.
result Adversarial learning improves class-agnostic detection efficacy.
Many objective Bayesian optimization tackles redundant objectives in expensive black-box functions.
problem Efficiently optimizing multiple expensive and noisy black-box functions with redundant objectives.
method Proposes a metric to identify redundant objectives and a Bayesian optimization algorithm to stop evaluating them.
result Reduces computational cost by stopping evaluation of redundant objectives, improving efficiency.
The paper introduces a new method to measure the shape relations between biological objects using r-parallel sets.
problem The influence of neighboring objects on the shape and function of biological objects.
method The authors develop a theory based on spatial point processes to measure the geometrical interaction between objects.
result The proposed measures provide detailed information about the shape of individual objects and their interactions.
We introduce a rich model for multi-objective clustering with lexicographic ordering over objectives and a slack. The slack denotes the allowed multiplicative deviation from the optimal objective value of the higher priority objective to facilitate improvement in lower-priority objectives. We then propose an algorithm …
A new method for faster multi-objective optimization by evaluating objectives separately.
problem Finding the Pareto front of trade-offs between multiple objectives efficiently.
method Knowledge Gradient with decoupled evaluations, accounting for different costs.
result The method significantly outperforms existing approaches in terms of evaluation cost.
Paper presents attacks on real-time object detection systems.
problem Adversarial attacks on real-time object detection systems.
method Three targeted adversarial Objectness Gradient attacks (TOG).
result Adversarial attacks can cause object-vanishing, object-fabrication, and object-mislabeling.
Top 8 robotic vision systems tackled lifelong object recognition challenges.
problem Lifelong learning in robotic vision for varied, dynamic environments.
method Design of a dataset with diverse conditions and rules for evaluation.
result Robotic vision systems improved over time with dynamic object appearances.
Generative models create indistinguishable adversarial objects for object detection.
problem Creating unrestricted adversarial examples for object detection.
method Search over latent space of GAN for adversarial objects.
result Generated adversarial objects are indistinguishable from non-adversarial objects and transferable.
The ability to detect and track objects in the visual world is a crucial skill for any intelligent agent, as it is a necessary precursor to any object-level reasoning process. Moreover, it is important that agents learn to track objects without supervision (i.e. without access to annotated training videos) since this w…
Bayesian methods improve tracking multiple objects through dynamic dependencies.
problem Tracking multiple objects with time-varying cardinality and unordered measurements.
method Employing Bayesian nonparametric models, specifically dependent Dirichlet and Pitman-Yor processes, for state estimation and Monte Carlo sampling for trajectory learning.
result The proposed methods outperform existing algorithms in estimating the time-varying number of objects and identifying object associations.
Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a probabilistic generative world model for learning SCALab…
Unified information-theoretic objectives for training deep neural networks.
problem Difficulty in computing information-theoretic quantities for large deep neural networks.
method Review and unify competing objectives, develop surrogate objectives.
result Surrogate objectives allow applying information bottleneck to modern neural network architectures.
BFPM improves machine learning accuracy by considering object types and memberships flexibly.
problem Inaccurate learning due to missing important parameters.
method BFPM relaxes fuzzy and possibilistic constraints, allowing arbitrary object memberships and movement analysis.
result BFPM enhances object movement analysis and provides flexible search space.
New framework converts multi-objective to single-objective optimisation.
problem Solving multi-objective optimisation problems.
method Formalises scalarisation into mathematical framework, uses R2 utilities.
result R2 utilities are monotone and submodular, optimised by greedy algorithms.
Generative model separates objects, positions, and appearances in images.
problem Generating complete images with accurate object positions and appearances.
method Structured latent representation that separates objects, positions, and appearances.
result Generative model can infer object decompositions and accurate depth orderings.
New method improves domain generalization by matching object representations.
problem Existing domain generalization methods fail to generalize to unseen domains.
method Proposes matching-based algorithms to match object representations across domains.
result MatchDG algorithm matches ground-truth object representations and improves out-of-domain accuracy.
Proposes a novel algorithm for multi-objective reinforcement learning.
problem Challenges in setting numerical preferences for objectives in different units and scales.
method Learn action distributions for each objective and use supervised learning to fit a parametric policy.
result Demonstrates effectiveness on robotics tasks, allowing tracing out the space of nondominated solutions.
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objec…
This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.
problem Training models for multilingual, multi-task speech processing is hampered by conflicting objectives.
method Investigates three multi-objective MSP formulations and introduces a lightweight layer-selection mechanism.
result A bi-level recipe outperforms standard flat optimization in state-of-the-art MSP models.
In this paper, we demonstrate a physical adversarial patch attack against object detectors, notably the YOLOv3 detector. Unlike previous work on physical object detection attacks, which required the patch to overlap with the objects being misclassified or avoiding detection, we show that a properly designed patch can s…
Simple greedy algorithms can excel in multi-objective bandits with multiple good arms.
problem Optimizing multiple objectives in bandits is traditionally harder.
method Introduced greedy algorithms that exploit multiple good arms for multiple objectives.
result Simple greedy algorithms achieve strong performance in multi-objective bandits.
GENESIS-V2 infers unordered object representations without iterative refinement.
problem Unsupervised learning of unordered object representations for complex images.
method Stochastic stick-breaking process for clustering pixel embeddings.
result GENESIS-V2 outperforms recent baselines in unsupervised image segmentation and scene generation.