This paper surveys and benchmarks AutoML frameworks.
problem Reducing the need for data scientists in building machine learning applications.
method Survey and benchmark of popular AutoML frameworks on real data sets.
result Evaluation of 137 data sets from established AutoML benchmark suits.
A new framework learns to select samples for active learning.
problem Lack of labeled data for training active learning strategies.
method Learning to Sample (LTS) framework with a sampling and boosting model.
result Significantly outperforms baselines in limited label budgets.
New framework assesses and benchmarks ML methods for multivariate time series.
problem Benchmarking and explaining performance of machine learning methods.
method Proposes a new framework with systematized performance-explainability characteristics.
result Illustrates application to multivariate time series classifiers.
We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.
Paper analyzes and simplifies deep learning framework parallelism for better performance.
problem Optimizing performance in complex machine learning frameworks.
method Analyzed and quantified the impact of key design features on performance, providing guidelines.
result Performance tuning guidelines outperform default settings by 1.29x and 1.34x.
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition. Importantly, we extend the framework to incorporate hidden variables. We show how the framework can be ap…
Baconian simplifies MBRL experiments by providing a flexible framework.
problem Lack of reusable open-source frameworks for MBRL research.
method Developed a flexible and modularized framework, Baconian.
result Facilitates MBRL experiments by allowing customization and reuse of algorithms.
The paper introduces a new framework for off-policy reinforcement learning.
problem Improving off-policy reinforcement learning algorithms.
method Conceptual framework based on conditional importance sampling.
result Theoretical analysis and concrete algorithm investigation.
New RL framework learns optimal control policies directly.
problem Directly learning optimal control policies in complex systems.
method General Markov Decision Process (MDP) framework for reinforcement learning.
result Convergence and optimality of control-based RL methods established.
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expert…
In real-world applications, not all instances in multi-view data are fully represented. To deal with incomplete data, Incomplete Multi-view Learning (IML) rises. In this paper, we propose the Joint Embedding Learning and Low-Rank Approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete d…
CFFL framework improves fairness in FL without sacrificing accuracy.
problem Overfitting and lack of collaborative fairness in Federated Learning.
method CFFL framework uses reputation to ensure participants converge to different models.
result CFFL achieves high fairness, comparable accuracy, and better performance than Standalone and Distributed frameworks.
Chainer accelerates deep learning research with a flexible framework.
problem Slow development and application of deep learning models.
method Flexible, intuitive, high-performance framework with GPU acceleration and dynamic models.
result Accelerates deep learning research and development.
Unified framework supports lifelong learning properties.
problem Capturing desirable properties of human learning in machine learning.
method Unified framework with one central mechanism.
result Supports almost all desirable properties of human learning.
Framework measures learning task complexity, distinguishing from memorization.
problem Measuring and distinguishing learning from memorization in learning tasks.
method Introduces an asymmetric distance and a non-asymptotic framework to compute complexity.
result Framework can measure complexity in large-scale models and real-world datasets.
Unified LP framework for offline reward learning from human demonstrations and feedback.
problem Reward learning from human demonstrations and feedback with robustness and sample efficiency.
method A novel linear programming framework for offline reward learning.
result Unified LP framework achieves better performance compared to MLE.
A distributed framework protects privacy while maintaining fairness in machine learning.
problem Protecting personal demographic data while ensuring fair machine learning outcomes.
method A distributed framework with private third-party data communication, ensuring privacy and fairness.
result Four fair learning methods consistently outperform existing ones in fairness and accuracy across three real-world datasets.
Unified framework for Bayesian online learning in changing conditions.
problem Probabilistic online learning in non-stationary environments.
method BONE framework with three modelling choices and two algorithmic choices.
result Framework allows for reinterpreting and proposing new methods.
FPPDL framework ensures fairness and privacy in federated deep learning.
problem Overfitting, low utility, single-point-of-failure, lack of fairness in federated learning.
method Local credibility mutual evaluation mechanism and three-layer onion-style encryption scheme.
result FPPDL balances fairness, privacy, and accuracy in federated deep learning.
Generative framework unifies and improves personalized learning and estimation methods.
problem Statistical heterogeneity in client data motivates personalized learning models.
method Generative framework unifies and suggests new personalized learning and estimation algorithms.
result AdaPeD algorithm numerically outperforms known algorithms.
Framework improves human decision-making by learning representations.
problem Improving human decision-making performance conflated with machine accuracy.
method Mind Composed with Machine framework, incorporating human decision-making model into representation learning.
result Empirically demonstrated successful application to various tasks and representational forms.
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental …
Paper proposes a federated learning framework for relative fairness.
problem Traditional fairness in federated learning overlooks performance disparities between client subgroups.
method Uses a minimax problem approach to minimize relative unfairness, introducing a fairness index based on loss ratios.
result Empirical evaluations confirm the framework's effectiveness in maintaining model performance while reducing disparity.
A new learning framework mimics biological STDP for neural networks.
problem To create a neural network that learns like biological systems.
method Developed MSTDP framework using Spike-timing dependent plasticity rules.
result Framework can learn and generate patterns without additional supervision.
Proposes a deep reinforcement learning framework for dynamic multichannel access.
problem Efficient use of limited spectral resources in dynamic multichannel access.
method Deep actor-critic reinforcement learning framework for both single-user and multi-user scenarios.
result Demonstrates improved performance and adaptive ability compared to existing methods.
New PAC-Bayesian framework for flexible meta-learning.
problem Limitations in transferring knowledge between tasks in meta-learning.
method PAC-Bayesian theory applied to learning the learning algorithm.
result Flexibility in meta-learning mechanisms and improved prediction quality.
Mathematical framework for transfer learning feasibility and transfer risk.
problem Theoretical analysis of transfer learning.
method Reformulated transfer learning as an optimization problem, introduced transfer risk concept.
result Demonstrated the potential and benefits of incorporating transfer risk in transfer learning evaluation.
Bayesian online meta-learning framework tackles catastrophic forgetting in few-shot classification.
problem Catastrophic forgetting in few-shot classification problems.
method Bayesian online learning, meta-learning, Laplace approximation, variational inference.
result Framework effectively achieves goal of overcoming catastrophic forgetting in few-shot classification.
Paper extends meta-learning framework to non-convex settings with improved performance.
problem Learning from past tasks for faster future tasks in a sequential setting.
method Generalized online meta-learning framework to non-convex settings, introduced local regret as performance measure.
result The framework achieves logarithmic local regret and robustness to hyperparameter initialization.
Extends GENO framework for GPU optimization of constrained ML problems.
problem Constrained optimization in classical machine learning.
method Extends GENO framework to GPU optimization, specifying problems in a modeling language.
result Solvers on GPU outperform state-of-the-art approaches by several orders of magnitude.
RISE framework unifies and improves time series learning with missing data.
problem Learning from time series with missing data.
method RISE framework unifies and improves time series learning with missing data.
result RISE instances always benefit from encoders that learn representations for numerical values.
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs …
A new unsupervised contrastive learning framework improves time series representation learning.
problem Lack of labeled data in time series data.
method Proposes an unsupervised contrastive learning framework using a novel contrastive loss and data augmentation.
result Framework outperforms other approaches on univariate and multivariate time series, and benefits transfer learning.
We present a novel view that unifies two frameworks that aim to solve sequential prediction problems: learning to search (L2S) and recurrent neural networks (RNN). We point out equivalences between elements of the two frameworks. By complementing what is missing from one framework comparing to the other, we introduce a…
Unified framework for analyzing machine learning model attributions.
problem Lack of a general and theoretical framework for understanding attribution methods.
method Proposes a Taylor attribution framework to unify and analyze seven mainstream attribution methods.
result Established three principles for good attribution and empirically validated the Taylor reformulations.
Replicates and improves a deep learning framework for financial portfolio management.
problem Financial portfolio optimization problem
method Deep Reinforcement Learning Framework with EIIE topology, PVM, OSBL, and reward function
result Framework performs well in cryptocurrency market but less so in stock market
With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data a…
Framework improves self-play for cooperative multi-agent learning.
problem Evolutionary learning converges to bad local optima in multi-agent RL.
method Add imaginary rewards using peer prediction method to elicit truthful signals.
result State-of-the-art performance in predator prey, traffic junction and StarCraft tasks.
OGD proves robustness to Catastrophic Forgetting in Continual Learning.
problem Catastrophic Forgetting in Continual Learning with deep neural networks.
method Theoretical framework based on Neural Tangent Kernel for OGD.
result First generalization bound for SGD and OGD in Continual Learning.
BayGo framework optimizes decentralized learning in multi-agent networks.
problem Information heterogeneity in multi-agent networks.
method Bayesian learning and graph optimization framework with fast convergence.
result Estimation error decreases exponentially with each iteration.
Flexible embedding framework for diverse data types.
problem Limited applicability of existing embedding learning methods.
method A flexible framework using entity-relation-matrices and sampling mechanism.
result Framework outperforms state-of-the-art approaches in various tasks.
A new framework for neural network classification using vector quantization.
problem Learning a neural network classifier under the IB principle.
method Aggregated Learning framework, combining vector quantization and variational techniques.
result The effectiveness of Aggregated Learning verified through experiments.
A new AI framework optimizes expert labeling of unlabeled data.
problem Efficiently labeling unlabeled data for AI systems.
method Human-in-the-loop active learning framework with innovative query schemes.
result Higher accuracy and lower loss compared to other methods.
Unified framework for cost-sensitive ensemble learning.
problem Different misclassification costs in data.
method A unifying framework for cost-sensitive ensemble methods.
result Unified understanding and natural extensions of existing methods.
New framework generalizes neural network parameters to C∗-algebra for more efficient feature learning.
problem Efficient feature learning and adaptability of neural network models.
method Generalizes neural network parameters to C∗-algebra-valued parameters and combines models continuously. result Shows improved feature learning with limited data using the new framework.
Framework for lifelong learning using eigentasks to avoid forgetting and transfer knowledge.
problem Avoiding forgetting and transferring knowledge in lifelong learning.
method Eigentask framework: skills paired with generative models, wake-sleep cycle for learning and consolidation.
result Improved performance in supervised continual learning, evidence of forward knowledge transfer.
Unified framework for various learning problems via Multiple-Instance Learning.
problem Various learning problems including multi-class, complementarily labeled, multi-label, and multi-task learning.
method Reduction of various learning problems to Multiple-Instance Learning (MIL) with theoretical guarantees.
result Unified and simplified framework for designing and analyzing algorithms for various learning problems.
A new framework learns clustering and dimensionality reduction together.
problem Challenges in clustering high-dimensional data.
method Gradient-based manifold optimization for joint learning.
result Better performance compared to existing clustering algorithms.