Paper presents a new framework for sequence classification.
problem Sequence classification in real-world applications.
method Reference-based sequence classification framework.
result New sequence classification algorithms achieve comparable accuracy.
Bias and heterogeneity in peer assessment can lead to the issue of unfair scoring in the educational field. To deal with this problem, we propose a reference ranking method for an online peer assessment system using HodgeRank. Such a scheme provides instructors with an objective scoring reference based on mathematics.
A new method samples from multi-modal distributions without hyperparameter tuning.
problem Sampling from multi-modal distributions is challenging and requires tuning hyperparameters.
method Learned Reference-based Diffusion Sampler (LRDS) that learns a reference model on high-density regions and uses it to train a diffusion-based sampler.
result LRDS best exploits prior knowledge on multi-modal distributions compared to competing algorithms.
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can …
RB-Modulation trains free diffusion models without external adapters.
problem Training-free personalization of diffusion models with style and content control.
method Stochastic optimal control with a style descriptor and cross-attention aggregation.
result Precise content and style extraction and control without external adapters.
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating fr…
New deep-unfolded network improves video background separation.
problem Video foreground-background separation.
method Deep unfolding of an iterative RPCA algorithm with adaptive learning.
result Proposed network outperforms state-of-the-art in video foreground-background separation.
Considering the driving habits which are learned from the naturalistic driving data in the path-tracking system can significantly improve the acceptance of intelligent vehicles. Therefore, the goal of this paper is to generate the prediction results of lateral commands with confidence regions according to the reference…
New algorithm reduces RL policy optimization gap.
problem Insufficient theoretical understanding of policy optimization methods.
method Reference-based Policy Optimization with Stable at Any Time guarantee (RPO-SAT)
result Achieves nearly minimax optimal policy-based algorithm for tabular RL.
This paper re-evaluates hyperparameters for fine-tuning pre-trained models.
problem Current hyperparameter settings for fine-tuning are often ad-hoc and fixed.
method Empirical evaluation of learning rate, batch size, and momentum for fine-tuning.
result Optimal hyperparameters are not only dataset-dependent but also sensitive to domain similarity.
Introduces AMLB, an open benchmark for AutoML frameworks.
problem Challenges in comparing AutoML frameworks.
method Open benchmark with 9 AutoML frameworks, 71 classification, 33 regression tasks, multi-faceted analysis, Bradley-Terry trees.
result Differences in AutoML frameworks' performance and trade-offs.
EagerPy simplifies writing code for multiple deep learning frameworks.
problem Code duplication and framework lock-in when using different deep learning libraries.
method Integrates multiple frameworks into a single Python framework.
result Automatic compatibility across PyTorch, TensorFlow, JAX, and NumPy.
OBSER framework infers sub-environments from objects, outperforming scene-based methods.
problem Zero-shot recognition of environments from object distributions.
method Bayesian framework using metric and self-supervised learning models to estimate object distributions in latent space.
result OBSER framework reliably performs inference in open-world and photorealistic environments, outperforming scene-based methods.
We propose a unified framework to speed up the existing stochastic matrix factorization (SMF) algorithms via variance reduction. Our framework is general and it subsumes several well-known SMF formulations in the literature. We perform a non-asymptotic convergence analysis of our framework and derive computational and …
A new forecasting framework uses suboptimal embeddings to improve multivariate time series prediction.
problem Randomly selecting embeddings or brute force methods often lead to suboptimal forecasts.
method Develops a framework that uses various suboptimal embeddings obtained via combinatorial optimization.
result Achieves the best results among existing frameworks for various datasets.
Extends DeTEcT framework for token economies with dynamic and probabilistic parameters.
problem Modeling wealth distribution in token economies with dynamic and probabilistic parameters.
method Introduces four parametrization techniques: dynamic vs static, probabilistic vs non-probabilistic.
result Derives existing wealth distribution models from DeTEcT framework with added restrictions.
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.
RYU framework constructs safe regions for optimization problems.
problem Optimization problems with specific component functions.
method RYU framework for constructing safe regions.
result RYU framework improves upon state-of-the-art methods.
A modular framework for knowledge distillation simplifies experiments and reproducibility.
problem Difficulty in reproducing knowledge distillation studies due to lack of generalized frameworks.
method A configuration-driven PyTorch framework for knowledge distillation studies.
result Demonstrates efficient training strategies and various knowledge distillation methods.
We give a combinatorial characterization of generic minimally rigid reflection frameworks. The main new idea is to study a pair of direction networks on the same graph such that one admits faithful realizations and the other has only collapsed realizations. In terms of infinitesimal rigidity, realizations of the former…
A new clustering framework using fixed points for data analysis.
problem Lack of unified understanding and application of clustering algorithms in data analysis.
method Restated model-based clustering using fixed point theory, iteratively constructing contraction maps to find cluster centers.
result Unified clustering framework reveals convergence mechanisms and interconnections among clustering algorithms.
The paper analyzes frameworks for integrating sustainability into investment decisions.
problem Understanding how ESG factors influence investment choices.
method Examined and analyzed various theoretical frameworks including Behavioral Finance, Modern Portfolio, and Risk Management.
result Investors increasingly integrate ESG factors to optimize financial outcomes and societal goals.
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…
In this paper we introduce a flexible HJM-type framework that allows for consistent modelling of intraday, spot, futures, and option prices. This framework is based on stochastic processes with economic interpretations and consistent with the initial term structure given in the form of a price forward curve. Furthermor…
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.
PySAD offers a unified Python framework for efficient streaming anomaly detection.
problem Efficient anomaly detection in streaming data with strict constraints.
method Unified architecture with 17+ streaming algorithms, specialized components, and support for multiple learning paradigms.
result PySAD enables real-time processing with bounded memory and is compatible with other Python frameworks.
TorchGAN simplifies GAN training and evaluation with a flexible framework.
problem Training and evaluating GANs can be complex and time-consuming.
method Modular design, extensibility, built-in support for popular models and metrics.
result TorchGAN achieves near-zero overhead compared to vanilla PyTorch.
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.
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.
DIVA clusters dynamic data without needing cluster count, outperforming baselines.
problem Clustering complex, dynamic data without prior knowledge of cluster count.
method Nonparametric Dirichlet Process Mixtures with memoized online variational inference.
result DIVA outperforms state-of-the-art in classifying complex data with changing features.
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 …
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.
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.
ProbDR framework interprets DR algorithms as probabilistic inference.
problem Efficiently compressing high-dimensional data into lower dimensions.
method ProbDR variational framework that treats DR as probabilistic inference.
result ProbDR unifies various DR algorithms and enables probabilistic reasoning.
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions. By setting the truncation points appropriately, we are able to generate various types of nonlinearities within a unified framework, including sigmoid, tanh and ReLU, the most commonly used nonlinearities in neural…
Framework fuses satellite and OSM data for local climate zone classification.
problem Heterogeneity of multimodal satellite and OSM data.
method Separate processing of satellite and OSM data, followed by fusion at the model level.
result Framework outperforms baseline by 6-2% in classification accuracy.
The new framework for finance is proposed. This framework based on three known approaches in econophysics. Assumptions of the framework are the following: 1. For the majority of situations market follows non-arbitrage condition. 2. For the small number of situations market influenced by the actions of big firms. 3. If …
Framework solves bilevel optimization on manifolds.
problem Solving bilevel optimization problems with manifold constraints.
method Hypergradient estimation strategies on manifolds, convergence and complexity analyses.
result Efficacy demonstrated through various applications.
We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle filters. Howe…
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.
We extend our generic rigidity theory for periodic frameworks in the plane to frameworks with a broader class of crystallographic symmetry. Along the way we introduce a new class of combinatorial matroids and associated linear representation results that may be interesting in their own right. The same techniques immedi…
A game-theoretic framework identifies influential hyperparameters for neural networks.
problem Understanding which hyperparameters are most important for neural network performance.
method Employing Shapley Effects for global sensitivity analysis and Pareto front sets for identifying effective configurations.
result Reveals which hyperparameters are most influential for different objectives in neural networks.
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly infeasible for moderate to large scale datasets. A significant effort t…
Proposes a new framework for discount models.
problem Arbitrage-free dynamic framework for discount models.
method Derives general consistency conditions for factor models.
result Alternative to Heath--Jarrow--Morton framework for forward rates.
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…
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …
Unified statistical framework for LSTM model selection.
problem Model selection and hyperparameter tuning in LSTM networks is heuristic and computationally expensive.
method Proposes a statistical framework extending classical model selection ideas to LSTM networks.
result Improved performance of the proposed framework demonstrated on biomedical data.
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.