A probabilistic framework for online test-time adaptation
problem Adapting models to new data under distributional shift
method State-space modelling architecture
result Characterizing parameter learning, time evolution, prior tuning, and prediction
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48 results for “adaptive framework”
Develops a generic two-layer framework for adaptive ABMs.
problem Bi-level adaptation problem in ABMs: agents adapt to environment, and environment adapts to agents.
method Formalizes bi-level problem as a Stackelberg game with conditional policies, solving coupled non-linear equations.
result Unified framework for adaptive ABMs, addressing traditional ABM limitations.
A new adaptive pricing framework for perpetual protocols using liquidity curves and on-chain oraclesq-fin.TR
Adaptive pricing framework for perpetual contracts using liquidity curves and oracles.
problem Ensuring stable and predictable pricing for perpetual contracts.
method Uses liquidity curves and on-chain oracles with parabolic and sigmoid functions to quote prices and fees.
result Ensures pricing stability and predictability through adaptive pricing framework.
Proposes a graph embedding framework for domain adaptation.
problem Improving performance on small datasets using related large datasets.
method Formulates domain adaptation as a graph embedding problem, learning a feature transformation end-to-end.
result Simple instantiation leads to state-of-the-art performance on benchmarks.
We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up t…
Develops adaptive framework for estimating survival effects with censoring.
problem Estimating causal effects in survival data with censoring.
method Derives semiparametric efficiency bound, proposes efficiency-optimal allocation policy, and develops Adaptive Survival Estimator (ASE).
result ASE achieves asymptotic normality via martingale central limit theorem and demonstrates efficiency gains over uniform randomization.
Adaptive variational Bayes framework improves inference adaptively.
problem Lack of general and computationally tractable variational Bayes method for adaptive inference.
method Proposes a novel adaptive variational Bayes framework combining variational posteriors over individual models.
result Adaptive variational Bayes achieves optimal contraction rates adaptively under general conditions.
Prototype extraction framework for domain adaptation.
problem Statistical distance minimization issues in unsupervised domain adaptation.
method Memory and computation-efficient probabilistic framework for class prototype extraction and feature alignment.
result Competitive performance with state-of-the-art methods, no additional model parameters required.
Optimizes biomolecular simulations by ranking adaptive sampling policies.
problem Efficiently sampling biomolecular systems to capture complex dynamical behaviors.
method Metric-driven ranking of adaptive sampling policies to identify the optimal policy for each round.
result Different adaptive sampling policies lead to faster convergence and improved sampling performance.
A decentralized approach for multi-source domain adaptation.
problem Transfer knowledge from multiple related domains to an unlabeled target domain.
method Federated Dataset Dictionary Learning (FedDaDiL) framework, eliminating central server, using Wasserstein barycenters.
result Our decentralized approach effectively adapts source domains to an unlabeled target domain.
Modeling supply chain disruptions from climate hazards with adaptive firms.
problem Systemic physical climate risk in supply chains.
method Agent-based model integrating geospatial hazards and firm adaptation.
result Firms' adaptive strategies reduce disruption by 48%.
Unified framework for adaptive connection sampling in GNNs improves performance and robustness.
problem Over-smoothing and over-fitting in deep GNNs.
method Adaptive connection sampling trained jointly with GNN model parameters.
result Adaptive connection sampling mathematically equivalent to Bayesian GNNs approximation.
Study introduces AMVP and AMRR for dynamic portfolio optimization in volatile markets.
problem Optimizing portfolios in volatile and nonstationary financial markets.
method Adaptive Minimum-Variance Portfolio (AMVP) framework with ARFIMA-FIGARCH processes and non-Gaussian innovations.
result Demonstrated superior performance in risk reduction and portfolio stability during market breaks.
We propose a general framework for studying adaptive regret bounds in the online learning framework, including model selection bounds and data-dependent bounds. Given a data- or model-dependent bound we ask, "Does there exist some algorithm achieving this bound?" We show that modifications to recently introduced sequen…
LALR adapts learning rate for faster convergence in regression and neural nets.
problem Finding optimal learning rates for faster convergence in regression and neural networks.
method Lipschitz continuity theory applied to Mean Absolute Error and Quantile loss functions.
result Adaptive learning rate policy enables up to 20x faster convergence.
ACS is an interactive framework for model-free selection with guaranteed error control.
problem Model-free selection with rigorous error control.
method Adaptive conformal selection with human-in-the-loop data exploration and new information incorporation.
result ACS provides concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating new data.
Proposes a label propagation framework for domain adaptation.
problem Subpopulation shift in machine learning domains.
method Label propagation based on a teacher classifier trained on source domain.
result End-to-end finite-sample guarantees on domain adaptation algorithm.
A cost-effective framework for gradual domain adaptation using multifidelity.
problem Degrading prediction performance due to large domain distance.
method Combines multifidelity and active domain adaptation.
result Improves prediction performance with reduced sample cost.
Adaptive Threshold Samplingstat.ML
Sampling is a fundamental problem in computer science and statistics. However, for a given task and stream, it is often not possible to choose good sampling probabilities in advance. We derive a general framework for adaptively changing the sampling probabilities via a collection of thresholds.In general, adaptive samp…
GIST adapts HMC by tuning parameters based on position and momentum.
problem Locally adaptive sampling in Hamiltonian Monte Carlo.
method GIST uses Gibbs sampling to adaptively tune HMC parameters.
result GIST improves sampling efficiency for high-dimensional models.
Paper proposes a new framework for predictive optimization without training data.
problem Prediction in a new domain without training samples.
method Proposes a simple framework for predictive optimization with zero-shot domain adaptation.
result Demonstrates the potential usefulness of the proposed framework through numerical experiments.
Enhances physics-informed neural networks with adaptive sampling and weighting.
problem Challenges in training physics-informed neural networks on complex problems.
method Hybrid adaptive sampling and weighting method.
result Consistently improves prediction accuracy and training efficiency.
This paper improves Adam's performance in machine learning tasks.
problem Improving the generalization ability of adaptive gradient methods.
method Develops a control theoretic framework to propose AdamSSM, a new variant of Adam.
result AdamSSM improves generalization accuracy and convergence compared to recent adaptive gradient methods.
Adaptive framework improves nonparametric dimensionality reduction.
problem Optimal hyper-parameter tuning for nonparametric dimensionality reduction.
method Adaptive framework using intrinsic dimension estimator and optimal local neighbourhood sizes.
result Significant improvements in various learning tasks through better low-dimensional visualizations.
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself a…
FedDuA adapts global learning rate for federated learning.
problem Slow convergence in federated learning due to dataset and parameter space heterogeneity.
method FedDuA uses mirror descent to adaptively select global learning rate based on inter-client and coordinate-wise heterogeneity.
result FedDuA achieves minimax optimal convergence for convex objectives and outperforms baselines in various settings.
We describe a framework for deriving and analyzing online optimization algorithms that incorporate adaptive, data-dependent regularization, also termed preconditioning. Such algorithms have been proven useful in stochastic optimization by reshaping the gradients according to the geometry of the data. Our framework capt…
Adaptive strategies reduce pension fund costs and risks.
problem Managing longevity and volatility risks in pension funds.
method Modular simulation framework with customizable metrics.
result Substantial reduction in pension plan costs and default risk.
Framework adapts to new tasks based on prior knowledge.
problem Models struggle to adapt to novel tasks without direct experience.
method Learned task representations and meta-mappings to transform them.
result Meta-mapping achieves 80-90% performance on novel tasks.
We consider the optimal solutions to the trade execution problem in the two different classes of i) fully adapted or adaptive and ii) deterministic or static strategies, comparing them. We do this in two different benchmark models. The first model is a discrete time framework with an information flow process, dealing w…
Proposes a framework to improve domain adaptation without labeled data.
problem Improving adaptability and preserving intrinsic data structure in unsupervised domain adaptation.
method Discriminative Manifold Propagation framework using soft labels and manifold metric alignment.
result The method achieves better transferability and discriminability compared to existing approaches.
Framework improves gradient estimation for faster training convergence.
problem Efficiently estimating noisy gradients in stochastic optimization.
method Dynamic adaptive importance sampling combining multiple distributions.
result Adaptively weighted multiple importance sampling yields superior gradient estimates.
A new method for analyzing adaptive experiments using kernel treatment effects.
problem Efficiently analyzing adaptive experiments that adjust treatment assignments based on outcomes.
method Kernel Treatment Effects (KTE) framework combining RKHS scores and witness functions.
result Effective for both mean shifts and higher-moment differences, outperforming adaptive baselines.
Develops a flexible batched experimentation framework for limited adaptivity.
problem Challenges of continual reallocation in bandit algorithms with delayed feedback.
method Computational framework leveraging Gaussian sequential experiment and dynamic programming.
result Improves statistical power over standard methods, even compared to Bayesian bandit algorithms.
FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Financeq-fin.CP
FinFlowRL learns from experts to optimize financial control in changing markets.
problem Traditional finance control methods fail in real-world, non-stationary markets.
method Imitation-Reinforcement Learning framework that pretrains on expert strategies and finetunes in noise space.
result Consistently outperforms individually optimized experts across diverse market conditions.
New framework for domain adaptation using hierarchical optimal transport.
problem Improving domain adaptation when source and target data distributions differ.
method Proposes a new theoretical framework and hierarchical Wasserstein distance.
result Provides more explicit generalization bounds and aligns specific structures for successful adaptation.
New framework using Jensen-Shannon divergence improves domain adaptation theory.
problem Incoherence between empirical domain adversarial training and theoretical -divergence.
method Established new theoretical framework based on Jensen-Shannon divergence, derived bi-directional upper bounds.
result Framework exhibits flexibilities for various transfer learning problems.
This paper explains why Adam generalizes worse than SGD by analyzing its components.
problem Understanding why Adam generalizes worse than Stochastic Gradient Descent (SGD).
method Diffusion theoretical framework to disentangle the effects of Adaptive Learning Rate and Momentum.
result Adaptive Learning Rate helps escape saddle points but not select flat minima, while Momentum provides a drift effect to help pass through saddle points.
New adaptive first-order methods improve on quasi-Newton variants.
problem Designing efficient gradient methods for practical applications.
method Online scaled gradient methods (OSGM) with new adaptive methods OSGM-Best.
result OSGM-Best matches quasi-Newton variants but requires less memory and cheaper iterations.
Develops a new GLM framework for claims reserving with adaptive estimation.
problem Accurate assessment of claims reserves with dynamic and dependent claim activity.
method Multivariate evolutionary GLM framework with adaptive particle filtering algorithm.
result Adaptive estimation of evolving factors improves claims reserve accuracy.
Paper proposes a DNN-driven AF framework for improved generalization.
problem Generalization challenge in adaptive filtering.
method Structural embedding of DNN into AF system, using maximum likelihood as implicit cost function.
result Demonstrates improved generalization capability through extensive experiments.
Domain adaptation framework identifies latent variables for target distribution identifiability.
problem Unsupervised domain adaptation without identifiable joint distribution of features and labels.
method Formulated latent variable model with invariant and changing components, constrained domain shift to influence only changing components.
result Joint distribution of data and labels in target domain is identifiable under mild conditions.
ELSA efficiently adapts to label shift without post-prediction calibrations.
problem Domain adaptation with label shift across training and testing datasets.
method Moment-matching framework based on influence function geometry; solves linear systems for adaptation weights.
result ELSA estimator is -consistent and asymptotically normal, achieving state-of-the-art estimation performance.
MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptationcs.LG
New framework improves model reliability under distribution shifts.
problem Lack of formal guarantees connecting shift magnitude to prediction reliability in TTA methods.
method Develops a PAC-Bayesian framework interpreting MMD-balls as credal sets.
result Establishes generalization bounds and provides epistemic uncertainty quantification.
Safe Optimization of an Industrial Refrigeration Process Using an Adaptive and Explorative Frameworkmath.OC
Paper optimizes industrial refrigeration using adaptive exploration.
problem Challenges in optimizing real-time industrial processes with unknown characteristics and safety constraints.
method Adaptive and explorative real-time optimization framework with Gaussian process uncertainty quantification.
result Approach increases energy efficiency of refrigeration process, approximating complete information solutions.
New framework tackles multi-source domain adaptation with optimism and consistency.
problem Adjusting mixture distribution weights and ensuring low error on target domain.
method Mildly optimistic objective function and consistency regularization.
result Beats current state of the art in multi-source domain adaptation.
Adaptive sampling detects local concept drift with limited labels.
problem Detecting local concept drift in dynamic environments with scarce labels.
method Combines residual-based exploration and exploitation with EWMA monitoring.
result Superior performance in label efficiency and drift detection accuracy.
Adaptive PCR improves panel data analysis with uniform guarantees.
problem Adaptive data collection in panel data settings.
method Adapting PCR to online settings using martingale concentration.
result Time-uniform guarantees for adaptive PCR in panel data.