Probabilistic models predict neural network performance across varying hyperparameters.
problem Predicting neural network performance with different hyperparameters.
method Probabilistic models based on random forests and Bayesian recurrent neural networks.
result Models outperform state-of-the-art hyperparameter optimization methods.
Method controls extrapolation in prediction profiles for statistical and machine learning models.
problem Avoiding invalid predictions due to extrapolation in prediction profiles.
method Genetic algorithm optimization over constrained factor regions.
result Optimal factor settings without constraint are often invalid and extrapolated.
New models extrapolate false alarms in ASV without new data.
problem Reliable extrapolation of false alarm rates in ASV without new speaker data.
method Generative models in ASV score space for arbitrary systems.
result Models accurately extrapolate false alarm rates for large speaker populations.
A new principle for extrapolating regression outside training data.
problem Regression extrapolation when predictions are outside training data range.
method Data-adaptive marginal transformation and simple relationship assumption.
result Progression method offers guarantees on approximation error beyond training data range.
Paper accelerates NMF algorithms using extrapolation.
problem Efficiently solving nonnegative matrix factorization problems.
method Inspired by convex optimization, uses extrapolation for two-block exact coordinate descent algorithms.
result Significantly accelerates NMF algorithms on various data sets.
A new method improves language model extrapolation without changing training.
problem Improving language model performance on sequences not seen during training.
method Perturbation-based training procedure for language models.
result The method consistently improves out-of-support prediction.
LC-PFN predicts learning curve performance more accurately and faster than MCMC.
problem Bayesian extrapolation of learning curves is computationally expensive and overly restrictive.
method Prior-Data Fitted Neural Networks (PFNs) for approximate Bayesian inference.
result LC-PFN outperforms MCMC in accuracy and is significantly faster.
The paper explores how to make neural networks extrapolate longer sequences.
problem Neural networks struggle to extrapolate beyond seen data, especially for longer sequences.
method The authors propose a model with separate content- and location-based attention mechanisms.
result Models with the proposed attention mechanisms are better at extrapolating longer sequences.
New methods improve neural network extrapolation to long sequences.
problem Neural networks struggle with extrapolation to very long or adversarial sequences.
method Activation binning and localized differentiable memory architecture.
result No extrapolation errors detected within memory constraints.
I study the behavior and the performance of the long-term forecasts issued by financial analysts with respect to the Extrapolation Hypothesis. That hypothesis states that investors, extrapolating from the firms' recent performances, are too optimistic about growth and large firms and too pessimistic about value and sma…
T-REX improves reinforcement learning from suboptimal demonstrations.
problem Inability of existing IRL methods to significantly outperform the demonstrator.
method Trajectory-ranked Reward EXtrapolation (T-REX) algorithm.
result T-REX outperforms state-of-the-art methods on Atari and MuJoCo tasks.
Diverging Flows detects extrapolations in flow models, ensuring reliable predictions.
problem Flow models extrapolate into invalid data, leading to silent failures.
method Structurally enforce inefficient transport for off-manifold inputs.
result Effective detection of extrapolations without compromising predictive fidelity or inference latency.
This paper studies how gradient descent in control systems can perform well on unseen data.
problem The extent of a learned controller's ability to extrapolate to unseen initial states.
method Theoretical study of policy gradient in Linear Quadratic Regulator (LQR) problems, focusing on the role of exploration.
result The performance of a learned controller on unseen initial states depends on the degree of exploration induced by the system.
A new Lévy process kernel model for robust function extrapolation.
problem Kernel uncertainty in Gaussian process predictions for long-range extrapolation.
method Modeling spectral mixture density with a Lévy process to form a distribution over kernels.
result Automatic and data-efficient learning, long-range extrapolation, and state-of-the-art predictive performance.
Paper improves PINNs' extrapolation by TL and adaptive AFs.
problem PINNs' poor extrapolation performance and sensitivity to AFs.
method Transfer learning within an extended domain and adaptive activation functions.
result Average 40% reduction in relative L2 error and 50% in mean absolute error in extrapolation domain.
Faster matrix completion and extrapolation via kernel regression.
problem Matrix completion and extrapolation with prior information.
method Kernel ridge regression in reproducing kernel Hilbert spaces.
result Novel algorithm performs faster and reduces recovery error.
The study predicts how well classifiers perform with more classes based on initial data.
problem Predicting classifier performance with more classes than initial data.
method Using conditional accuracy distribution and pseudolikelihood, the study extrapolates classifier performance.
result The expected accuracy is the k-1st moment of a conditional accuracy distribution.
Accelerates coordinate descent methods for machine learning problems.
problem Slowness of coordinate descent methods in machine learning.
method Extrapolation-based accelerated coordinate descent.
result Significant speed-up in practice compared to existing methods.
Paper tackles multi-object reinforcement learning, improving skill extrapolation.
problem Learning robust manipulation tasks in multi-object settings.
method Introduces a linear relation network module to enhance skill generalization.
result Agents can extrapolate and generalize to any new object number, scaling linearly.
RTE enables extrapolation to new tasks by learning task transformations.
problem Learning systems struggle to generalize to unseen tasks.
method Relational Task Extrapolator (RTE) learns task transformations to enable extrapolation.
result RTE substantially outperforms existing approaches on extrapolation tasks.
Learnable token perturbations boost extrapolation in LLMs.
problem Limited flexibility of current discrete perturbations in large language models.
method Learnable continuous latent vector transformations in embedding space, unbiased estimating equations, stochastic gradient descent optimization.
result Significant gains in out-of-domain settings over state-of-the-art methods.
We extend nonparametric models to handle extrapolation, providing bounds for inference.
problem Challenges in nonparametric statistical inference when evaluating outside the conditioning variable's support.
method Introduced a class of extrapolation assumptions and a consistent estimation procedure to handle extrapolation.
result Validated extrapolation-aware conclusions through various applications and real-world data.
D-REX learns reward functions from ranked demonstrations to beat the demonstrator's performance.
problem Limited performance of imitation learning compared to the demonstrator.
method Disturbance-based Reward Extrapolation (D-REX) that generates ranked demonstrations from behavioral cloning.
result D-REX significantly outperforms standard imitation learning approaches and beats the demonstrator's performance.
Study improves estimation of rare language model outputs.
problem Estimating probabilities of rare outputs in language models.
method Importance sampling vs. activation extrapolation for low probability estimation.
result Importance sampling outperforms activation extrapolation.
FedExProx's performance is no better than GD for quadratic optimization.
problem Improving convergence of parallel proximal algorithms.
method Developed a novel analysis framework to establish tighter convergence rates.
result FedExProx can outperform GD in non-strongly convex quadratic problems.
Physics models integrated into VAEs improve generative performance and extrapolation.
problem Improving generative models with interpretability and robustness.
method Physics-based latent space in VAEs with regularized learning to balance physics and neural network components.
result Generative performance and extrapolation improvements demonstrated on synthetic and real-world datasets.
Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral dens…
New model predicts neural network performance from early training epochs, incorporating architecture impact.
problem Predicting neural network performance from early training epochs, neglecting architecture impact.
method Architecture-aware graph ordinary differential equation model.
result Model outperforms state-of-the-art methods for MLP and CNN learning curves.
State-space models improve dynamical system predictions efficiently and accurately.
problem Challenges in predicting dynamical systems, including long-time integration and long-range dependencies.
method State-space models implemented in Mamba, addressing limitations of existing architectures.
result Mamba outperforms other models in interpolation and challenging extrapolation tasks.
The paper tackles enforcing constraints in GANs for interpolation and extrapolation.
problem Enforcing constraints in GANs for interpolation and extrapolation.
method The approach involves adding noise to constraints for interpolation and using a projection step for extrapolation.
result The method improves the efficiency and accuracy of GAN training for constrained interpolation and extrapolation.
Binary encoding enables neural networks to extrapolate periodic functions.
problem Extrapolating periodic functions without prior knowledge of their form.
method Normalized Base-2 Encoding (NB2E) for continuous numerical values.
result MLPs using NB2E can successfully extrapolate diverse periodic signals.
Neural networks struggle with extrapolation, but a new framework allows them to learn counterfactual invariances.
problem Neural networks' inability to extrapolate beyond training data distribution.
method Introduces a learning framework that allows neural networks to extrapolate over group transformations based on counterfactual invariances.
result Neural networks can learn counterfactual invariances from a single environment, overcoming their limitations in extrapolation.
A new method uses extrapolation to train deep learning models with larger batch sizes.
problem Persistent degradation in performance when increasing batch size for deep learning.
method Proposes extrapolation to stabilize optimization trajectory and improve generalization.
result Demonstrates scaling to larger batch sizes while maintaining or surpassing state-of-the-art accuracy.
We introduce a new activation function using Chebyshev-Lagrange polynomials for improved neural network performance.
problem Improving data efficiency and accuracy of neural networks.
method Parameterized piece-wise polynomial activation functions based on Chebyshev nodes and Lagrangian interpolation.
result Significant improvements in model capacity and accuracy, especially in linear extrapolation.
NLR models often perform worse than LR for outlying input data in environmental sciences.
problem NLR models often give poor predictions for input data outside the training domain.
method Screened input data for outliers, using linear extrapolation for outliers based on NLR within the non-outlier domain.
result NLROR approach reduces poor extrapolation and tends to outperform NLR and LR for outliers. Proposes engression for extrapolation in distributional regression.
problem Challenging extrapolation problem in nonlinear regression.
method Neural network-based distributional regression.
result Engression successfully performs extrapolation under certain assumptions.
Study shows LLMs can extrapolate rules from out-of-distribution prompts.
problem Understanding LLMs' ability to generalize from unexpected inputs.
method Formal languages and rule-based scenarios to evaluate LLMs' OOD behavior.
result LLMs can extrapolate rules from out-of-distribution prompts, even in complex scenarios.
Paper proposes a new method to improve BART model predictions outside training data range.
problem Improving prediction and prediction intervals for BART models at extrapolation points.
method Gaussian processes are added to BART leaf nodes for extrapolation.
result The new method outperforms standard BART and frequentist resampling methods in simulations.
Celer accelerates Lasso solving with dual extrapolation.
problem Slow optimization of Lasso problems in high-dimensional machine learning.
method Dual extrapolation technique to accelerate Lasso solving.
result Significant computational speedups on real-world problems.
LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
problem Behavioral biases in LLMs' stock return forecasts.
method Comparison of LLM forecasts with crowd-sourced estimates and historical data.
result LLMs overestimate stock returns and are less accurate at predicting extreme outcomes.
New methods reduce extrapolation errors in feature importance.
problem Flawed feature importance methods using unrestricted permutations lead to extrapolation errors.
method Three new approaches: conditional model reliance, Knockoffs with Gaussian transformation, and restricted ALE plot designs.
result Theoretical and numerical results show our strategies reduce/eliminate extrapolation.
Neural networks extrapolate poorly in simple tasks but succeed in complex ones.
problem Understanding neural networks' extrapolation capabilities and conditions for success.
method Analyzing ReLU MLPs and GNNs, connecting to neural tangent kernel.
result ReLU MLPs learn linear functions but not most nonlinear ones, while GNNs succeed in complex tasks due to task-specific non-linearities.
FLUXtrapolation benchmarks machine learning for extrapolating ecosystem fluxes under distribution shifts.
problem Machine learning challenges in extrapolating ecosystem fluxes under distribution shifts.
method Defined temporal, spatial, and temperature-based extrapolation scenarios; evaluated performance across domains, temporal aggregations, and tail errors.
result Baselines perform similarly under median hourly RMSE but differ under tail-focused and multi-scale evaluations.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
problem Optimal portfolio choice under stochastic volatility
method Smooth, nonlinear extrapolation function with sentiment and variance hedging
result Lower welfare loss with asymmetric nonlinear extrapolation
Logical neural networks solve mazes by filling dead ends, but not all methods generalize well.
problem Understanding how logical neural networks extrapolate solutions to mazes.
method Examined recurrent and implicit neural networks trained on maze-solving tasks.
result Models fail to generalize well to diverse maze sizes, suggesting limitations in learning scalable algorithms.
Neural network learns equations from data, improving interpretability and extrapolation.
problem Combining neural networks and symbolic regression for better model interpretability and extrapolation.
method Integrating a neural network-based Equation Learner (EQL) network with other deep learning architectures.
result The EQL-based architecture can extrapolate well outside of the training data set.
Accelerates Riemannian gradient methods with extrapolation.
problem Optimizing functions on manifolds efficiently.
method Extrapolating iterates in Riemannian gradient descent.
result Achieves optimal convergence rate and computational advantage.
Improves reinforcement learning extrapolation in Gridworlds.
problem Generalizing to unseen states in reinforcement learning.
method Avoiding deterministic action choice, using ego-centric representation, incorporating symmetry, and adding an entropy term.
result Significant improvement in extrapolation performance in Gridworld environments.