The development of algorithms for unsupervised pattern recognition by nonlinear clustering is a notable problem in data science. Markov clustering (MCL) is a renowned algorithm that simulates stochastic flows on a network of sample similarities to detect the structural organization of clusters in the data, but it has n…
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48 results for “Nonlinear Baselines”
This work establishes safe reinforcement learning for LQR with nonlinear baselines.
problem Safe reinforcement learning in LQR with unknown dynamics and safety constraints.
method General framework for nonlinear baselines, focusing on 1D spaces.
result Achieves optimal regret bounds for constrained reinforcement learning.
Independent Component Analysis (ICA) aims to find a coordinate system in which the components of the data are independent. In this paper we construct a new nonlinear ICA model, called WICA, which obtains better and more stable results than other algorithms. A crucial tool is given by a new efficient method of verifying…
Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental …
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
problem Weak predictive accuracy and limited interpretability of psychiatric questionnaires.
method Two-stage method: stable preprocessing followed by a linear mapping.
result REFINE outperforms other interpretable approaches in psychiatric and non-psychiatric prediction tasks.
RFMs transition from linear to nonlinear under specific input-label correlation.
problem Understanding the transition from linear to nonlinear behavior in RFMs.
method Analyzing RFMs under spiked covariance designs, characterizing the interaction between anisotropy and input-label correlation.
result The RFM generalization error is governed by the strength of input-label correlation, leading to a clear nonlinear advantage above a specific boundary.
Proposes KAR for nonlinear causal discovery using kernel methods.
problem Learning causal relationships in nonlinear settings.
method Kernel anchor regression (KAR) with improved three-stage nonparametric regression.
result KAR outperforms existing methods in nonlinear causal discovery.
This paper analyzes a simplified strategy for nonlinear control using local linear models and iLQR updates.
problem Nonlinear policy optimization in control systems.
method Iterative estimation of local linear models and iLQR-like policy updates.
result Demonstrates polynomial sample complexity and overcomes exponential problem horizon dependence.
Robust method learns nonlinear structures robustly to noise.
problem Learning nonlinear structures in noisy data.
method Robust Non-Linear Matrix Factorization (RNLMF).
result RNLMF achieves noticeable improvements in denoising and clustering.
JULIA combines multi-linear and nonlinear models for tensor completion.
problem Complex patterns in real-world tensors require a unified model.
method JULIA unifies multi-linear and nonlinear models with flexible component assignment and efficient alternating optimization.
result JULIA outperforms existing methods in large-scale tensor completion.
Unified framework for inference in complex nonlinear processes.
problem Challenges in inferring nonlinear continuous stochastic processes with sparse observations and complex topologies.
method Neural Backward Filtering Forward Guiding (NBFFG) framework that constructs a variational posterior using a proxy linear-Gaussian process.
result Empirical results show NBFFG outperforms baselines on synthetic benchmarks and high-dimensional phylogenetic analysis tasks.
New method linearizes nonlinear coupled oscillators on graphs.
problem Predicting global synchronization in nonlinear coupled oscillators on graphs.
method Latent dynamic filters learned through supervised matrix factorization.
result Latent dynamics filters enable effective prediction of global synchronization.
This paper extends performative prediction to nonlinear cases.
problem Performative prediction's effectiveness is limited by linear assumptions in real-world applications.
method Formulated a maximum margin approach loss function and extended it to nonlinear spaces using kernel methods.
result Derived conditions for performative stability in both linear and nonlinear cases.
How Data Mixing Shapes In-Context Learning: Asymptotic Equivalence for Transformers with MLPsstat.ML
Transformers with MLP heads outperform linear baselines in in-context learning.
problem Understanding in-context learning in Transformers with nonlinear MLP heads.
method Analyzing a model with two-layer MLPs trained via gradient steps and fully optimized, under high-dimensional asymptotics.
result Nonlinear MLPs enhance ICL performance, particularly on nonlinear tasks.
New method learns low-dimensional representations of nonlinear time series without supervision.
problem Learning low-dimensional representations of nonlinear time series without supervision.
method Based on monotone variational inequality, the method learns representations by assuming sequences arise from a common domain.
result The method can learn the geometry for the entire domain and faithful representations for the dynamics of each individual sequence.
DiffSlack learns neural networks with nonlinear constraints via learnable slack variables.
problem Enforcing nonlinear inequality constraints in neural networks.
method DiffSlack reformulates inequalities as equalities with learnable slack variables, predicting them as part of the network output.
result DiffSlack achieves higher planning success rates and stronger geometric constraint satisfaction compared to existing methods.
Federated learning interprets temporal dynamics across clients with graph attention.
problem Interpreting temporal patterns across decentralized, heterogeneous systems with nonlinear dynamics.
method Graph Attention Network for learning state transition models over latent states communicated between clients.
result First interpretable characterization of cross-client temporal interdependencies in decentralized nonlinear systems.
KRCD detects unobserved confounders in nonlinear observational data.
problem Detecting unobserved confounders in nonlinear observational studies.
method Kernel Regression Confounder Detection (KRCD) using reproducing kernel Hilbert spaces.
result KRCD outperforms existing methods and achieves superior computational efficiency.
Estimates effects of multiple interventions with hidden confounders using single-variable interventions.
problem Estimating effects of multiple interventions in the presence of hidden confounders.
method Identifiability under nonlinear structural causal model with additive Gaussian noise; pooling and joint likelihood maximization.
result Proven identifiability and superior performance compared to baseline.
Develops a flexible model for regime transitions in time series data.
problem Nonlinear and context-dependent regime transitions in time series data.
method Semi-parametric state-space model with learned transition functions.
result Improved recovery of nonlinear transition dynamics and earlier detection of regime changes.
Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work. The main challenge lies in that the combination of the ambient noise with dynamic baseline fluctuation, often contaminates the observations, thereby deteriorating the reliability o…
Proposes IIB for domain generalization, overcoming failure modes of IRM.
problem Domain generalization with nonlinear classifiers and pseudo-invariant features.
method Invariant Information Bottleneck (IIB) using mutual information and variational formulation.
result Significantly outperforms IRM on synthetic datasets and real-world benchmarks.
Spectral deconfounding improves machine learning models by reducing hidden confounding effects.
problem Machine learning models can be misled by hidden confounders, leading to unreliable predictions.
method Develops a nonlinear spectral deconfounding framework for gradient boosting that modifies boosting dynamics to slow down in confounding-aligned directions.
result Spectrally deconfounded boosting improves estimation of the target function under hidden confounding and is more scalable.
A new method detects change points in time series with conceptors.
problem Detecting change points in time series with nonlinear temporal dependence.
method Use of conceptor matrix to learn baseline dynamics and identify change points.
result The method provides a consistent estimate of the true change point.
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour. Existing work cannot handle the tasks well since they model such dynamics either directly on observations or enforce the availabi…
The Score Kalman Filtereess.SY
A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.
problem Nonlinear Bayesian filtering challenges in representing belief distributions.
method Combines score matching with Stein's identity to avoid partition function evaluation.
result The Score Kalman Filter (SKF) outperforms existing methods on nonlinear systems.
Proposes iCaRL for nonlinear OOD generalization in causal settings.
problem Machine learning systems fail to generalize to new environments.
method iCaRL, leveraging exponential family distributions and causal discovery.
result Generalization guarantees in nonlinear settings for both representations and classifiers.
New learned optimizers outperform baselines by incorporating known and novel mechanisms.
problem Understanding how learned optimizers outperform traditional ones.
method Careful analysis and visualization of learned optimizers trained on various tasks.
result Learned optimizers incorporate known techniques like momentum and gradient clipping, as well as new forms of learning rate adaptation.
Adapts manifold structure for better clustering performance.
problem Lack of consideration for local manifold structure in existing multiple kernel k-means methods.
method Adopts manifold adaptive kernel to integrate local manifold structure of kernels.
result Proposed method outperforms state-of-the-art methods.
Paper proposes ConvSCN for robust subspace clustering and classification.
problem Insufficient handling of nonlinear manifolds, data corruptions, and out-of-sample data.
method Self-supervised convolutional subspace clustering network (ConvSCN) with FC layer, CIM for robustness, and BD regularization.
result Robust ConvSCN outperforms baseline on unseen data.
MASF improves score-based filters for high-dimensional nonlinear systems with spatially sparse measurements.
problem Challenges in data assimilation for nonlinear, high-dimensional systems with spatially sparse measurements.
method Developed a forward process tailored for filtering that transforms the system state toward the measurement space, enabling a theoretically sound formulation of the likelihood score.
result MASF shows improved performance over existing score-based filters and ensemble-type Kalman filters, achieving up to a 28.2× wall-clock speedup.
RoME optimizes mobile health interventions by modeling user and time-specific effects.
problem Challenges in optimizing mobile health interventions due to participant heterogeneity, nonstationarity, and nonlinear relationships.
method RoME uses a Robust Mixed-Effects contextual bandit algorithm with random effects, network cohesion penalties, and debiased machine learning.
result RoME achieves robust regret bounds even with complex baseline rewards, demonstrating superior performance in simulations and studies.
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis. The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods. Recently, researchers have pro…
Demixing Sparse Signals from Nonlinear Observations using Generalized Non-convex Regularizationstat.ML
New method recovers sparse signals from nonlinear observations with robust error bounds.
problem Recovering two sparse vectors from nonlinearly mixed observations with limited data.
method Regularization-based framework combining Huberized data fidelity and generalized folded-concave penalties with a proximal alternating algorithm.
result Estimation error bounds of order at every localized stationary point, with oracle rate under beta-min condition.
Reservoir subspace injection improves online ICA by preserving injected features.
problem Discarding injected features in top- whitening can degrade performance.
method Formalized reservoir subspace injection (RSI) and developed diagnostics (IER, SSO, ρ_x) to identify and mitigate the failure mode.
result RSI controller preserves passthrough retention, improving performance by up to 2.2 dB.
Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets. DPPs are parameterized by a positive semi-definite kernel matrix. While DPPs have substantial expressive power, they are fundamentally limited by t…
Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heurist…
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting of the log-proportional hazard as a function of the covariates and are convenient as they do not require estimation of the baseline hazard …
Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsible for most rhythmic motion in living organisms. This rhythmic control is broadly applicable to fields such as locomotive robotics and medic…
Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. T…
New method combines long-memory reservoirs for accurate dengue forecasting from short data.
problem Accurate dengue forecasting from short, noisy, non-stationary, and nonlinear data.
method Fractional ESN and Wavelet ESN frameworks integrating long-term memory.
result fESN and wESN outperform baselines in multiple dengue datasets and forecasting horizons.
Token-adaptive FFN design improves LLM expressivity.
problem Fixed activation functions limit FFN expressivity in LLMs.
method Mixture of Activations (MoA) and learnable activations (LA).
result MoA achieves lower terminal loss and better scaling than baselines.
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Predictioncs.AI
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according…
Prediction of cancer dynamics under treatment using Bayesian neural networks: A simulated studyq-bio.QM
Bayesian neural networks improve cancer dynamics prediction.
problem Predicting cancer dynamics under treatment due to heterogeneity and sparse data.
method Hierarchical Bayesian model using baseline covariates and Bayesian neural networks for nonlinear interactions.
result Bayesian neural networks outperform linear models in predicting cancer dynamics with interactions.
LEAP identifies latent causal variables from temporal data.
problem Recovering time-delayed latent causal variables from general temporal data.
method Proposes LEAP, a framework that extends VAEs with constraints for temporally causal latent processes.
result Successfully identifies temporally causal latent processes from observed variables under various dependency structures.
VSCOUT detects anomalies in high-dimensional data using a hybrid VAE approach.
problem Challenges in classical SPC for high-dimensional, non-Gaussian data.
method Hybrid VAE architecture with ARD prior, ensemble filtering, and changepoint detection.
result VSCOUT achieves superior sensitivity to special-cause structure and controlled false alarms.
How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embe…
DecompKAN improves time series forecasting accuracy and transparency.
problem Accurate and transparent time series forecasting in scientific domains.
method Combines decomposition, patching, normalization, and B-spline KAN edge functions.
result Achieves best or tied-best MSE on 20 of 36 comparisons across 9 datasets.