AER dynamically adjusts entropy regularization for better LLM reinforcement learning.
problem Policy entropy collapse in RLVR training limits exploration and reasoning performance.
method Adaptive Entropy Regularization (AER) with difficulty-aware coefficient allocation, initial-anchored target entropy, and dynamic global coefficient adjustment.
result AER consistently outperforms baselines on mathematical reasoning benchmarks, improving both accuracy and exploration.
Based on the daily data of American and Chinese stock markets, the dynamic behavior of a financial network with static and dynamic thresholds is investigated. Compared with the static threshold, the dynamic threshold suppresses the large fluctuation induced by the cross-correlation of individual stock prices, and leads…
Adaptive sampling for multimodal distributions converges faster than classical methods.
problem Sampling from multimodal distributions efficiently.
method Adaptive linear dynamics with adaptive diffusion coefficients and vector fields, interpreted as weighted Wasserstein gradient flows.
result Derivative-free dynamics can achieve significantly faster convergence for nonconvex potentials.
This paper improves neural network generalization by dynamically learning kernel parameters.
problem Improving neural network generalization and adaptability.
method Diagonal adaptive kernel model that learns kernel eigenvalues and output coefficients during training.
result The diagonal adaptive kernel model significantly improves generalization over fixed-kernel methods.
Novel method estimates complex nonlinear systems with stochastic differential equations.
problem Handling complex nonlinear dynamical systems with strong learning guarantees.
method Estimates drift and diffusion coefficients of continuous, multidimensional, nonlinear controlled stochastic differential equations.
result Strong theoretical guarantees including finite-sample bounds for various metrics.
We present a theoretical and empirical study of the gradient dynamics of overparameterized shallow ReLU networks with one-dimensional input, solving least-squares interpolation. We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network fun…
Diffusion models adapt to low-dimensional data regardless of coefficient choices.
problem Understanding how diffusion models adapt to low-dimensional data structures.
method Analysis of diffusion models with flexible coefficient choices.
result Proven that O ~ ( k / ε ) \widetilde{O}(k/\varepsilon) O ( k / ε ) iterations suffice for accurate sampling in total variation distance. Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstruction. This paper proposes a framework for online (or time-sequential)…
Adaptive market-making strategy improves profit by adjusting to order flow.
problem Optimizing market-making profits in a dynamic market environment.
method Closed-form solutions for optimal bid-ask spreads, modeling demand randomness, and adapting to market order behavior.
result Adaptive strategies outperform fixed and non-adaptive strategies.
New algorithm reduces dynamic regret in time-varying movement costs.
problem Dynamic regret in online convex optimization with time-varying movement costs.
method Introduced a novel algorithm for time-varying movement costs, achieving comparator-adaptive dynamic regret bound.
result Established first comparator-adaptive dynamic regret bound of O ~ ( ( M 2 + M P T ) ( T + ∑ t λ t ) ) \widetilde{\mathcal{O}}(\sqrt{(M^2+MP_T)(T+\sum_t λ_t)}) O ( ( M 2 + M P T ) ( T + ∑ t λ t ) ) . Proves existence and trapped surface formation for Einstein-Vlasov system without symmetry assumptions.
problem Formation of trapped surfaces in Einstein-Vlasov system without symmetry.
method Calibrated hierarchy of estimates, refined renormalization, strategic restriction of elliptic estimates, precise commutator calculus.
result First large-data, symmetry-free construction of dynamical black hole formation.
We consider the optimal control problem for a linear conditional McKean-Vlasov equation with quadratic cost functional. The coefficients of the system and the weigh-ting matrices in the cost functional are allowed to be adapted processes with respect to the common noise filtration. Semi closed-loop strategies are intro…
Adaptive LASSO improves model selection for functional geostatistical data.
problem Modeling georeferenced data with spatiotemporal dynamics and functional coefficients.
method Penalized maximum likelihood estimator with adaptive LASSO penalty for simultaneous selection of spline basis functions and regressors.
result The penalized estimator outperforms the unpenalized estimator in all scenarios tested.
Neural GARCH models financial time series with time-varying coefficients.
problem Modeling conditional heteroskedasticity in financial time series.
method Neural network adaptation of GARCH and BEKK models with time-varying coefficients parameterized by a recurrent neural network.
result Neural Students t model consistently outperforms other models on financial time series.
New insights show NAG and FISTA converge linearly without knowing strong convexity modulus.
problem Understanding linear convergence of NAG and FISTA without strong convexity modulus knowledge.
method High-resolution ODE framework, dynamically adapting kinetic energy coefficient.
result NAG and FISTA demonstrate linear convergence without requiring strong convexity modulus knowledge.
A new method evolves point clouds using B-splines for smooth surfaces.
problem Evolution of smooth surfaces from discrete point clouds.
method Adaptive Lagrangian B-spline framework for geometric evolution.
result Efficient and accurate reproduction of surface evolution phenomena.
This paper introduces a novel online inference method for high-dimensional GLMs.
problem Real-time analysis of sequentially collected data in high-dimensional settings.
method Adaptive stochastic gradient descent with online debiasing for dynamic objective functions.
result Established the asymptotic normality of the Adaptive Debiased Lasso (ADL) estimator.
Bayesian optimization with RPCE reduces MAP estimation for structural dynamics models.
problem Estimating parameters of structural dynamic models efficiently.
method Bayesian optimization with RPCE surrogate model.
result Effective reduction in model evaluations for MAP estimation.
Paper tests if beta coefficients in AMF model are consistent over time.
problem Testing time-invariance of beta coefficients in AMF model.
method Used AMF model with GIBS algorithm to identify relevant factors, compared to FF5 model.
result AMF model shows time-invariant beta coefficients for most periods, FF5 does not.
Paper adapts DDPM to low-dimensional structures in image distributions.
problem Understanding and adapting to low-dimensional structures in image distributions.
method Developed a novel set of analysis tools to characterize algorithmic dynamics.
result First theoretical demonstration that DDPM can adapt to unknown low-dimensional structures.
AJL framework detects dynamic patterns in high-dimensional time-varying models.
problem Complex time-varying associations and abrupt regime shifts in longitudinal processes.
method Hierarchical regularization framework integrating functional variable selection with structural changepoint detection.
result The refined estimator achieves the oracle property in ultra-high-dimensional settings.
A novel Bayesian method for dynamic sparsity in Gaussian dynamic linear regression.
problem Variable selection and shrinkage in time-varying regression models.
method Time-varying sparsity via Markov switching priors for coefficients' variances, extending spike-and-slab priors.
result Induces smoothness or shrinkage towards zero at each time point, leading to improved model performance.
Adaptive transfer learning model for varying mechanisms across domains.
problem Improving inference in a target domain by leveraging related source domains with varying mechanisms.
method Semi-parametric domain-varying coefficient model (DVCM) for structured transfer learning.
result Minimax rate-optimal adaptive transfer learning estimator with provable negative transfer safeguards.
Proposes a Varying-Coefficient MoE model for analyzing dynamic data.
problem Inadequate constant coefficients in MoE models for dynamic settings.
method Varying-Coefficient Mixture of Experts (VCMoE) model with varying coefficients in gating and expert models.
result Established identifiability and consistency of the VCMoE model.
Adaptive smooth non-stationary bandits achieve optimal regret rates without knowing parameters.
problem Smooth non-stationary bandits with Hölder class rewards.
method Established optimal dynamic regret rate and adaptive algorithm.
result Optimal dynamic regret can be attained adaptively without knowing Hölder exponent and coefficient.
We study the dynamic interactions and structural changes in global financial indices in the years 1998-2012. We apply a principal component analysis (PCA) to cross-correlation coefficients of the stock indices. We calculate the correlations between principal components (PCs) and each asset, known as PC coefficients. A …
Modeling implied volatility surface dynamics with Hawkes kernels.
problem Understanding and predicting high-frequency dynamics of the implied volatility surface.
method Hawkes modeling of the volatility surface, with coefficients governing skew and convexity.
result Simple conditions on Hawkes kernel coefficients ensure no-arbitrage and reduce parameter estimation.
Framework uses deep learning and statistical models to solve PDEs with discontinuous coefficients.
problem Solving PDEs with discontinuous coefficients.
method Two-stage physics-informed deep learning and statistical mixture models.
result Framework achieves adaptability and accurate parameter identification.
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
problem Understanding the structure and dynamics of crude oil futures markets during global challenges.
method Visibility graph analysis of daily and high-frequency data.
result Crude oil futures markets exhibit small-world properties and assortative mixing, with unique sensitivities to global disruptions.
PDMP samplers improve Bayesian PDE coefficient inference.
problem Efficient Bayesian inference in non-linear inverse problems with expensive likelihoods.
method Piecewise deterministic Markov process (PDMP) with surrogate-assisted thinning.
result PDMP samplers achieve higher accuracy and efficiency than traditional methods.
Investigates the use of Information Coefficient as a stock selection model performance measure.
problem The adequacy and effectiveness of Information Coefficient (IC) for evaluating stock selection models is unclear.
method Simulation and simple statistical modeling to examine IC behavior statically and dynamically.
result Proposes two practical procedures for IC-based ongoing performance monitoring of stock selection models.
We describe the polynomial time complexity algorithm for computing first coefficients of the skein (Homflypt) and Kauffman polynomial invariants of links, discovered by D.Vertigan in 1992 but never published.
This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares …
Algorithm learns interference network and optimizes treatment allocation for unknown network effects.
problem Adaptive experimentation under unknown network interference.
method Thompson sampling algorithm with Gibbs sampler for joint learning of interference network and treatment allocation.
result Proves a Bayesian regret bound and achieves sublinear regret in real-world applications.
Recent breakthrough results in compressed sensing (CS) have established that many high dimensional objects can be accurately recovered from a relatively small number of non- adaptive linear projection observations, provided that the objects possess a sparse representation in some basis. Subsequent efforts have shown th…
Adapts model-based advice to stabilize black-box policies for nonlinear control.
problem Stabilizing machine-learned policies for nonlinear control with limited model information.
method Proposes an adaptive λ λ λ -confident policy to combine black-box and model-based advice. result Proves the stability of the adaptive λ λ λ -confident policy and its competitive ratio. Improves continual learning with theoretical guarantees and a new algorithm.
problem Learning incremental tasks with dynamic data distributions.
method Contrastive and distillation losses with theoretical performance guarantees.
result Theoretical performance bounds and improved continual learning performance.
Proposes a new ridge estimator for smooth covariates with adaptive centering.
problem Estimating coefficients and center function for smooth covariates in linear models.
method SACR framework with convex formulation, roughness penalty, and adaptive centering.
result Improves prediction and variable selection for smooth covariates.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
problem Dynamic pricing with unknown Hölder smoothness of demand function.
method Self-similarity condition and adaptive algorithm.
result Adaptive algorithm achieves minimax optimal regret without prior knowledge of smoothness.
FNSDA adapts to new dynamics via Fourier space adaptation.
problem Generalizing to unseen dynamical systems with limited data.
method Automatic partitioning of known environments in Fourier modes and adaptation of specific modes for new environments.
result FNSDA achieves superior or competitive generalization performance with reduced parameter cost.
A new method reduces bias in adaptive Lasso estimates.
problem Bias in adaptive Lasso estimates.
method Proximal gradient approach to learn penalty coefficients as decision variables.
result Reduces bias in estimates and encourages arbitrary sparsity structure.
Paper proposes ABDR for convex subspace clustering with adaptive block diagonal representation.
problem Subspace clustering with block diagonal structure for noisy data.
method ABDR explicitly pursues block diagonality without sacrificing convexity, using a specially designed convex regularizer.
result Experimental results show ABDR outperforms state-of-the-arts.
Paper develops a hybrid DNN approach for RUL prediction with adaptive drift.
problem RUL estimation challenges in practice, especially online update and uncertainty quantification.
method Hybrid DNN approach with Wiener-based-degradation model and adaptive drift. LSTM-CNN for trajectory prediction and Bayesian inference for adaptive drift.
result Superior accuracy in RUL prediction demonstrated on turbofan engines data.
New algorithm reduces control error in systems with changing dynamics.
problem Online control of systems with time-varying linear dynamics.
method Introduces adaptive regret metric and a novel meta-algorithm.
result First adaptive regret bound for online convex optimization with memory.
Predictive Sparse Manifold Transform learns dynamic video sequences.
problem Learning and predicting natural dynamics in video sequences.
method Two-layer framework: sparse coding and manifold learning.
result PSMT with dynamic embedding space outperforms static baselines in future frame prediction.
Study forecasts cholera outbreaks in Malawi using dynamic models.
problem Cholera transmission forecasting in developing countries.
method Qualitative dynamics, Monte Carlo Markov Chain, sensitivity analysis, machine learning.
result Enhanced cholera forecasting models improve future trends prediction.
We derive a class of macroscopic differential equations that describe collective adaptation, starting from a discrete-time stochastic microscopic model. The behavior of each agent is a dynamic balance between adaptation that locally achieves the best action and memory loss that leads to randomized behavior. We show tha…
Study shows polystability of tangent and canonical sheaves on Kähler-Einstein log Fano pairs.
problem Stability of tangent and canonical sheaves on Kähler-Einstein log Fano pairs.
method Analysis of adapted tangent and canonical sheaves under singular Kähler-Einstein metrics.
result Adapted tangent and canonical sheaves are polystable.