FlexCodeTS is a flexible time series density estimator.
problem Estimating conditional densities for time series data.
method Nonparametric conditional density estimator based on arbitrary regression methods.
result FlexCodeTS adapts its convergence rate based on the chosen regression method.
Flexible device participation improves federated learning convergence.
problem Strict device participation limits federated learning reach.
method Analytical results and new aggregation scheme for flexible participation.
result Convergence improved with flexible device participation.
Improved algorithm for optimal stopping problems reduces runtime.
problem Optimal stopping problems with infinite time horizon and random discounting.
method Flexible forward improvement iteration with a variable look-ahead distance.
result The new algorithm converges and can significantly reduce runtime.
Teichmüller geodesics can have flexible limit sets.
problem Teichmüller geodesics may not converge to a single point in the boundary.
method Analyzing the topology of the hierarchically hyperbolic space boundary.
result Limit sets of Teichmüller geodesics can be almost anything allowed by the topology.
Adma proposes a flexible loss function for neural networks.
problem Static loss functions limit neural network performance.
method Introduces a flexible loss function that adapts to ANN complexity and data distribution.
result Flexible loss function achieves state-of-the-art performance.
Unified view of score estimators for flexible densities.
problem Estimating the score from unknown distributions.
method Regularized nonparametric regression framework.
result Unified convergence analysis and new estimators with desirable properties.
New factorial power constants improve optimization convergence rates.
problem Optimization convergence rates depend on various constants.
method Proposes using factorial powers for defining these constants.
result Factorial powers simplify or improve convergence rates of optimization methods.
Proposes ELASTICBSP for faster, more flexible distributed deep learning training.
problem Inefficient synchronization in classic BSP for distributed deep learning.
method Introduces ELASTICBSP model with ZIPLINE method for more flexible training.
result ELASTICBSP converges faster and more accurately than classic BSP.
New method avoids IV limitations for flexible estimation.
problem Nonparametric estimation of IV regressions with multiple solutions.
method Minimax penalized estimator avoiding identification and closedness conditions.
result Strong L 2 L_2 L 2 convergence rate without closedness condition. Flexible estimator synthesizes noisy experiments and covariates for optimal effect estimation.
problem Simultaneous analysis of many noisy experiments with rich covariate information.
method Plug-in empirical Bayes estimator that synthesizes noisy experimental results and covariates.
result Within a constant factor of minimax for a simple data-generating model, and robust convergence guarantees hold under generality.
Paper adapts diffusion sampler training for faster convergence and better sampling.
problem Training limitations in diffusion samplers.
method Decouples generation and destruction variances, learns both as unconstrained Gaussians.
result Training both processes leads to faster convergence and improved sampling quality.
Flexible ADMM-based algorithm for non-convex problems with convergence guarantees.
problem Solving non-convex problems with smooth and convex components.
method Incorporates ADMM, SCA, distributed, asynchronous, and inexact gradient methods.
result Establishes first-order convergence rate guarantees under mild assumptions.
Flexible DNN for survival data, avoiding proportional hazards assumption.
problem Survival analysis with complex interactions and non-proportional hazards.
method Partially linear DNN model with a flexible nonparametric component.
result FLEXI-Haz achieves optimal convergence rates and asymptotic efficiency.
This work improves variational inference by reducing gradient variance.
problem Hard optimization of flexible variational distributions.
method Control variate based on quadratic approximation of the model's mean and covariance.
result Significant improvement in gradient variance and optimization convergence.
A-NICE-MC trains flexible MCMC kernels to efficiently sample from complex distributions.
problem Slow convergence and poor performance of general-purpose MCMC methods.
method Adversarial training with flexible volume-preserving flows to optimize MCMC kernels.
result A-NICE-MC significantly outperforms competing methods like Hamiltonian Monte Carlo.
Study shows convergence of certain metrics to flat torus.
problem Stability of metrics on three-torus with negative scalar curvature.
method Defined metrics and used Stern's inequality to show convergence.
result Subsequence of metrics converges to flat metric.
Paper introduces adaptive parameterization to improve neural network efficiency.
problem Neural networks' limited flexibility due to fixed activation functions.
method Adaptive parameterization of feed-forward layers that learn to adapt based on input.
result Adaptive LSTM achieves state-of-the-art performance with fewer parameters and faster convergence.
Introduces BPEL for EL, enhancing flexibility and using MCMC for inference.
problem Computational challenges in EL methods.
method Bayesian Penalized Empirical Likelihood (BPEL) framework with MCMC sampling.
result Enhanced flexibility and practicality of EL methods with MCMC.
This paper analyzes and improves convergence in federated learning with biased client selection.
problem Analyzing convergence in federated learning with biased client selection.
method First convergence analysis of federated optimization for biased client selection strategies, proposing Power-of-Choice framework.
result Power-of-Choice strategies converge up to 3 times faster and give 10% higher test accuracy than random selection.
This paper establishes the consistency of spectral approaches to data clustering. We consider clustering of point clouds obtained as samples of a ground-truth measure. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. We investigate the…
Estimates outcomes under hypothetical scenarios using a flexible framework.
problem Adapting to sudden shifts in treatment patterns.
method Doubly robust estimator using incremental interventions.
result Achieves n \sqrt{n} n -consistency and asymptotic normality. Develops flexible non-parametric ACFs using B-spline kernels.
problem Flexible modelling of the autocovariance function (ACF) in time-series, spatial, and spatio-temporal analysis.
method Derives the inverse Fourier transform of B-spline spectral bases to create a general class of non-parametric ACFs.
result Provides a provably dense, flexible, and general class of non-parametric ACFs for various types of processes.
SC-InfoNCE improves InfoNCE for feature clustering in contrastive learning.
problem Lack of theoretical understanding of InfoNCE's feature clustering mechanism.
method Introduced a transition probability matrix to model data augmentation dynamics and optimize feature similarity.
result SC-InfoNCE achieves strong performance across diverse domains, aligning feature similarity with downstream data.
PMM uses Bayesian inference to generate data from noisy approximations.
problem Creating flexible generative models for various data types.
method Bayesian inference and conjugate pairs of distributions.
result PMM achieves performance competitive with existing generative models.
SimFBO simplifies FBO, making it more efficient and flexible.
problem Complex nested optimization in machine learning and edge computing.
method Proposes SimFBO, a simple and flexible FBO framework with improved communication efficiency.
result SimFBO and ShroFBO achieve linear convergence speedup and improved sample and communication complexities.
Flexible framework for transfer learning with optimal rates.
problem Inference about a target population using related source data.
method Adaptive transfer learning framework allowing covariate-dependent relationships.
result Achieves minimax optimal rates of convergence by adapting to transfer relationship.
Study models weather index insurance pricing by insurers and farmers, finding flexible pricing kernels boost profits.
problem Monopoly pricing of weather index insurance with risk and flexibility considerations.
method Bowley-type sequential game with insurer and farmer, using neural networks for farmer's payoff.
result Flexible pricing kernels increase insurer profits closer to indemnity insurance levels.
We propose a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally constant over a predefined graph structure. Our key insight is to decompose the graph into a set of trails which can then each be solved efficientl…
It is shown that the the popular least squares method of option pricing converges even under very general assumptions. This substantially increases the freedom of creating different implementations of the method, with varying levels of computational complexity and flexible approach to regression. It is also argued that…
Study on SGD for overparameterized neural networks, focusing on convergence rates.
problem Understanding convergence rates of SGD in overparameterized two-layer neural networks.
method Combines NTK approximation with RKHS analysis to explore SGD dynamics.
result Established sharp convergence rates for SGD in overparameterized two-layer neural networks.
New method for private learning with public features improves convergence rates.
problem Private estimation with public features under local differential privacy.
method Semi-feature LDP, HistOfTree estimator.
result HistOfTree reaches mini-max optimal convergence rate.
Model financial default cascades on sparse graphs via hitting times.
problem Capturing systemic risk in large, sparsely-connected financial networks.
method Dynamic particle systems with hitting times and convergence theory.
result Characterization of default time distribution in tree-like networks.
Inference is an integral part of probabilistic topic models, but is often non-trivial to derive an efficient algorithm for a specific model. It is even much more challenging when we want to find a fast inference algorithm which always yields sparse latent representations of documents. In this article, we introduce a si…
The paper develops a bootstrap method to measure algorithmic convergence in regression.
problem Ensuring a randomized ensemble performs nearly as well as an ideal infinite ensemble.
method Bootstrap method for regression setting, complementing classification setting.
result Theoretical guarantees for the bootstrap method can be established under weaker assumptions.
Study proves fluid limits of fragmented limit-order markets.
problem Modeling fragmented limit-order markets with small and frequent orders.
method Proved convergence of discrete system to fluid limit characterized by coupled nonlinear ODEs.
result Fluid system converges to stationary equilibrium state over time.
New analysis proves consistency for adaptive Bayesian quadrature methods.
problem No theoretical guarantees for adaptive Bayesian quadrature methods.
method Introduces weak adaptivity and proves consistency for a broad class of adaptive Bayesian quadrature rules.
result Proves consistency and derives non-tight but informative convergence rates for adaptive Bayesian quadrature methods.
This paper extends policy gradient methods to partially observable environments.
problem Learning optimal policies in partially observable environments.
method Developed new tools including advantage function to generalize policy gradient algorithms and study their convergence in partially observable Markovian policies.
result Generalized theoretical guarantees of policy gradient algorithms to partially observable domains.
Samplets and multiwavelets constructed from scattered data converge to specific densities in the limit.
problem Constructing data-adapted multiresolution analyses and multiwavelets with flexible vanishing moments.
method Probabilistic framework for samplet construction; convergence to multiwavelets with broken polynomial densities.
result Samplet construction converges to multiwavelets in the infinite data limit.
The paper analyzes how larger minibatch sizes in SG-MCMC lead to faster convergence.
problem Theoretical analysis of impact of minibatch size on SG-MCMC convergence rate.
method Proposes a variance-reduction technique for SG-MCMC and proves its faster convergence rate.
result The proposed variance-reduction technique leads to a faster convergence rate than standard SG-MCMC.
Empirical Gaussian Processes learn flexible priors from data.
problem Limited effectiveness of standard Gaussian process kernels.
method Estimate mean and covariance functions empirically from data.
result Empirical GPs converge to closest GP to real data generating process.
FLORAS uses orthogonal sequences for SISO FL, offering both DP and convergence guarantees.
problem Privacy-preserving wireless federated learning in SISO systems.
method Leverages orthogonal sequences to eliminate CSIT requirement and provide DP guarantees.
result FLORAS achieves a smooth tradeoff between convergence rate and DP levels.
This work improves federated learning privacy and accuracy with non-private data sharing and approximate gradient coding.
problem Challenges of non-IID data and stragglers in federated learning.
method Data-driven strategy combining offline data sharing and approximate gradient coding.
result Achieves a trade-off between privacy and utility, leading to improved model convergence and accuracy.
New convergence results for NGVI with various step sizes and sample sizes.
problem Understanding convergence of stochastic NGVI for various schedules.
method Projected stochastic NGVI for exponential family variational distributions.
result Geometric convergence and $\mathcal{O}\left(\frac{1}{T^ρ}
ight)$ rates for different schedules.
New stochastic algorithm improves on existing PRSM methods.
problem Improving performance of stochastic PRSM algorithms.
method Proposes SS-PRSM, a new stochastic algorithm based on PRSM.
result Demonstrates O ( 1 / K ) O(1/K) O ( 1/ K ) convergence rate, faster than ADMM. Efficient poisoning attack converges to any target classifier with provable convergence.
problem Inducing a corrupted model that misbehaves in favor of an adversary.
method Online convex optimization to find poisoning points incrementally.
result Provably converges to any attainable target classifier.
Flexible framework compresses models using LC algorithm.
problem Efficiently compressing neural networks for resource constraints.
method Decouples learning and compression steps with alternating L and C phases.
result Compressed models maintain performance and accuracy.
Simplified SGD interpretation as Ito process for broader applicability.
problem Lack of generality in current SGD interpretation.
method Introduced a simplified scheme for discrete-time approximation of Ito process.
result Flexibly interprets SGD and SGLD, providing insights into their asymptotic properties.
New framework quantifies uncertainty in flexible density-based clustering.
problem Uncertainty quantification in clustering with non-parametric density estimation.
method Martingale posterior distributions and density-based clustering.
result Efficient GPU-compatible inference on clustering structures with uncertainty.