A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper explores effective data selection methods for weakly supervised learning.
We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distributio…
Subsampling reduces computational cost in supervised learning in reproducing kernel Hilbert spaces.
A new method speeds up sampling of Boltzmann distribution in high-dimensional systems.
Improves survey sampling with unbiased machine learning methods.
A new method reduces variance in SGMCMC by preferentially subsampling data.
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators,…
New insights into how randomization affects greedy model selection.
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for observations is estimated from a random subset of observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smal…
A new method combines variational inference and MCMC for efficient data subsampling.
For optimization on large-scale data, exactly calculating its solution may be computationally difficulty because of the large size of the data. In this paper we consider subsampled optimization for fast approximating the exact solution. In this approach, one gets a surrogate dataset by sampling from the full data, and …
New method for evaluating sequential recommendations with lower variance.
Algorithm corrects bias in classification data.
URGE improves diffusion model quality without gradients or Hessian.
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these p…
New method improves inference for hierarchical models.
Unified theory and debiasing framework for random oblique projections in high dimensions.
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance updates to the weights for the value function. In this work, we explore a resampling strategy as an alternative to reweighting. We propose Im…
New loss function restores importance weighting in overparameterized models.
A scalable algorithm for GP regression selects relevant covariates efficiently.
Iterative reweighted algorithms, as a class of algorithms for sparse signal recovery, have been found to have better performance than their non-reweighted counterparts. However, for solving the problem of multiple measurement vectors (MMVs), all the existing reweighted algorithms do not account for temporal correlation…
Gibbs sampling is the de facto Markov chain Monte Carlo method used for inference and learning on large scale graphical models. For complicated factor graphs with lots of factors, the performance of Gibbs sampling can be limited by the computational cost of executing a single update step of the Markov chain. This cost …
A new method selects a representative subsample for efficient kernel density estimation.
In this paper we demonstrate that tempering Markov chain Monte Carlo samplers for Bayesian models by recursively subsampling observations without replacement can improve the performance of baseline samplers in terms of effective sample size per computation. We present two tempering by subsampling algorithms, subsampled…
This paper optimizes subsampling for large datasets using Poisson distribution.
Large sample size brings the computation bottleneck for modern data analysis. Subsampling is one of efficient strategies to handle this problem. In previous studies, researchers make more fo- cus on subsampling with replacement (SSR) than on subsampling without replacement (SSWR). In this paper we investigate a kind of…
This paper develops a novel deep recurrent neural network for sequential signal reconstruction.
A significant hurdle for analyzing large sample data is the lack of effective statistical computing and inference methods. An emerging powerful approach for analyzing large sample data is subsampling, by which one takes a random subsample from the original full sample and uses it as a surrogate for subsequent computati…
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Specifically, we provide a tight upper bound on the Rényi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms that: (1) subsamp…
A new model-free subsampling method using uniform designs is proposed.
Improved matching for multiple objects using a novel reweighting method.
Reweighted l1-algorithms have attracted a lot of attention in the field of applied mathematics. A unified framework of such algorithms has been recently proposed by Zhao and Li. In this paper we construct a few new examples of reweighted l1-methods. These functions are certain concave approximations of the l0-norm func…
Proposes a new method to improve regression models with reweighted samples.
Improved learning to reweight using deep interactions between student and teacher models.
Deep latent variable models have become a popular model choice due to the scalable learning algorithms introduced by (Kingma & Welling, 2013; Rezende et al., 2014). These approaches maximize a variational lower bound on the intractable log likelihood of the observed data. Burda et al. (2015) introduced a multi-sample v…
For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least squares estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this p…
Defense against user shilling attacks in collaborative filtering using edge reweighting.
Unified framework for subsampling mechanisms with tighter privacy guarantees.
We analyze a reweighted version of the Kikuchi approximation for estimating the log partition function of a product distribution defined over a region graph. We establish sufficient conditions for the concavity of our reweighted objective function in terms of weight assignments in the Kikuchi expansion, and show that a…
CurveRL optimizes large model reasoning by reweighting prompts based on their rank and density.
The Sampled Gaussian Mechanism's noise level decreases with larger subsampling rates, improving privacy-utility trade-offs.
Stein importance sampling is a widely applicable technique based on kernelized Stein discrepancy, which corrects the output of approximate sampling algorithms by reweighting the empirical distribution of the samples. A general analysis of this technique is conducted for the previously unconsidered setting where samples…
Group-equivariant subsampling layers improve CNNs' equivariance.
Develops a method to optimize hyperparameters for subsampling methods.
New equivalences found between subsampling and ridge regularization methods.
New insights into privacy guarantees for subsampled mechanisms under composition.
Unified analysis of reweighted least-squares algorithms for linear models.
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, traditional Gaussian process-based Bayesian optimization strategies which assume vector inputs can fall short. To address this, we develop a B…