arXiv research

A scheme robust to action erasures improves MAB performance.

New lower bounds show sparse recovery is hard even with multiple preconditioners.

New study shows feedback doesn't improve MAB performance over erasure channels.

Paper proposes SSSE, an efficient method to erase samples from trained models.

TaCo prevents non-linear classifiers from detecting sensitive attributes.

This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both (a) erasures: most entries are not observed, and (b) errors: values at a constant fraction of (unknown) locations are arbitrarily corrupted. We provide a new unified performance guarantee on when the natura…

We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity clustering algorithm based on thresholding the correlations between the data po…

Differentiable Masking reveals how neural models make decisions across layers.

A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an $N$ dimensional signal with a $K$-sparse WHT, where $N$ is a power of two and $K = O(N^α)$, scales sub-linearly in $N$ for some $0 < α< 1$. Assuming a random support model for the non-zero transform domain…

Residual Networks with convolutional layers are widely used in the field of machine learning. Since they effectively extract features from input data by stacking multiple layers, they can achieve high accuracy in many applications. However, the stacking of many layers raises their computation costs. To address this pro…

Formula derived for sample complexity in binary hypothesis testing.

We prove several results about chordal graphs and weighted chordal graphs by focusing on exposed edges. These are edges that are properly contained in a single maximal complete subgraph. This leads to a characterization of chordal graphs via deletions of a sequence of exposed edges from a complete graph. Most interesti…

Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation. We propose a distributed optimization framework where the dataset is "encoded" to have an over-complete representation with built-in redundancy, and the …

Introduces MPR to measure and optimize representation across intersectional groups in retrieval.

This paper considers the problem of implementing large-scale gradient descent algorithms in a distributed computing setting in the presence of {\em straggling} processors. To mitigate the effect of the stragglers, it has been previously proposed to encode the data with an erasure-correcting code and decode at the maste…

CSD learns a common component for domain generalization, outperforming existing methods.

In this paper, we model Density Evolution (DE) using Recurrent Neural Networks (RNNs) with the aim of designing capacity-approaching Irregular Low-Density Parity-Check (LDPC) codes for binary erasure channels. In particular, we present a method for determining the coefficients of the degree distributions, characterizin…

No universal trading strategy exists due to mathematical impossibilities.

We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm providing an efficient method for clustering data belonging to a few low-dimensional linear or affine subspaces. The main difference of our algorithm from predecessors is its ability to work with noisy data having a high rate of erasures (missed e…

COCA refactors training data to identify and erase unsafe concepts in LLMs.

We study "active" decision making over sensor networks where the sensors' sequential probing actions are actively chosen by continuously learning from past observations. We consider two network settings: with and without central coordination. In the first case, the network nodes interact with each other through a centr…

Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data Protection Regulation (GDPR). In this light, we investigate network embeddings, i.e., the representation of network nodes as low-dimensional …

The emergence of various intelligent mobile applications demands the deployment of powerful deep learning models at resource-constrained mobile devices. The device-edge co-inference framework provides a promising solution by splitting a neural network at a mobile device and an edge computing server. In order to balance…

We consider high dimensional sparse regression, and develop strategies able to deal with arbitrary -- possibly, severe or coordinated -- errors in the covariance matrix $X$. These may come from corrupted data, persistent experimental errors, or malicious respondents in surveys/recommender systems, etc. Such non-stochas…

We propose a framework for verifying data deletion in MLaaS systems.

Enhanced Sampling Scheme improves masked generative modeling.

PRS improves rejection sampling by learning better proposals.

This paper reviews various sampling methods from statistics and machine learning.

RISA improves VFL by using imputed samples with low uncertainty.

Improved privacy-preserving methods for estimating multiple samples from distributions.

Paper introduces a new sampling method combining Consistency Models with importance sampling.

Neural network accuracy improves with denser training samples.

Wedge Sampling improves tensor completion with nearly-linear sample complexity.

Algorithm samples constrained stochastic differential equations.

In modern data analysis, random sampling is an efficient and widely-used strategy to overcome the computational difficulties brought by large sample size. In previous studies, researchers conducted random sampling which is according to the input data but independent on the response variable, however the response variab…

Generative models map simple samples to complex target samples.

Sampling is a fundamental problem in computer science and statistics. However, for a given task and stream, it is often not possible to choose good sampling probabilities in advance. We derive a general framework for adaptively changing the sampling probabilities via a collection of thresholds.In general, adaptive samp…

New sampling algorithm for non-smooth potentials.

Efficiently samples sequences without replacement for machine learning models.

Ensemble sampling approximates Thompson sampling for complex models.

Optimizes biomolecular simulations by ranking adaptive sampling policies.

Optimizes sample and round complexity in adaptive sampling from multiple distributions.

Meta-learners improve causal effect estimation in small samples.

REP-GAN improves GANs by reparameterizing proposals for better sample quality and efficiency.

Unified framework for model-based RL with sample complexity guarantees.

Proposes a neural network method to combine nonprobability and probability survey samples.

Dynamic Influence Tracker measures changing sample importance during model training.

New sampling methods improve classifier performance estimation.