arXiv research

A scheme robust to action erasures improves MAB performance.

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

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…

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

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.

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

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…

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…

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…

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…

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…

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 …

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.

Formula derived for sample complexity in binary hypothesis testing.

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.

We give examples of symplectic actions of a cyclic group, inducing a trivial action on homology, on four-manifolds that admit Hamiltonian circle actions, and show that they do not extend to Hamiltonian circle actions. Our work applies holomorphic methods to extend combinatorial tools developed for circle actions to stu…

Reduces proper actions to simpler core actions for analysis.

Introduces Conditional Action Trees to simplify RL action spaces.

One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis. However, previous studies relied on human…

Study polar actions on Damek-Ricci spaces, proving existence and finding examples.

New RL algorithm tackles complex discrete action spaces.

Reduction principles for proper actions on smooth manifolds.

Totally geodesic sections found in polar actions.

Simplifies large action space bandits by selecting representative actions.

We study isometric actions on Riemannian symmetric spaces of noncompact type which are induced by reductive algebraic subgroups of the isometry group. We show that for such an action there exists a corresponding isometric action on a dual compact symmetric space, which reflects many properties of the original action. F…

Classifies totally geodesic submanifolds and polar actions on Stiefel manifolds.

Conditions for reducing quasi-actions to tree actions and group properties.

The study proves conditions for symplectic torus actions on manifolds with non-contractible orbits.

We identify action representations from video data, proving their statistical benefits.

Study properties of orbits of Hermann actions without commutability assumptions.

We classify the polar actions on the complex hyperbolic plane up to orbit equivalence. Apart from the trivial and transitive polar actions, there are five polar actions of cohomogeneity one and four polar actions of cohomogeneity two.

A new method learns action representations for reinforcement learning.

The paper studies curvatures and austere properties of orbits in symmetric spaces.

New reinforcement learning framework for adapting to new actions.

AQL uses amortized inference to handle high-dimensional action spaces in Q-learning.

Defines and computes a generalized spectral action for Lorentz warped products.

The paper studies acylindrical actions on trees and proves acylindrical hyperbolicity of Baumslag-Solitar groups.

UTE improves reinforcement learning by measuring action uncertainty, enhancing policy learning efficiency.