arXiv research

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

A scheme robust to action erasures improves MAB performance.

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 uses RNNs to design LDPC codes for binary erasure channels.

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 a bad arm existing checking problem in which a player's task is to judge whether a positive arm exists or not among given K arms by drawing as small number of arms as possible. Here, an arm is positive if its expected loss suffered by drawing the arm is at least a given threshold. This problem is a formalizati…

New algorithms for streaming bandits with limited memory.

Study best arm identification in restless Markov multi-armed bandits with state-dependent transitions.

Paper tackles identifying an odd arm in a multi-armed bandit with restless Markov processes and trembling hand.

Improved theoretical guarantees for Top Two algorithms.

We address the M-best-arm identification problem in multi-armed bandits. A player has a limited budget to explore K arms (M<K), and once pulled, each arm yields a reward drawn (independently) from a fixed, unknown distribution. The goal is to find the top M arms in the sense of expected reward. We develop an algorithm …

Sequential screening and dynamic regret in multi-armed bandits with arriving arms

Paper tackles good arm identification in stochastic bandits.

We consider the best-arm identification problem in multi-armed bandits, which focuses purely on exploration. A player is given a fixed budget to explore a finite set of arms, and the rewards of each arm are drawn independently from a fixed, unknown distribution. The player aims to identify the arm with the largest expe…

A novel algorithm reduces communication costs in federated best arm identification.

New algorithms identify best arm with less pulls, adapting to arm covariances.

Study best arm identification in restless bandits with unknown TPMs.

New method for identifying best arm in batched multi-armed bandit problems.

Optimal best-arm identification with known number of optimal arms.

We consider a novel multi-armed bandit framework where the rewards obtained by pulling the arms are functions of a common latent random variable. The correlation between arms due to the common random source can be used to design a generalized upper-confidence-bound (UCB) algorithm that identifies certain arms as $non-c…

Paper tackles outlier detection in multi-armed bandits, achieving high accuracy with reduced exploration costs.

We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward $v_a$ and can choose an amount $x_a$ to pass on to the principal (keeping $v_a-x_a$ for itself). All non-pulle…

Improved best-arm identification in correlated multi-armed bandits.

Optimal best-arm identification in linear bandits reduces sampling budget.

New model for multi-armed bandits with growing arms.

Study optimizes identifying the best arm with fixed rounds and Gaussian outcomes.

This work optimizes identifying good arms in nonparametric multi-armed bandits.

We analyze the regret of combinatorial Thompson sampling (CTS) for the combinatorial multi-armed bandit with probabilistically triggered arms under the semi-bandit feedback setting. We assume that the learner has access to an exact optimization oracle but does not know the expected base arm outcomes beforehand. When th…

Develops a framework for clustering and distribution matching with bandit feedback.

New algorithm reduces super-arm selection complexity exponentially.

New algorithm identifies best target arm with known additive relationship between source and target MAB instances.

This paper studies Thompson sampling's arm-pull dynamics and inference, revealing key differences from UCB algorithms.

Study best arm identification with limited precision sampling in bandits.

The paper tackles a bandit problem with infinitely many arms per group, aiming to identify the group with the highest quantile reward.

New algorithm reduces worst-case sample complexity for learning best arm.

Greedy algorithms outperform UCB in many-armed bandit problems.

New strategies for identifying the best arm in bandits with decreasing variances.

We consider the Max $K$-Armed Bandit problem, where a learning agent is faced with several sources (arms) of items (rewards), and interested in finding the best item overall. At each time step the agent chooses an arm, and obtains a random real valued reward. The rewards of each arm are assumed to be i.i.d., with an un…

Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is an unknown vector of parameters that is common across the arms, and a generalized linear model captures the dependence of rewards on the cov…

New methods optimize personalized treatment assignment in trials with many arms.

Sampling from distributions to find the one with the largest mean arises in a broad range of applications, and it can be mathematically modeled as a multi-armed bandit problem in which each distribution is associated with an arm. This paper studies the sample complexity of identifying the best arm (largest mean) in a m…