arXiv research

New algorithm reduces reinforcement learning regret by adapting to interaction variability.

New algorithm for contextual combinatorial bandits with probabilistic arm triggering.

Adaptive linear bandit algorithm with best-of-three-worlds regret bounds.

Optimum-statistical collaboration improves black-box optimization efficiency.

RAVEN-UCB addresses non-stationary MAB problems with tighter regret bounds.

New confidence intervals improve treatment effect estimation in randomized experiments.

Significant improvements in regret analysis for adaptive online learning problems.

Novel confidence sets improve linear bandit performance by adapting to unknown noise levels.

The paper offers error bounds for quantized dynamical models.

EVA adapts LoRA for faster, more efficient fine-tuning.

New algorithms improve best-arm identification with varying rewards.

New algorithm reduces best-in-class regret in contextual bandits.

The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an esti…

Shampoo achieves higher token efficiency than Muon in language models.

Improved spectral gap for MwG with adaptive RWM proposals.

The paper proposes a method for distribution-free prediction sets that adapt to unknown temporal changes.

A federated learning algorithm tackles linear bandits with adversarial actions, achieving optimal regret bounds.

New algorithm reduces regret for linear bandits with unknown noise variance.

Improved off-policy selection and learning in contextual bandits with better guarantees.

Paper proves robust M-estimators' coordinates' normality in high dimensions.

Examines algorithmic modeling across three cultures.

Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that $UCB$, $M3$, $S$ or $Exp3$ algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…

Meta-algorithm selection aims to choose the best algorithm selector for a given problem instance.

Proposes CLRS benchmark to evaluate algorithmic reasoning.

Combines multiple bandit algorithms to create a nearly optimal single algorithm.

We propose accelerated randomized coordinate descent algorithms for stochastic optimization and online learning. Our algorithms have significantly less per-iteration complexity than the known accelerated gradient algorithms. The proposed algorithms for online learning have better regret performance than the known rando…

The exchange algorithm is studied for its convergence and asymptotic variance.

Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. In real application, training data are always incomplete or some nodes are hidden. To deal with this problem many learning parameter algorithms are suggested foreground EM, Gibbs sampling and RBE algori…

No algorithm outperforms uniform sampling in A/B testing.

This review article surveys data augmentation MCMC algorithms.

Bayesian learning rule unifies and generalizes various machine learning algorithms.

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach th…

This review summarizes five Lasso optimization algorithms.

Neural networks mimic algorithms to solve complex problems.

Paper proposes a reinforcement learning framework for efficient hyper-parameter tuning of stochastic optimization algorithms.

In this paper, we propose a convergent parallel best-response algorithm with the exact line search for the nondifferentiable nonconvex sparsity-regularized rank minimization problem. On the one hand, it exhibits a faster convergence than subgradient algorithms and block coordinate descent algorithms. On the other hand,…

Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate infe…

New algorithms reduce bilevel optimization complexity to ε^(-1.5).

The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix. Before that recursive algorithm was applied in [4], its impr…

Researchers analyze how algorithmic and implementation choices affect RL performance.

Study on selecting between base algorithms in stochastic bandit problems.

New bounds derived for KG algorithm's performance in finite time.

Paper proves linear convergence of SCMS algorithm for directional data.

We resolve the fundamental problem of online decoding with general $n^{th}$ order ergodic Markov chain models. Specifically, we provide deterministic and randomized algorithms whose performance is close to that of the optimal offline algorithm even when latency is small. Our algorithms admit efficient implementation vi…

MLE and CVE are equivalent under exponential families, leading to faster and more stable EM algorithms.

New algorithm speeds up learning of graphical models.

The goal of data-driven algorithm design is to obtain high-performing algorithms for specific application domains using machine learning and data. Across many fields in AI, science, and engineering, practitioners will often fix a family of parameterized algorithms and then optimize those parameters to obtain good perfo…

The paper examines how algorithmic classification affects behavior and proposes democratizing stakes to mitigate predatory practices.