arXiv research

Proposes a flexible tournament design combining knockout and round-robin.

CRA improves UL-based CO solvers by dynamically smoothing and enforcing discreteness.

Balances and eliminates base algorithms in bandits and RL to bound total regret.

Gradient descent with biased rounding errors converges faster under certain conditions.

New algorithm eliminates arms to minimize regret in complex bandit problems.

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…

An algorithm learns from multiple models to match an oracle's risk.

Algorithm identifies the best arm in linear bandits with high probability.

New algorithm reduces adaptation lag in online model selection.

In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH), that uses meta-regressors to determine which candidate configurations should be el…

New algorithm reduces federated learning rounds and improves privacy.

New algorithm reduces memory usage for multi-pass bandit problems.

Proposes a new semi-parametric framework for batched bandits with covariates.

In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have $K$ arms $[K] = \{1, 2, \ldots, K\}$, and playing arm $i$ would gain reward from an unknown distribution for arm $i$ while obtaining reward feedback for all arms $j \ge i$. One-sided full information bandit ca…

Study on individual regret in cooperative MAB with agents communicating over a graph.

The paper tackles fair sequential decision making with biased linear bandit feedback.

This paper tackles objective inconsistency in federated optimization with heterogeneous clients.

A distributed algorithm reduces communication cost in linear bandits to near-optimal levels.

Two efficient algorithms improve online item recommendation for large user-item matrices.

Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.

This paper introduces a new metric, ULI, for RL that ensures both cumulative and instantaneous performance.

Classification may not be reliable for several reasons: noise in the data, insufficient input information, overlapping distributions and sharp definition of classes. Faced with several possibilities neural network may in such cases still be useful if instead of a classification elimination of improbable classes is done…

Paper addresses privacy and robustness in stochastic linear bandits.

Improved algorithm for adaptive dueling bandits with near-optimal regret bound.

Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination…

Recursive training of generative models can lead to model collapse, and the recursion converges to a unique limiting distribution.

We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank. Given a set of items to chose from, the elimination strategy starts with the whole item set and iteratively eliminates the least worthy item in the remaining …

Round surgery diagrams represent 3-manifolds in $S^3$.

Aims to eliminate domain bias in authentication without domain labels.

Contact round surgeries on $(\mathbb{S}^3,ξ_{st})$ help in constructing and understanding contact 3-manifolds.

In this article, we extend Huisken's theorem that convex surfaces flow to round points by mean curvature flow. We construct certain classes of mean convex and non-mean convex hypersurfaces that shrink to round points and use these constructions to create pathological examples of flows. We find a sequence of flows that …

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

Consider an analytic map of a neighborhood of 0 in a vector space to a Euclidean space. Suppose that this map takes all germs of lines passing through 0 to germs of circles. Such a map is called rounding. We introduce a natural equivalence relation on roundings and prove that any rounding, whose differential at 0 has r…

New method for online low-rank matrix completion with improved regret.

New findings show infinitely many knots cannot be smoothly round handle slices.

We discuss the integrability of orthogonal almost complex structures on Riemannian products of even-dimensional round spheres and give a partial answer to the question raised by E. Calabi concerning the existence of complex structures on a product manifold of a round 2-sphere and a round 4-sphere.

We simplify Khovanov homology for torus braids using Gaussian elimination.

This work investigates how multi-round reasoning improves LLM performance.

Logic approach finds real singularities in differential equations.

A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do not provide a compact way to express repeated structure when compared to plate diagrams for directed graphical models. To exploit efficient t…

We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four cas…

New findings on $κ$-solutions with round cylinder as asymptotic shrinker.

Round balls minimize liquid drop model volumes ≤ 1.

In this paper, we theoretically prove that adding one special neuron per output unit eliminates all suboptimal local minima of any deep neural network, for multi-class classification, binary classification, and regression with an arbitrary loss function, under practical assumptions. At every local minimum of any deep n…

Gradient descent stagnates in low-precision, but unbiased rounding schemes improve convergence.

We consider the problem of PAC learning the most valuable item from a pool of $n$ items using sequential, adaptively chosen plays of subsets of $k$ items, when, upon playing a subset, the learner receives relative feedback sampled according to a general Random Utility Model (RUM) with independent noise perturbations to…

Round cylinders are rigid in Ricci shrinkers close to the standard product.

A half-geodesic is a closed geodesic realizing the distance between any pair of its points. All geodesics in a round sphere are half-geodesics. Conversely, this note establishes that Riemannian spheres with all geodesics closed and sufficiently many half-geodesics are round.