arXiv research

Improved approximations for rough Heston model reduce errors.

Quantum computers outperform classical methods in density modeling.

The study reveals the efficiency of sampling from tilted distributions.

Study shows SQ hardness for multiclass linear classification with random noise.

We provide a detailed study on the implicit bias of gradient descent when optimizing loss functions with strictly monotone tails, such as the logistic loss, over separable datasets. We look at two basic questions: (a) what are the conditions on the tail of the loss function under which gradient descent converges in the…

In this article we construct closed, isospectral, non-isometric locally symmetric manifolds. We have three main results. First, we construct arbitrarily large sets of closed, isospectral, non-isometric manifolds. Second, we show the growth of size these sets of isospectral manifolds as a function of volume is super-pol…

We explain how to adapt a construction of M. Sageev's to construct a proper action on a CAT(0) cube complex starting from a proper action on a wall space, and use this to deduce that if G is a group containing an amenable subgroup H of super-polynomial growth and G acts properly on a space with walls then there are arb…

Lower bound shows super-polynomial gap for estimating truncated Gaussian means.

We prove that the colored HOMFLY polynomial of a link, colored by symmetric or exterior powers of the fundamental representation, is q-holonomic with respect to the color parameters. As a result, we obtain the existence of an (a,q) super-polynomial of all knots in 3-space. Our result has implications on the quantizatio…

In many estimation problems, e.g. linear and logistic regression, we wish to minimize an unknown objective given only unbiased samples of the objective function. Furthermore, we aim to achieve this using as few samples as possible. In the absence of computational constraints, the minimizer of a sample average of observ…

We prove that the HOMFLYPT polynomial of a link, colored by partitions with a fixed number of rows is a $q$-holonomic function. Specializing to the case of knots colored by a partition with a single row, it proves the existence of an $(a,q)$ super-polynomial of knots in 3-space, as was conjectured by string theorists. …

In this work, we consider solutions of the Maxwell equations on the Schwarzschild-de Sitter family of black hole spacetimes. We prove that, in the static region bounded by black hole and cosmological horizons, solutions of the Maxwell equations decay to stationary Coulomb solutions at a super-polynomial rate, with deca…

Enhances quantum computing for symmetrical systems, proving a new class of problems.

We modify our previous construction of link homology in order to include a natural duality functor $\mathfrak{F}$. To a link $L$ we associate a triply-graded module $HXY(L)$ over the graded polynomial ring $R(L)=\mathbb{C}[x_1,y_1,\dots,x_\ell,y_\ell]$. The module has an involution $\mathfrak{F}$ that intertwines the F…

New algorithm recovers sparse signals from linearly sparse dictionaries efficiently.

LLMs struggle with arithmetic tasks unless they use high numerical precision.

New algorithm learns ReLU networks efficiently using Schur polynomials.

We consider braids with repeating patterns inside arbitrary knots which provides a multi-parametric family of knots, depending on the "evolution" parameter, which controls the number of repetitions. The dependence of knot (super)polynomials on such evolution parameters is very easy to find. We apply this evolution meth…

WildCat efficiently compresses neural network attention mechanisms.

Study shows SQ lower bounds for learning ReLUs with Massart noise.

The paper proves barriers to approximating functions with small weights and depth in neural networks.

Modern inference and learning often hinge on identifying low-dimensional structures that approximate large scale data. Subspace clustering achieves this through a union of linear subspaces. However, in contemporary applications data is increasingly often incomplete, rendering standard (full-data) methods inapplicable. …

Quantum circuits are hard to learn on average.

New framework formalizes RLHF trilemma: improving safety, fairness, and robustness is computationally infeasible.

This paper strengthens the computational separation between multimodal and unimodal learning, showing unimodal learning is hard on typical instances.

The paper explores how LLMs with CoT improve performance on complex tasks.

The paper tackles robust policy learning in multitask contextual bandits with adversarial users.

Algorithm learns near-optimal policies for reward-mixing MDPs with few latent contexts.

Efficient algorithm for online learning with Massart noise achieves near-optimal mistake bound.

Polynomial-time algorithm learns high-dimensional halfspaces without labels.

The goal of this paper is to characterize function distributions that deep learning can or cannot learn in poly-time. A universality result is proved for SGD-based deep learning and a non-universality result is proved for GD-based deep learning; this also gives a separation between SGD-based deep learning and statistic…

This paper is concerned with jointly recovering $n$ node-variables $\left\{ x_{i}\right\}_{1\leq i\leq n}$ from a collection of pairwise difference measurements. Imagine we acquire a few observations taking the form of $x_{i}-x_{j}$; the observation pattern is represented by a measurement graph $\mathcal{G}$ with an ed…

Near-optimal SQ hardness shows learning halfspaces with Massart noise is hard.

As the success of deep learning reaches more grounds, one would like to also envision the potential limits of deep learning. This paper gives a first set of results proving that certain deep learning algorithms fail at learning certain efficiently learnable functions. The results put forward a notion of cross-predictab…

Two algorithms learn Gaussian graphical models from Glauber dynamics trajectories, achieving optimal performance.

Algorithm learns means of spherical Gaussian mixtures efficiently in low dimensions.

Efficiently estimates Gaussian distributions privately and robustly.

Paper addresses classification under misspecification, providing simpler algorithms and resolving open questions.

New lower bounds show learning intersections of halfspaces is hard even for a few halfspaces.

Efficient algorithm learns mixture models of heavy-tailed distributions.

Sum-of-Squares lower bound shows NGCA requires more samples than known algorithms.

Proves weak convergence equals mean convergence in GGC.

This is an intuitive survey of extrinsic and intrinsic notions of convergence of manifolds complete with pictures of key examples and a discussion of the properties associated with each notion. We begin with a description of three extrinsic notions which have been applied to study sequences of submanifolds in Euclidean…

The abstract discusses convergence properties of Lipschitz functions and sets defined by equations.

Study shows intrinsic timed Hausdorff convergence leads to Gromov-Hausdorff and big bang convergence.

Studied SGD convergence under weak conditions.

Study on convergence rate of $Q$-curvature flow in 6 dimensions.

The objective of this paper is to introduce the notion of generalized almost statistical (briefly, GAS) convergence of bounded real sequences, which generalizes the notion of almost convergence as well as statistical convergence of bounded real sequences. As a special kind of Banach limit functional, we also introduce …