arXiv research

New sparse Gaussian process method tackles unconstrained regression problems.

Study uses RL to optimize investment with financial constraints, showing exploration benefits.

Paper adapts diffusion sampler training for faster convergence and better sampling.

Proposes a Gaussian process model for constrained dynamics learning.

Investigates portfolio optimization with and without gearing constraints.

Unconstrained MLIPs outperform constrained ones in accuracy and speed.

Our paper explains deep neural collapse in multiple layers.

New approach reduces unconstrained linear bandits to simpler optimization problems.

We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own implies group calibration, that is, the outcome variable is conditionally independent of group membership given the score. We show that under r…

Novel method uses Gaussian process to estimate particle sizes from scattering data.

The paper analyzes reflected diffusion models on hypercube data.

Gaussian process (GP) modulated Cox processes are widely used to model point patterns. Existing approaches require a mapping (link function) between the unconstrained GP and the positive intensity function. This commonly yields solutions that do not have a closed form or that are restricted to specific covariance funct…

Develops parameter-free online mirror descent for optimal dynamic regret.

This manuscript proposes a probabilistic framework for algorithms that iteratively solve unconstrained linear problems $Bx = b$ with positive definite $B$ for $x$. The goal is to replace the point estimates returned by existing methods with a Gaussian posterior belief over the elements of the inverse of $B$, which can …

A new optimization algorithm improves convergence in unconstrained problems.

Unconstrained models learn physical symmetries effectively with simple data augmentation.

Gaussian process detects OOD data without needing OOD samples.

The paper explores solving inverse problems for ODEs with and without constraints.

Proposes ConstraintMatch for semi-supervised clustering with unconstrained data.

Paper studies sparsity and DAG constraints for learning linear DAGs.

This paper extends neural collapse to class-imbalanced datasets using an unconstrained ReLU feature model.

New algorithms reduce online learning regret by tracking gradient variation.

Shielded LMC samples from non-convex spaces with repulsive drift.

We prove a conjecture about approximating Gaussian Processes on one dimension.

A new L-BFGS method tackles large-scale optimization with fewer evaluations.

A new model reconciles rough volatility and jumps.

BOSS optimizes string inputs using string kernels and genetic algorithms.

This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial cha…

New algorithm minimizes cumulative loss in dynamic linear bandits without prior knowledge of comparator switches.

We propose an online convex optimization algorithm (RescaledExp) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the los…

We address structured covariance estimation in elliptical distributions by assuming that the covariance is a priori known to belong to a given convex set, e.g., the set of Toeplitz or banded matrices. We consider the General Method of Moments (GMM) optimization applied to robust Tyler's scatter M-estimator subject to t…

VAV method optimizes learning rate for faster, stable SGD convergence.

Paper tackles constrained bandit problems with a new learning framework.

We consider a variant of online convex optimization in which both the instances (input vectors) and the comparator (weight vector) are unconstrained. We exploit a natural scale invariance symmetry in our unconstrained setting: the predictions of the optimal comparator are invariant under any linear transformation of th…

The paper provides a method to minimize regret in estimate-then-optimize decision-making.

The paper forecasts joint electricity demand across 14 British regions using additive models.

Solves VaR-constrained portfolio optimization in markets with stochastic volatility.

Cookbook transforms constrained statistical inference into unconstrained problems.

Paper revisits DP-SCO in Euclidean and $\ell_p^d$ spaces, focusing on constrained and bounded sets.

DP-GD achieves dimension-independent convergence for unconstrained private GLMs.

In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics. We propose a novel parametrization that characterizes any collection of non-crossing quantile planes over arbitrarily shaped convex predictor domains in a…

Tree ensemble kernels improve Bayesian optimization for mixed features and constraints.

Yau's Affine Normal Descent optimizes smooth unconstrained problems with geometrically adapted directions.

Study optimal portfolio management with periodic evaluations in stochastic models, considering convex constraints.

Adam converges with high probability under unconstrained non-convex smooth stochastic optimizations.

Paper analyzes regret bounds for unconstrained online optimization.

A drawdown constraint forces the current wealth to remain above a given function of its maximum to date. We consider the portfolio optimisation problem of maximising the long-term growth rate of the expected utility of wealth subject to a drawdown constraint, as in the original setup of Grossman and Zhou (1993). We wor…

New methods solve complex optimization problems in machine learning.