arXiv research

Reweighted ALPS improves sampling from multimodal distributions using warm start points.

New method achieves optimal sample complexity without warm-start in bilevel optimization.

WSD uses a deterministic model to accelerate diffusion-based sampling.

We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems. This is useful when optimizing the output of a stochastic simulator that fails to provide derivative information, for which Bayesian opt…

New algorithm speeds up HMC by generating a warm start in O(d^1/4) iterations.

Warm starts improve Gaussian process regression by up to 16x.

Warm-start strategies speed up GP inference by 19x.

We present an affine-invariant random walk for drawing uniform random samples from a convex body $\mathcal{K} \subset \mathbb{R}^n$ that uses maximum volume inscribed ellipsoids, known as John's ellipsoids, for the proposal distribution. Our algorithm makes steps using uniform sampling from the John's ellipsoid of the …

Improved log-concave sampling to $O(d^{1/2})$ with warm starts.

In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural properties of the problem (e.g., daily financial data) or active, where samples are selected according to a measure of their quality (e.g., experi…

A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.

Warm starts improve variational quantum algorithms by avoiding barren plateaus.

Faster algorithm for sampling logconcave densities in high dimensions.

Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which…

This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in rea…

This paper speeds up iterative GP inference with warm starting.

Enhances genetic programming for stock alpha discovery with warm start and structural constraints.

Flow-VQE uses generative flows to optimize VQE efficiently.

Paper presents algorithm for optimal job selection with dynamic scoring.

Study efficient power iteration for tensor models, proving convergence under specific conditions.

Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representations of ad IDs drastically improve CTR prediction accuracies. However, such learning techniques are data demanding and work poorly on new ads w…

The paper speeds up hyperparameter optimisation in Gaussian processes.

Accelerates optimal transport computation by 10x with spectral insights.

Bayesian optimization (BO) is a model-based approach for gradient-free black-box function optimization. Typically, BO is powered by a Gaussian process (GP), whose algorithmic complexity is cubic in the number of evaluations. Hence, GP-based BO cannot leverage large amounts of past or related function evaluations, for e…

We study the Gibbs sampling algorithm for continuous determinantal point processes. We show that, given a warm start, the Gibbs sampler generates a random sample from a continuous $k$-DPP defined on a $d$-dimensional domain by only taking $\text{poly}(k)$ number of steps. As an application, we design an algorithm to ge…

New model uses pretrained biochemical language models to generate drug compounds.

Active learning tackles cold start and imbalanced data issues.

This paper analyzes EM algorithm for softmax mixture models in high dimensions.

New bound shows variational algorithms may struggle with barren plateaus.

A new screening rule improves lasso solving speed.

DWTS uses observational data to improve clinical trial efficiency.

Zigzag sampling algorithm efficiently samples from strongly log-concave distributions with low computational cost.

New method for sparse kernel selection improves prediction accuracy.

New screening rules improve lasso model fitting efficiency.

New method solves nonsmooth low-rank matrix optimization problems efficiently.

Adaptive SAA solves large-scale stochastic linear programs efficiently.

DiffOPF solves multi-valued OPF problems by sampling from system history.

Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation. However, attempting to learn \textit{tabula rasa} disregards the logical structure of many domains as well as the wealth of readily available knowledge from domain experts that could help "warm start" t…

Bayesian online learning algorithm for one-pass data, achieving frequentist validity and uncertainty quantification.

A novel diffusion method for Bayesian posterior sampling with theoretical guarantees.

We use convex relaxation techniques to provide a sequence of solutions to the matrix completion problem. Using the nuclear norm as a regularizer, we provide simple and very efficient algorithms for minimizing the reconstruction error subject to a bound on the nuclear norm. Our algorithm iteratively replaces the missing…

Paper presents a method to solve variational inequalities with general constraints without requiring analytic solutions.

OKRidge solves sparse ridge regression problems for nonlinear systems.

ArtificialReplay improves data efficiency in bandits using historical data.

MALA mixes optimally in κ√d steps for log-concave sampling.

A bilevel optimization method for reweighting data leads to suboptimal results.

Optimizes portfolios with discrete units using simulated annealing.

Hyperparameter optimization aims to find the optimal hyperparameter configuration of a machine learning model, which provides the best performance on a validation dataset. Manual search usually leads to get stuck in a local hyperparameter configuration, and heavily depends on human intuition and experience. A simple al…