arXiv research

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.

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

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

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

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…

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.

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.

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…

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

Flow-VQE uses generative flows to optimize VQE efficiently.

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.

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 …

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…

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…

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.

A new screening rule improves lasso solving speed.

In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem. The problem allows job positions to be either free or already occupied at the beginning of the process. Throughout the selection process, the decision m…

DWTS uses observational data to improve clinical trial efficiency.

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

New screening rules improve lasso model fitting efficiency.

Adaptive SAA solves large-scale stochastic linear programs efficiently.

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

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.

New bound shows variational algorithms may struggle with barren plateaus.

ArtificialReplay improves data efficiency in bandits using historical data.

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

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…

A new federated learning method speeds up training by selecting faster nodes first.

Algorithm learns halfspaces with Tsybakov noise in polynomial time.

This work improves convergence guarantees for unadjusted HMC in KL and Rényi divergences.

New findings show increased exploration needed in non-stationary RL tasks.

We investigate the feasibility of learning from a mix of both fully-labeled supervised data and contextual bandit data. We specifically consider settings in which the underlying learning signal may be different between these two data sources. Theoretically, we state and prove no-regret algorithms for learning that is r…