arXiv research

Optimizes query routing to LLMs under cost and resource constraints.

Efficiently learns reward functions with fewer queries and shorter computation times.

In this paper, we are proposing a unified and principled method for both the querying and training processes in deep batch active learning. We are providing theoretical insights from the intuition of modeling the interactive procedure in active learning as distribution matching, by adopting the Wasserstein distance. As…

Data generation and labeling are usually an expensive part of learning for robotics. While active learning methods are commonly used to tackle the former problem, preference-based learning is a concept that attempts to solve the latter by querying users with preference questions. In this paper, we will develop a new al…

BMBO-DARN optimizes expensive functions with varying fidelities.

We propose a new batch mode active learning algorithm designed for neural networks and large query batch sizes. The method, Discriminative Active Learning (DAL), poses active learning as a binary classification task, attempting to choose examples to label in such a way as to make the labeled set and the unlabeled pool …

Meta-algorithm for efficient reinforcement learning from human preferences.

In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are more cost effective than one-by-one sequential queries. Furthermore, it is often not possible to directly choose items to query. Instead, the experimenter specifies a set of constraints that genera…

CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching

In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion tha…

The study analyzes batched methods for early stopping in stochastic multi-armed bandits.

Batch Thompson Sampling reduces exploration-exploitation trade-off in online decision making.

Paper addresses privacy and robustness in stochastic linear bandits.

New method reduces labeling costs in semi-supervised learning.

Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the B…

A new BO method tackles high-dimensional optimization without reconstruction.

We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates th…

The paper tackles fast rates in batch active learning with pool-based data.

Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger numb…

New algorithm guarantees performance on noisy data.

Differentiable learning via SGD and GD can simulate various learning problems, depending on precision and minibatch size.

BOE reformulates BO as a classifier for scalable batch optimisation.

Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. However, for many large-scale problem…

In many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on …

Data collection and labeling is one of the main challenges in employing machine learning algorithms in a variety of real-world applications with limited data. While active learning methods attempt to tackle this issue by labeling only the data samples that give high information, they generally suffer from large computa…

sGPO optimizes RLVR training by balancing inference FLOPs and training efficiency.

In this paper, we consider the convex and non-convex composition problem with the structure $\frac{1}{n}\sum\nolimits_{i = 1}^n {{F_i}( {G( x )} )}$, where $G( x )=\frac{1}{n}\sum\nolimits_{j = 1}^n {{G_j}( x )}$ is the inner function, and $F_i(\cdot)$ is the outer function. We explore the variance reduction based met…

Adapts data analysis for growing data, improving generalization guarantees.

Study learns random hypergraphs with queries, improving on previous results.

Stochastic momentum methods trade compute efficiency for serial runtime.

Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch gradient methods. But the efficacy of such ideas for mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm of moder…

We develop an algorithm for minimizing a function using $n$ batched function value measurements at each of $T$ rounds by using classifiers to identify a function's sublevel set. We show that sufficiently accurate classifiers can achieve linear convergence rates, and show that the convergence rate is tied to the difficu…

This work uses RL to optimize batch experiments in SDOE.

SOBER optimizes and quadrates efficiently in parallel for diverse tasks.

Unified study of active learning for deep neural networks.

TES optimizes black-box functions efficiently with minimal approximations.

Gradient descent fails to learn simple neural networks efficiently.

AdvMind detects adversary intent in black-box attacks with high accuracy.

Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an $ε$-greedy procedure for Bayesian optimisation in batch s…

Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high…

Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervised. To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network…

A new framework reduces traffic congestion by 36%.

Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regulations, such a data stream can only be stored and used for learning purposes in a limited duration. To overcome the noise in this on-line s…

Unified analysis of first-order methods for smooth games using IQCs.

New analysis for black-box learning without gradients, improving generalization bounds.

Influence functions help study large language model generalization, revealing surprising decay patterns.

Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Se…

New sublinear sketches improve ANN and KDE for massive data streams.