arXiv research

Efficient method for generating adversarial examples with limited query budget.

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversaria…

We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budg…

Improves model classification accuracy in black-box settings.

New method reduces regret in budgeted learning problems.

LSDAT reduces query efficiency for decision-based adversarial attacks.

Budgeted deferral framework reduces expert query costs in machine learning.

Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection in health-care and finance. In these problems, there are two key challenges: the query budget is often limited; the ratio between classes i…

Study exact community recovery in noisy SBM with limited queries.

Noisy Max and Sparse Vector are selection algorithms for differential privacy and serve as building blocks for more complex algorithms. In this paper we show that both algorithms can release additional information for free (i.e., at no additional privacy cost). Noisy Max is used to return the approximate maximizer amon…

Online active regression algorithms minimize label queries for efficient data regression.

A new method for active learning works well across all label budgets.

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

Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…

Optimizes budgeted evaluations of LLMs by allocating queries to judges efficiently.

Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than ot…

We propose a novel active learning framework for activity recognition using wearable sensors. Our work is unique in that it takes physical and cognitive limitations of the oracle into account when selecting sensor data to be annotated by the oracle. Our approach is inspired by human-beings' limited capacity to respond …

Learn low-degree functions with few random queries.

This paper quantifies privacy loss in exploratory data analysis.

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…

New findings on complexity limits in fixed budget bandit identification.

Algorithm adapts to shifting domains with minimal label queries.

Adaptive querying learns user psychometrics with AI personas.

A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.

We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting. We formalize the problem setting and systematically evaluate what benefits the adversary can gain by using substitute models. We show that there is an exploration-exploita…

Extends risk control to adaptive data collection, anytime-valid guarantees.

Statistical query algorithms and low-degree tests are nearly equivalent in high-dimensional hypothesis testing.

LAZO reduces query complexity and variance in ZO methods.

Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incomplete data leads to skewed or misleading results. In this paper, we in…

FAQ efficiently evaluates LLMs with statistical guarantees using adaptive query selection.

New algorithm tackles dynamic query routing to multiple embedding models.

New method uses low logit rank to simplify complex language models.

Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.

A new perceptual adjustment query for metric learning reduces complexity in high-dimensional data.

CPATTA uses conformal prediction for efficient test-time adaptation.

Attention improves edge prediction in e-commerce graphs.

Unified framework for simulation-based inference learns a single model for multiple tasks.

Meta-AAD uses deep reinforcement learning to improve anomaly detection by selecting the most informative instances.

Understanding search queries is critical for shopping search engines to deliver a satisfying customer experience. Popular shopping search engines receive billions of unique queries yearly, each of which can depict any of hundreds of user preferences or intents. In order to get the right results to customers it must be …

The paper improves PCS approximation for ranking and selection under limited simulation budgets.

Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large-scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our a…

Proposes a method to allocate time budgets in mixed criticality systems.

Efficiently clusters noisy data with minimal queries.

We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision. We illustrate the viability of the fram…

Bayesian Algorithm Execution uses mutual information to infer properties of black-box functions efficiently.

The aim of black-box optimization is to optimize an objective function within the constraints of a given evaluation budget. In this problem, it is generally assumed that the computational cost for evaluating a point is large; thus, it is important to search efficiently with as low budget as possible. Bayesian optimizat…

Optimal ability estimation in adaptive testing with binary responses.

We propose an intriguingly simple method for the construction of adversarial images in the black-box setting. In constrast to the white-box scenario, constructing black-box adversarial images has the additional constraint on query budget, and efficient attacks remain an open problem to date. With only the mild assumpti…