A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
Paper develops active learning for clustering unknown pairwise similarities.
In correlation clustering, we are given objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity sc…
We study the problem of recovering the structure underlying large Gaussian graphical models or, more generally, partial correlation graphs. In high-dimensional problems it is often too costly to store the entire sample covariance matrix. We propose a new input model in which one can query single entries of the covarian…
We study power-law correlations properties of the Google search queries for Dow Jones Industrial Average (DJIA) component stocks. Examining the daily data of the searched terms with a combination of the rescaled range and rescaled variance tests together with the detrended fluctuation analysis, we show that the searche…
We propose an approximation algorithm for efficient correlation search in time series data. In our method, we use Fourier transform and neural network to embed time series into a low-dimensional Euclidean space. The given space is learned such that time series correlation can be effectively approximated from Euclidean …
This paper models the crowdsourced labeling/classification problem as a sparsely encoded source coding problem, where each query answer, regarded as a code bit, is the XOR of a small number of labels, as source information bits. In this paper we leverage the connections between this problem and well-studied codes with …
Solves TOD systems' query annotation problem without explicit annotations.
This study examines how sequential correlations affect in-context learning in sequence models.
Efficiently clusters noisy data with minimal queries.
We propose a new localized inference algorithm for answering marginalization queries in large graphical models with the correlation decay property. Given a query variable and a large graphical model, we define a much smaller model in a local region around the query variable in the target model so that the marginal dist…
Study exact partition recovery with same-cluster oracle, bounded error.
Corella protects client data privacy in multi-server learning with correlated queries.
A new system combines vision and language for person re-identification.
It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern…
New bounds show complex neural networks need many queries to learn.
Preformer improves Transformer for long-term time series forecasting.
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
Attention improves edge prediction in e-commerce graphs.
New algorithm for clustering with faulty oracle achieves optimal queries and efficiency.
In this paper, we initiate a rigorous theoretical study of clustering with noisy queries (or a faulty oracle). Given a set of elements, our goal is to recover the true clustering by asking minimum number of pairwise queries to an oracle. Oracle can answer queries of the form : "do elements and belong to the…
New algorithm learns disjunctions faster than previous methods.
A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.
In many networks, vertices have hidden attributes, or types, that are correlated with the networks topology. If the topology is known but these attributes are not, and if learning the attributes is costly, we need a method for choosing which vertex to query in order to learn as much as possible about the attributes of …
We live in a computerized and networked society where many of our actions leave a digital trace and affect other people's actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of discipli…
Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The…
Estimates neural architecture performance speedily.
Improves search performance by transferring knowledge from recommender system.
Previous work has shown that popular trending events are important external factors which pose significant influence on user search behavior and also provided a way to computationally model this influence. However, their problem formulation was based on the strong assumption that each event poses its influence independ…
BMBO-DARN optimizes expensive functions with varying fidelities.
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transitive relational data (e.g. entailment graphs). However, OE learns a deterministic knowledge base, limiting expressiveness of queries and the…
New DP methods for estimating means and frequencies with varying privacy demands.
This paper uses LLMs for causal discovery with active learning and dynamic scoring to improve efficiency and fairness.
Proves SQ lower bounds for learning two-hidden-layer neural networks.
New privacy method for eye tracking data reduces correlations and maintains accuracy.
Multi-modal data collections, such as corpora of paired images and text snippets, require analysis methods beyond single-view component and topic models. For continuous observations the current dominant approach is based on extensions of canonical correlation analysis, factorizing the variation into components shared b…
New method improves multi-fidelity Bayesian optimization by accounting for local correlations and varying noise.
Algorithm learns arbitrary ReLU neurons under Gaussian inputs.
In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore p…
Neural network learns low-dimensional polynomials with SGD near information-theoretic limit.
Study exact community recovery in noisy SBM with limited queries.
ProbETA models travel time correlations between trips for better navigation.
Information retrieval (IR) systems often leverage query data to suggest relevant items to users. This introduces the possibility of unfairness if the query (i.e., input) and the resulting recommendations unintentionally correlate with latent factors that are protected variables (e.g., race, gender, and age). For instan…
Proposes a new query autocompletion method that maximizes retrieval performance.
A new method for private query release using Johnson-Lindenstrauss projection.
LAZO reduces query complexity and variance in ZO methods.
We consider a query-based data acquisition problem for binary classification of unknown labels, which has diverse applications in communications, crowdsourcing, recommender systems and active learning. To ensure reliable recovery of unknown labels with as few number of queries as possible, we consider an effective quer…
We consider the problem of adaptively PAC-learning a probability distribution 's mode by querying an oracle for information about a sequence of i.i.d. samples generated from . We consider two different query models: (a) each query is an index for which the oracle reveals…