The study compares how deletions and trades affect stock prices and spread changes.
problem Understanding the impact of deletions and trades on stock prices and spread changes.
method Examined the frequencies of relative amounts of price changing events due to trades, deletions, and order placements.
result Deletions of orders open the bid-ask spread more often than trades and have a similar effect on prices as trades.
Second-order optimizers retain residual information after data deletion, affecting machine unlearning.
problem Residual information in second-order optimizers after data deletion.
method Comparison of first-order and second-order learners, eigendecomposition analysis.
result Second-order optimizers retain residual information, not detectable by first-order analysis.
The configuration space F2(M) of ordered pairs of distinct points in a manifold M, also known as the deleted square of M, is not a homotopy invariant of M: Longoni and Salvatore produced examples of homotopy equivalent lens spaces M and N of dimension three for which F2(M) and F2(N) are not homoto…
Proposes a new jackknife method for time series hyperparameter selection.
problem Hyperparameter selection for time series models.
method Artificial delete-d jackknife approach.
result Asymptotic and finite-sample advantages demonstrated.
Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, b…
Transformer improves sequence generation with insertion and deletion phases.
problem Sequence generation challenges in machine translation.
method Insertion-Deletion Transformer with iterative insertion and deletion phases.
result Significant BLEU score improvement over insertion-only models.
New approach protects privacy of deleted records in machine learning.
problem Privacy of deleted records in machine learning models.
method Sound deletion guarantee and noisy gradient descent algorithm.
result Privacy of existing records is necessary for deleted records' privacy.
DaRE forests enable efficient data deletion from random forests.
problem Efficiently removing data from machine learning models.
method Random Forests with data deletion enabled (DaRE).
result Data deletion from DaRE models is orders of magnitude faster than retraining.
Paper tackles adaptive deletion of data points from trained models.
problem Adaptive deletion of data points from trained models.
method Reduction from adaptive to non-adaptive deletion guarantees using differential privacy and max information.
result Strong provable deletion guarantees for adaptive deletion sequences.
This research tackles data deletion in linear regression with noisy SGD, finding perfect deleted points.
problem Finding points to delete from a dataset without significantly affecting the training result.
method Signal-to-noise ratio and an algorithm based on it.
result The perfect deleted point is crucial for maintaining model performance and privacy budget.
Efficient algorithms for deleting data from machine learning models without significantly affecting performance.
problem Deleting data from machine learning models while maintaining performance.
method Leveraging convex optimization and reservoir sampling, the paper introduces algorithms for handling long sequences of adversarial updates.
result First data deletion algorithms that promise steady-state error not growing with the length of the update sequence.
Paper proposes a fast method for approximate data deletion in generative models.
problem Efficient data deletion in unsupervised learning models is an open problem.
method Density-ratio-based framework for generative models, fast method for approximate data deletion, statistical test.
result Theoretical guarantees and empirical demonstrations of the proposed methods across various generative models.
New method for efficiently deleting data from ML models.
problem Efficiently removing data from trained ML models without retraining.
method Approximate deletion method for linear and logistic models.
result Significantly faster than existing methods, with linear time dependence on feature dimension.
New formulas for feature importance tests in regression models.
problem Identifying important features in regression models.
method Established formulas for AUC criteria and proposed alternative metrics.
result Integrated Gradients (IG) performs nearly as well as Kernel SHAP (KS) but is faster.
This paper tackles efficient data deletion from machine learning models.
problem Efficiently removing individual data points from trained machine learning models.
method Investigates algorithmic principles for data deletion and proposes efficient deletion algorithms for k-means clustering.
result Proposes two provably efficient deletion algorithms for k-means clustering that achieve significant improvement in deletion efficiency.
Study on deleting user data in linear regression models to maintain limited memory.
problem Deleting user data in a limited time frame for statistical models.
method Proposed FIFD-OLS and FIFD-Adaptive Ridge algorithms for low-dimensional and online settings.
result Demonstrated effectiveness of FIFD-Adaptive Ridge in maintaining statistical efficiency.
The paper develops algorithms to find a robust summary of data under deletion, achieving good approximation guarantees.
problem Finding a summary of data that remains valuable even after some elements are deleted.
method Constant-factor approximation algorithms for deletion robust submodular maximization under matroid constraints.
result The algorithms provide good approximation guarantees for both centralized and streaming settings.
Agents use object-oriented reasoning to solve problems more quickly.
problem Finding solutions to problems more efficiently.
method Hierarchical controller directs low-level agent to simulate alternate states of the world.
result Achieves similar reward levels as non-hierarchical agents but with better data efficiency.
New examples show deletion type admissible pairs can be rigid under rational saturation.
problem Rigidity of admissible pairs of rational homogeneous spaces of Picard number one.
method Application of Mok's general criterion for non-subdiagram type admissible pairs.
result Examples of deletion type admissible pairs are rigid under rational saturation.
ID-ExpO fine-tunes neural networks for more faithful explanations.
problem Improving the faithfulness of explanations for complex machine learning models.
method Differentiable insertion/deletion metric-aware regularizers for optimization.
result Fine-tuned predictors produce more faithful explanations.
The paper compares two methods for handling missing data in causal discovery.
problem Handling missing data in causal discovery algorithms.
method Test-wise deletion and multiple imputation.
result Multiple imputation is more challenging for causal discovery than for estimation.
A new algorithm for competing agents in a two-sided market setting.
problem Decentralized competition between agents in a two-sided market with unknown valuations.
method UCB-D3 algorithm for UCB with Decentralized Dominant-arm Deletion.
result UCB-D3 is order optimal and achieves a new regret lower bound.
Study analyzes stock order transitions during US-China trade war using Markov chains.
problem Understanding order dynamics during extreme macroeconomic events.
method First-order time-homogeneous discrete-time Markov chain model.
result Active participation by different traders during high volatility days, influencing market outcomes.
We show that deleting an edge of a 3-cycle in an intrinsically knotted graph gives an intrinsically linked graph.
The paper tackles robust submodular maximization under matroid constraints, providing approximation algorithms for summary extraction.
problem Maximizing submodular functions while ensuring high value even after deletions.
method Constant-factor approximation algorithms for centralized and streaming settings, considering both non-monotone and monotone objectives.
result Approximation algorithms with space complexity depending on matroid rank and deleted elements, achieving improved factors in monotone cases.
Linear filtration helps delete training data from models.
problem Deleting training data from models when individuals request it.
method Linear filtration as a computationally efficient sanitization method.
result Demonstrates benefits in an adversarial setting over naive deletion schemes.
We use a variation on the commutator collection process to characterize those pure braids which become trivial when any one strand is deleted, or, more generally, those pure braids which become trivial when all the strands in any one of a list of sets of strands is deleted.
RS-Del provides robustness for sequence classifiers against edit distance attacks.
problem Certifying robustness of discrete sequence classifiers against edit distance attacks.
method Randomized deletion (RS-Del) for discrete sequence classifiers, focusing on edit distance-bounded adversaries.
result Achieved a certified accuracy of 91% at an edit distance radius of 128 bytes on malware detection.
Graph pruning improves neural network performance by addressing squashing and smoothing issues.
problem Over-squashing and over-smoothing in Graph Neural Networks.
method Proposes edge deletions to simultaneously address over-squashing and over-smoothing, optimizing spectral gap.
result Edge deletions improve generalization and distinguishability of nodes of different classes.
Gordon and Litherland showed that all compact, unoriented, possibly non-orientable surfaces in S3 bounded by a link are realted by attaching/deleting tubes and half twisted bands. In this note we give an elementary proof for this result.
We propose a framework for verifying data deletion in MLaaS systems.
problem Ensuring compliance with data deletion requests in MLaaS systems.
method Formal framework based on hypothesis testing, novel backdoor-based verification mechanism.
result Demonstrated high confidence in certifying data deletion with minimal impact on ML service accuracy.
New graph shows edge deletion/contraction doesn't always result in intrinsically linked graphs.
problem Edge operations in intrinsically knotted graphs don't always produce intrinsically linked graphs.
method Presented a new intrinsically knotted graph.
result Edge operations in intrinsically knotted graphs don't always result in intrinsically linked graphs.
New algorithms delete user data from machine learning models efficiently.
problem Deleting user data from machine learning models trained with empirical risk minimization.
method Developed an online unlearning algorithm using the infinitesimal jackknife, targeting non-smooth regularizers.
result Empirically improved runtime while maintaining memory requirements and test accuracy.
We investigate the problem of reliable communication between two legitimate parties over deletion channels under an active eavesdropping (aka jamming) adversarial model. To this goal, we develop a theoretical framework based on probabilistic finite-state automata to define novel encoding and decoding schemes that ensur…
Efficiently adds or deletes data in GBDT models.
problem Traditional GBDT training requires all data to be accessed simultaneously, limiting add/delete operations.
method Proposes an online learning framework for GBDT supporting incremental and decremental learning.
result First work to unify incremental and decremental learning on GBDT in-place.
Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and a…
New algorithms reduce matching market regret to log(T) with improved stability.
problem Minimizing regret in two-sided matching markets with bandit feedback.
method Phase-based algorithm with local arm deletion to improve stability.
result Achieves Θ(log(T)) regret for markets with uniqueness consistency.
There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial attack. In order to solve the "needle in the haystack" problem of which samples t…
New framework for consistent submodular maximization with insertions and deletions.
problem Maintaining near-optimal solutions in a dynamic setting with insertions and deletions.
method Developed a general framework for fully dynamic submodular maximization, instantiated for cardinality and rank-k matroid constraints.
result First constant-factor approximations with sublinear consistency for both cardinality and rank-k matroid constraints.
Well-quasi-order proved for plane minors; polynomial-time algorithm for link diagrams.
problem Proving the well-quasi-order of plane minors and solving link diagrams.
method Sequence of vertex and edge deletions and contractions to prove well-quasi-order; polynomial-time algorithm for link diagrams.
result Well-quasi-order of plane minors established; polynomial-time algorithm for link diagrams.
Bayesian models can be tricked into believing false data.
problem Vulnerability of Bayesian inference to data poisoning attacks.
method Developed attacks to manipulate Bayesian posterior through deletion and replication of data.
result Demonstrated that Bayesian inference can be steered to target distributions.
New algorithm for maximizing submodular functions in real-time data changes.
problem Maximizing submodular functions under dynamic constraints.
method Randomized algorithm with O(k2) amortized update time. result 4-approximate solution to submodular maximization problem.
We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deleting model edges, and (2) rectifying the result by applying an edge-by-edge correction. The approach leads to an intuitive framework in which o…
Researchers develop methods to prevent GANs from generating certain types of images.
problem Pre-trained GANs sometimes produce undesirable samples.
method Post-editing GANs to prevent certain types of outputs, using three algorithms.
result Our algorithms effectively prevent GANs from generating certain types of images while maintaining high quality.
Paper proposes first unlearning algorithm for MCMC models.
problem Enforcing right to be forgotten in AI causes high costs for data deletion.
method Converts MCMC unlearning to explicit optimization problem, designs MCMC influence function.
result MCMC unlearning does not compromise generalizability of models.
A framework for certified unlearning in decentralized federated learning.
problem Privacy-preserving machine learning in decentralized federated learning.
method Newton-style updates to quantify and correct data influence, using Fisher information matrices for scalability.
result The proposed framework ensures that the unlearned model is difficult to distinguish from a retrained model without the deleted data.
In order to apply quantum topology methods to nonplanar graphs, we define a planar diagram category that describes the local topology of embeddings of graphs into surfaces. These \emph{virtual graphs} are a categorical interpretation of ribbon graphs. We describe an extension of the flow polynomial to virtual graphs, t…
XL-Editor improves sentence post-editing using XLNet's variable-length insertion probability.
problem Post-editing sentences to refine generated text.
method XL-Editor trains XLNet to estimate variable-length insertion probabilities and apply post-editing operations.
result XL-Editor outperforms XLNet on text insertion and deletion tasks, and achieves significant style transfer improvements.