Developed HIquant to quantify proteoforms accurately without biases.
problem Peptide-specific biases in quantifying proteoforms by MS methods.
method First-principles model (HIquant) for quantifying proteoform stoichiometries.
result High accuracy in quantifying fractional PTM occupancy without external standards.
FlipTest detects discrimination in classifiers using optimal transport.
problem Detecting discrimination in classifiers without causal information.
method Optimal transport to match individuals in different protected groups, creating similar pairs of in-distribution samples.
result FlipTest identifies subgroups that may be harmed by model discrimination, even when the model satisfies group fairness criteria.
This work improves testing of machine learning model modifications using novel statistical methods.
problem Overfitting and conservative Bonferroni correction when testing multiple model modifications.
method Introduces alpha-recycling and SRGPs to control error rate and approve more beneficial modifications.
result Novel statistical methods approve a higher number of beneficial modifications than previous approaches.
New method detects RNA modifications without prior training, revealing novel sites.
problem Detecting RNA modifications with high accuracy and sensitivity.
method Anomaly detection using nanopore raw ionic current signals and nearest neighbor comparison.
result Detects diverse RNA modifications without prior training, including a novel 2'-O-methylated site in DENV.
We define an operation on homology B4 which we call an n-twist annulus modification. We give a new construction of smoothly slice knots and exotically slice knots via n-twist annulus modifications. As an application, we present a new example of a smoothly slice knot with non-slice derivatives. Such examples we…
Study shows stability of locally conformally balanced condition under modifications but not under small deformations.
problem Stability of locally conformally balanced condition under small deformations and modifications.
method Proved stability under proper modifications and instability under small deformations using examples and Hilbert-Chow map.
result Stability of locally conformally balanced condition under proper modifications and instability under small deformations.
DeepDiff predicts differential gene expression from histone modifications using deep learning.
problem Predicting differential gene expression from histone modification signals, capturing combinatorial effects.
method Attention-based deep learning architecture with multiple LSTM modules and attention mechanisms.
result DeepDiff significantly outperforms state-of-the-art baselines for differential gene expression prediction.
Proposes simplified SHAP for faster black-box model explanations.
problem Computational expense of SHAP for models with many features.
method Ensemble of random SHAPs with feature selection and point generation.
result Efficiency and properties demonstrated through numerical experiments.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
Paper proposes adversarial modifications for link prediction models to improve robustness and interpretability.
problem Improving accuracy is not enough; robustness and interpretability are also crucial for link prediction models.
method Adversarial modifications to identify influential facts and evaluate model sensitivity and interpretability.
result The approach identifies the most influential facts and evaluates the sensitivity of link prediction models to additional facts.
Study of unimodular Sasaki and Vaisman Lie groups, determining all modifications explicitly.
problem Classifying unimodular Sasaki and Vaisman Lie groups.
method Applying the technique of modification to determine all homogeneous Sasaki and Vaisman manifolds of unimodular Lie groups explicitly.
result Complete classification of unimodular Sasaki and Vaisman Lie groups.
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational …
Adaptive weighting improves Deep Forest classifier's performance.
problem Improving Deep Forest classifier's efficiency and accuracy.
method Adaptive weighing of training instances at each cascade level, inspired by AdaBoost.
result The modified Deep Forest classifier performs better than the original.
Method explains anomaly detection by generating normal modifications.
problem Complexity of deep learning methods in anomaly detection.
method Generates multiple alternative modifications for anomalies.
result High-quality semantic explanations provided for anomaly detection.
We study Khovanov homology classes which have state cycle representatives, and examine how they interact with Jacobsson homomorphisms and Lee's map Φ. As an application, we describe a general procedure, quasipositive modification, for constructing H-thick knots in rational Khovanov homology. Moreover, we show that sp…
Preserves hyperbolicity in link complements with two moves.
problem Maintaining hyperbolicity in link complements.
method Chain move and switch move to preserve hyperbolicity.
result Preserves hyperbolicity in a substantial number of links.
The perceptron's compression is explored and applied to neural networks.
problem Understanding and improving the perceptron's margin and compression.
method Modifications to the perceptron algorithm and deductions from compression.
result Better guarantees on the margin of the perceptron's hyperplane.
Paper proposes using pairwise feature comparisons to infer modification costs for user recourse.
problem Learning and inferring user preferences for modifying features in black-box models.
method Bradley-Terry model for inferring feature-wise costs from non-exhaustive human comparison surveys.
result Non-exhaustive human surveys can efficiently learn feature costs, enabling recourse finding.
We propose a modification of the three-manifold invariant based on the use of Euclidean metric values ascribed to the elements of manifold triangulation. We thus obtain a nontrivial invariant that can, in particular, distinguish non-homeomorphic lens spaces.
Max-Cut decision tree improves classification accuracy and reduces computation time.
problem Improving decision tree accuracy and efficiency for complex classification tasks.
method Alternative splitting metric (max cut) and PCA-based feature selection at each node.
result 49% improvement in accuracy with 94% reduction in CPU time on CIFAR-100 data.
We briefly review the approach to optimization of portfolios according to the theory of Markowitz and propose a further modification that can improve the outcome of the optimization process. The modification takes account of the entropic contribution from the time series used to compute the parameters in the Markowitz …
In this paper we suggest a modification of the regression-based variance reduction approach recently proposed in Belomestny et al. This modification is based on the stratification technique and allows for a further significant variance reduction. The performance of the proposed approach is illustrated by several numeri…
Kapustin and Witten associate a Hecke modification of a holomorphic bundle over a Riemann surface to a singular monopole on a Riemannian surface times an interval satisfying prescribed boundary conditions. We prove existence and uniqueness of singular monopoles satisfying prescribed boundary conditions for any given He…
We present an axiomatic modification of quaternionic quantum mechanics with a possible-worlds semantics capable of predicting essential "nonquantum" features of an observable universe model - the dimensionality and topology of spacetime, the existence, the signature and a specific form of a metric on it, and certain na…
A new bias score method optimizes fairness in classification.
problem Ensuring fairness in binary classification under group constraints.
method Introducing bias scores and developing a post-hoc approach to adapt to fairness constraints.
result The method maintains high accuracy while ensuring fairness constraints.
Label smoothing improves model robustness against misspecification.
problem Improving model robustness against model misspecification.
method Introducing modified label smoothing (MLSLR) that maintains consistent probability estimation while modifying the loss function.
result MLSLR exhibits higher robustness against model misspecification than conventional label smoothing.
Automatic video modification to hide faces while maintaining pose, illumination, and expression.
problem Face de-identification in video to protect identities.
method A novel feed-forward encoder-decoder network architecture conditioned on facial image high-level representation.
result Fully automatic video modification at high frame rates with minimal distortion.
In this paper, we consider a proper modification f:M~→M between complex manifolds, and study when a generalized p−Kähler property goes back from M to M~. When f is the blow-up at a point, every generalized p−Kähler property is conserved, while when f is the blow-up along a submanifold, t…
Thompson Sampling remains differentially private with minimal modifications.
problem Ensuring privacy in Thompson Sampling for multi-arm bandits.
method Demonstrated differential privacy of original Thompson Sampling, provided per-round guarantees, and introduced modifications for tighter privacy.
result Privacy guarantees can be tuned by modifying the algorithm, and these modifications impact expected regret.
Modified LSTM cells iteratively process input, improving model performance.
problem Improving LSTM network performance for RNNs.
method Iterative scheme where LSTM computations are repeated over constant input and cell state values, updating hidden state multiple times.
result Enhanced model performance, 3 times more parameters than original model.
Proposes a probabilistic method for generating semantically-aware adversarial examples.
problem Generating adversarial examples that are difficult for humans to detect while preserving semantics.
method Embeds subjective understanding of semantics as a distribution into adversarial example generation.
result Achieves higher success rates in circumventing adversarial defense mechanisms.
We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al. (2012), Claassen et al. (2013)). The first step of all these algorithms consists of the PC-algorithm. This algorithm is known to be order-d…
Modified BFGS and LBFGS++ libraries boost performance for non-parallelizable functions.
problem Improving performance of non-parallelizable functions using SIMD and AAD.
method Modifications to BFGS and LBFGS++ libraries, utilizing SIMD and Automatic Differentiation (AAD).
result Up to 3.8 times faster for European Swaption curve calibration and 1.4 times faster for LMM model calibration.
MCD automates counterfactual design searches for multi-modal tasks.
problem Designing for multi-objective goals and complex constraints.
method Model-agnostic counterfactual search method for multi-modal design modifications.
result MCD streamlines and automates counterfactual search, recommending effective design modifications.
Enhances statistical mechanics solving using VANs with MCMC or importance sampling.
problem Sampling error in solving statistical mechanics using VANs.
method Integrates MCMC or importance sampling to correct sampling error in VANs.
result Asymptotically unbiased estimators for physical quantities are achieved.
A stealthy framework injects faults into DNNs to misclassify images without affecting overall accuracy.
problem Vulnerability of deep neural networks to misclassification attacks.
method Fault sneaking attack using ADMM optimization with constraints on maintaining model accuracy and minimizing parameter modifications.
result The framework can inject multiple sneaking faults into DNNs without reducing overall accuracy.
We discuss a general framework for cutting constructions and reinterpret in this setting the work on non-Abelian symplectic cuts by Weitsman. We then introduce two analogous non-Abelian modification constructions for hyperkähler manifolds: one modifies the topology significantly, the other gives metric deformations. We…
Study efficient black-box attacks on classifiers with limited queries.
problem Attacking machine learning classifiers with query constraints.
method Bayesian optimization to minimize query count.
result Number of queries can be reduced to about 1/10th of random strategy.
New groups contactomorphic to stratified ones found.
problem Understanding contactomorphic relationships between polarised and stratified Lie groups.
method Constructing modifications of stratified groups and proving contactomorphic relationships.
result Polarised groups are contactomorphic to stratified groups under specific conditions.
Improved peak detection in ChIP-seq data reduces over-dispersion.
problem Over-dispersion in ChIP-seq data reduces peak detection accuracy.
method Supervised segmentation models with alternative noise assumptions.
result Improved peak detection accuracy compared to natural assumptions.
The two main topics of this text are as follows: Firstly, three modifications of the theorem of Beltrami will be presented for diffeomorphisms between Riemannian manifolds and a space form which preserve the geodesic circles, the geodesic hyperspheres, or the minimal surfaces, respectively. Secondly, it is defined what…
Defines complex manifolds for Khovanov homology computation.
problem Constructing symplectic Khovanov homology for links.
method Moduli spaces of Hecke modifications for elliptic and rational curves.
result Explicit computations of PM(X,1,n) for rational and elliptic curves. PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modifications to the original model that both simplify its structure an…
Study shows how to manipulate VAEs for attacks and assess their robustness.
problem Adversarial attacks on Variational Autoencoders (VAE).
method Examine modifications to VAEs and propose metrics for robustness.
result Metrics to quantify the robustness of VAEs to adversarial attacks.
A new method improves continual learning by replaying pseudo data and using orthogonal weight modification.
problem Catastrophic forgetting in class incremental learning.
method Generative replay with orthogonal weight modification.
result Our method achieves substantial improvement over conventional generative replay and OWM.
In this note we show that for the group G = U(N) the space of Hecke modifications of a rank N vector bundle over a Riemann surface C coincides with the moduli space of solutions of certain non-abelian vortex equations over C . Through the recent work of Kapustin and Witten this then leads to an isomorphism between the …
This paper unifies observability notions for colored graphs and identifies graph modifications to improve observability.
problem Observability of colored graphs and its relationship to graph properties.
method Unified framework of observability notions, graph modifications, and NP-completeness results.
result Identification of graph modifications to improve observability and NP-completeness of one modification problem.
The paper modifies twistor spaces for Kähler surfaces and finds metrics on the modified spaces.
problem Constructing a modified twistor space for Kähler surfaces and studying its properties.
method Constructing a modification S(M) of the twistor space of a Kähler scalar flat surface M and studying its complex-geometric and metric properties. result Complete balanced metrics are constructed on S(M) and it is shown that S(M) cannot be Kähler when M is a compact simple hyperkähler manifold.