PASS protects private attributes by stochastically substituting data.
problem Protecting private attributes in ML services while maintaining data utility.
method PASS uses stochastic data substitution with a novel loss function derived from information theory.
result PASS effectively protects private attributes across various datasets.
Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern o…
Paper introduces a new method to create adversarial examples against gradient-obfuscating defenses.
problem Crafting adversarial examples to fool gradient-obfuscating defenses.
method Stochastic Substitute Training (SST), a gray-box approach.
result Adversaries can create adversarial examples without knowledge or limited information about the defense.
SAFER method certifies robustness to word substitutions without model structure.
problem Certified robustness against synonymous word substitutions in NLP models.
method Randomized smoothing with stochastic ensemble of randomized inputs.
result Significantly outperforms state-of-the-art methods for certified robustness.
The paper introduces a method to improve adversarial robustness in neural networks using randomized perturbations.
problem Deep neural networks are sensitive to small perturbations on correctly classified examples, leading to erroneous predictions.
method The approach uses randomized perturbations to optimize the worst case loss function over all possible substitutions of training examples, ensuring that substitution likelihood is weighted by the proximity to the original word.
result The method achieves performance gains and differentially-private model training, improving robustness against adversarial attacks.
DES training speeds up large-scale recommender systems convergence.
problem Training large-scale recommender systems with dynamic sparse features.
method Distributed Equivalent Substitution (DES) framework for fully synchronous training.
result DES achieves higher AUC and up to 68.7% communication savings.
New method removes contrastive loss by adding a prediction head, revealing learning mechanisms.
problem Understanding why neural networks learn competitive representations despite trivial optima.
method Empirical and theoretical analysis of a trainable, identity-initialized prediction head.
result The trainable prediction head enables learning all features, preventing dimensional collapse.
01 loss neural network improves adversarial robustness on CIFAR10.
problem Improving machine learning models' resistance to adversarial attacks.
method Proposes a single hidden layer 01 loss neural network trained with stochastic coordinate descent.
result 01 loss network has the largest adversarial distortion compared to other models.
We define 2-dimensional topological substitutions. A tiling of the Euclidean plane, or of the hyperbolic plane, is substitutive if the underlying 2-complex can be obtained by iteration of a 2-dimensional topological substitution. We prove that there is no primitive substitutive tiling of the hyperbolic plane $\mathbb{H…
The L1-regularized models are widely used for sparse regression or classification tasks. In this paper, we propose the orthant-wise passive descent algorithm (OPDA) for optimizing L1-regularized models, as an improved substitute of proximal algorithms, which are the standard tools for optimizing the models nowada…
This paper reduces the number of queries needed for black-box attacks on DNNs.
problem Efficiently conducting black-box attacks on deep neural networks.
method Active learning strategy combined with state-of-the-art white-box attack methods.
result Significantly reduces the number of queries needed for black-box attacks.
01 loss is robust to outliers and adversarial attacks, outperforming convex losses.
problem Improving robustness in binary classification models.
method Developed stochastic coordinate descent algorithms for 01 loss classifiers and neural networks.
result 01 loss classifiers are more robust to adversarial attacks compared to convex losses.
A method estimates causal parameters using a latent variable recovery.
problem Estimating causal parameters in contexts with multiple causes and unobserved confounding.
method Substitute adjustment via recovery of latent variables.
result Substitute adjustment estimates adjusted regression parameters under certain conditions.
Bayesian DL model improves DCMs for better predictive and inferential performance.
problem Limited interpretability and predictive underperformance of traditional DCMs.
method Integrates deep learning with approximate Bayesian inference (SGLD).
result Improves predictive and inferential metrics in discrete choice models.
This paper explores how the generalization of substitute classifiers affects the success of black-box adversarial attacks.
problem Understanding the factors driving the transferability of black-box adversarial examples.
method Max-min adversarial example game framework and theoretical generalization bounds.
result Substitute NN with better generalization behavior results in more transferable adversarial examples.
We introduce Markov substitute processes, a new model at the crossroad of statistics and formal grammars, and prove its main property : Markov substitute processes with a given support form an exponential family.
Study generalizes Lebesgue curves to new space-filling and fractal sets.
problem Generating space-filling curves from planar substitutions.
method Generalized Lebesgue's construction to new curves and fractal sets.
result Some substitutions create relatively dense fractal-like sets.
The definition of deposit substitutes in Philippine tax law fails to consider the maturity of a debt instrument. This makes it possible for long-term bonds to be considered as deposit substitutes if they meet the 20-lender rule, taxable at 20% final tax. However, long-term debt instruments cannot realistically function…
ENN uses covariance matrices for gradient-free optimization in neural networks.
problem Efficiently optimizing neural networks without gradient calculations.
method Ensemble Neural Networks (ENN) with EnRML algorithm.
result ENN outperforms traditional BNNs and is robust to small datasets.
Meta-model framework improves efficiency in parameter estimation of dynamical systems.
problem Efficiently estimating parameters of complex dynamical systems with computationally expensive objective functions.
method Dynamic adaptation of surrogate model and substitution strategy using a meta-model framework.
result Significant improvement in optimization efficiency, reducing evaluations by up to 77%.
A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages range from guarantees for statistical generalization to better accuracy to compression. But support for general constraints within widely used …
An efficient algorithm for k-median clustering in a sequential setting without substitutions.
problem Clustering a sequence of examples without being able to substitute centers later.
method An efficient algorithm with a multiplicative approximation factor of twice the offline algorithm's factor, and an optimal offline algorithm.
result The efficient algorithm achieves a good approximation of the optimal offline solution.
Computed tomography (CT) equivalent information is needed for attenuation correction in PET imaging and for dose planning in radiotherapy. Prior work has shown that Gaussian mixture models can be used to generate a substitute CT (s-CT) image from a specific set of MRI modalities. This work introduces a more flexible cl…
Geometric theory explains substitutability in market outcomes based on production constraints.
problem Understanding substitutability in markets with structured feasible products.
method Modeling the set of feasible products as a compact Riemannian manifold to study intrinsic geometry and its effects on substitutability.
result Intrinsic geometry of the feasible set governs substitutability and market outcomes, with curvature controlling technological substitution elasticity.
In this paper, a time substitution as used by Duru and Kleinert in their treatment of the hydrogen atom with path integrals is performed to price timer options under stochastic volatility models. We present general pricing formulas for both the perpetual timer call options and the finite time-horizon timer call options…
This work addresses the situation where a black-box model with good predictive performance is chosen over its interpretable competitors, and we show interpretability is still achievable in this case. Our solution is to find an interpretable substitute on a subset of data where the black-box model is overkill or nearly …
In a market with stochastic investment opportunities, we study an optimal consumption investment problem for an agent with recursive utility of Epstein-Zin type. Focusing on the empirically relevant specification where both risk aversion and elasticity of intertemporal substitution are in excess of one, we characterize…
Proposes deterministic autoencoders simpler than VAEs.
problem Theoretical and practical challenges in VAEs.
method Interprets stochasticity in VAEs as noise, applies regularization, introduces ex-post density estimation.
result Deterministic autoencoders generate comparable or better samples than VAEs.
Adaptive methods sometimes outperform SGD in over-parameterized DNNs.
problem Generalization in over-parameterized DNNs with adaptive methods.
method Comparing SGD and adaptive methods on over-parameterized linear regression.
result Minimum weight norm is not always the best indicator of good generalization.
New algorithm for clustering data streams with no substitutions.
problem Clustering sequential data with no point substitution.
method Careful risk estimation, linear bin division, multiscale center selection.
result First constant approximation algorithm for random-order k-median clustering.
This work improves neural network robustness to symbol substitutions using formal verification.
problem Neural networks' vulnerability to adversarial attacks, especially under discrete text perturbations.
method Formal verification using Interval Bound Propagation on a simplex model of input perturbations.
result Models show improved verified accuracy under perturbations with formal guarantees.
Defect of knot polynomials remains invariant under certain braid substitutions.
problem Invariance of knot polynomial defects under specific transformations.
method Investigation of defect invariants under antiparallel and parallel braid substitutions.
result Defect remains unchanged under antiparallel braid substitutions and changes by half the added length under parallel braid substitutions.
This paper investigates and compares currency substitution between the currencies of Central and Eastern European (CEE) countries and the euro. In addition, we develop a model with microeconomic foundations, which identifies difference between currency substitution and money demand sensitivity to exchange rate variatio…
Augment small datasets with synthetic backgrounds to train lightweight CNNs for human pose estimation.
problem Training CNNs from limited real-world data for human pose estimation.
method Synthetic background substitution for data augmentation.
result Improves generalization to unseen environments.
We study continuous time Bertrand oligopolies in which a small number of firms producing similar goods compete with one another by setting prices. We first analyze a static version of this game in order to better understand the strategies played in the dynamic setting. Within the static game, we characterize the Nash e…
New method extracts stochastic laws from data, including Lévy noise.
problem Extracting stochastic laws from data with non-Gaussian noise.
method Using normalizing flows to estimate transition density, then applying nonlocal Kramers-Moyal formulas.
result Can learn stochastic differential equations with Lévy motion.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
Suppose that f and g are Markov surjections, each defined on a wedge of circles, each fixing the branch point and having the branch point as the only critical value. We show that if the points in the inverse limit spaces associated with f and g corresponding to the branch point are distinguished then these inverse limi…
The paper optimizes financial derivatives for market completion in SV models.
problem Optimizing financial derivatives for market completion in stochastic volatility models.
method Simulation-based method to approximate optimal portfolio strategy, using double optimization approach (utility maximization and risk exposure minimization).
result Strangle options are the best choices for market completion in equity options.
Network models assume unrealistic idiosyncratic risk, which can be mitigated by allowing for correlated shocks.
problem Network models assume idiosyncratic risk, which can be unrealistic and lead to incorrect predictions.
method Proposed a production-based asset pricing model to account for substitutability between trade partners and correlation in supply and demand shocks.
result Assets positively exposed to average propagation of upstream and downstream shocks earn lower average risk premia.
Develops methods to improve demand counterfactuals from imperfect proxies.
problem Imperfect proxies in demand models lead to biased counterfactuals and invalid inference.
method Practical toolkit for market-level and individual data, requiring minimal computation.
result Improves substitution prediction and counterfactual performance.
This work addresses time inconsistency in risk measures and develops a dynamic programming principle for risk minimization problems.
problem Time inconsistency in optimized certainty equivalents (OCEs) risk measures.
method Enlargement of state space to achieve a substitute for time consistency, derivation of dynamic programming principle.
result Characterization of the value function via viscosity solutions of Hamilton--Jacobi--Bellman--Issacs equations.
In certain situations that shall be undoubtedly more and more common in the Big Data era, the datasets available are so massive that computing statistics over the full sample is hardly feasible, if not unfeasible. A natural approach in this context consists in using survey schemes and substituting the "full data" stati…
Flexible Kernels for Protein Property Prediction
problem Predicting protein properties from sparse experimental data
method Sequence kernels using evolutionary substitution matrices and local linearity
result Data-efficient models of protein property landscapes
Integrated Assessment Models (IAMs) are mainstay tools for assessing the long-term interactions between climate and the economy and for deriving optimal policy responses in the form of carbon prices. IAMs have been criticized for controversial discount rate assumptions, arbitrary climate damage functions, and the inade…
We analyze the practical consequences of the bilateral counterparty risk adjustment. We point out that past literature assumes that, at the moment of the first default, a risk-free closeout amount will be used. We argue that the legal (ISDA) documentation suggests in many points that a substitution closeout should be u…
Solves consumption-investment problem with random horizon under Epstein-Zin preferences.
problem Maximizing consumption and investment under random time horizons with Epstein-Zin utility.
method Backward stochastic differential equations with superlinear growth on unbounded random horizons.
result Optimal strategies differ significantly when moving from fixed to random time horizons.
This paper benchmarks UNSW-NB15 as a modern alternative to KDD-99 for A-NIDS.
problem Skewed response distribution and non-stationarity in KDD-99 hamper A-NIDS performance.
method Compared UNSW-NB15 to KDD-99 and NSL-KDD using various classification models and SMOTE oversampling.
result UNSW-NB15 outperforms KDD-99 and NSL-KDD in minority class performance, suggesting it as a better benchmark.