Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based e…
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48 results for “non-additive models”
We proposed a new statistical dependency measure called Copula Dependency Coefficient(CDC) for two sets of variables based on copula. It is robust to outliers, easy to implement, powerful and appropriate to high-dimensional variables. These properties are important in many applications. Experimental results show that C…
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We present new online algorithms for path learning with non-additive count-based gains for the three settings of full information, semi-bandit and…
Diamond method controls FDR for trustworthy feature interaction discovery in ML models.
problem Limited interpretability of ML models due to black box nature.
method Diamond method integrates model-X knockoffs framework to control FDR for non-additive interactions.
result Diamond method ensures accurate discovery of feature interactions with FDR control.
We consider online optimization in the 1-lookahead setting, where the objective does not decompose additively over the rounds of the online game. The resulting formulation enables us to deal with non-stationary and/or long-term constraints , which arise, for example, in online display advertising problems. We propose a…
The paper investigates the limitations of additive explanations for complex models.
problem The trustworthiness of additive explanations for non-additive models.
method Examine and introduce a new method to detect interactions for instance-level explanations.
result Additive explanations can be misleading for non-additive models.
New risk measure considers horizon risk and interest rate uncertainty.
problem Dynamic risk evaluation considering horizon risk and interest rate uncertainty.
method Introduced a risk measure based on generalized Tsallis entropy.
result New q-entropic risk measure quantifies capital requirement.
Econophysics has developed as a research field that applies the formalism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quantum Econophysics. In Finance, Quantum Econophysics' contrib…
This paper introduces new risk measures for evaluating losses with varying time horizons.
problem Capturing horizon risk and cash non-additivity in risk evaluation.
method Uses BSDEs and shortfall approaches to develop h-generalized shortfall risk measures.
result Introduces hq-entropic risk measures as a new family of fully-dynamic risk measures.
Defense against Wasserstein adversarial attacks using randomized smoothing.
problem Certified robustness against Wasserstein adversarial attacks.
method Randomized smoothing applied to the space of flows between images, bounding Wasserstein distance by L_1 distance.
result Significantly improved accuracy under Wasserstein adversarial attacks compared to unprotected models.
A well-known property of the signature of closed oriented 4n-dimensional manifolds is Novikov additivity, which states that if a manifold is split into two manifolds with boundary along an oriented smooth hypersurface, then the signature of the original manifold equals the sum of the signatures of the resulting manifol…
Causal Mosaic distinguishes cause from effect using nonlinear ICA and ensemble methods.
problem Distinguishing cause from effect in bivariate settings.
method Nonlinear ICA and ensemble framework (Causal Mosaic).
result Causal Mosaic shows state-of-the-art performance on artificial and real-world datasets.
The paper analyzes insurance pricing and capital allocation in imperfect markets.
problem Analyzing insurance pricing and capital allocation in imperfect markets.
method Non-additive distortion pricing functional and principle of equal priority of payments in default.
result Derives the natural allocation of premium and margin with properties that merit the name.
We discuss multi-task online learning when a decision maker has to deal simultaneously with M tasks. The tasks are related, which is modeled by imposing that the M-tuple of actions taken by the decision maker needs to satisfy certain constraints. We give natural examples of such restrictions and then discuss a general …
Several proofs have been published of the Mod Z gluing formula for the eta-invariant of a Dirac operator. However, so far the integer contribution to the gluing formula for the eta-invariant is left obscure in the literature. In this article we present a gluing formula for the eta-invariant which expresses the integer …
Greedy MI maximization method outperforms existing approaches in nonlinear models.
problem Maximizing mutual information in nonlinear models with non-Gaussian noise.
method Greedy approaches based on log-Sobolev inequalities for computationally inexpensive MI lower bounds.
result Proposed method outperforms random selection and Gaussian approximations.
Paper proposes OPF policy for fair resource allocation with sublinear regret.
problem Fair resource allocation in an online setting against an unrestricted adversary.
method Online Proportional Fair (OPF) policy achieving approximate sublinear regret.
result OPF policy achieves -approximate sublinear regret with .
SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.
problem Non-additivity of hazard and survival functions limits standard additive explanation methods.
method SurvFD decomposes higher-order effects into time-dependent and time-independent components, extending Shapley interactions to time-indexed functions.
result SurvFD and SurvSHAP-IQ offer a new perspective on survival explanations, explicitly characterizing feature interactions.
We present for the first time an asymptotic convergence analysis of two time-scale stochastic approximation driven by `controlled' Markov noise. In particular, both the faster and slower recursions have non-additive controlled Markov noise components in addition to martingale difference noise. We analyze the asymptotic…
Using theorems of Eliashberg and McDuff, Etnyre [Et] proved that the intersection form of a symplectic filling of a contact 3-manifold supported by planar open book is negative definite. In this paper, we prove a signature formula for allowable Lefschetz fibrations over with planar fiber by computing Maslov index…
Characteristic Function of the Tsallis -Gaussian and Its Applications in Measurement and Metrologystat.CO
Paper presents characteristic function of Tsallis q-Gaussian and its applications.
problem Modeling input quantities in measurement models using Tsallis q-Gaussians.
method Developed a characteristic function and proposed a numerical method for its inversion.
result Exact probability distribution of output quantities can be determined.
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a…
We study online learnability of a wide class of problems, extending the results of (Rakhlin, Sridharan, Tewari, 2010) to general notions of performance measure well beyond external regret. Our framework simultaneously captures such well-known notions as internal and general Phi-regret, learning with non-additive global…
Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.
problem Analyzing how trading frictions impact cap-and-trade market effectiveness.
method Developed a dynamic stochastic model with multiple trading frictions, characterized access choices in closed form, and quantified using EU ETS data.
result Trading frictions interact to amplify or dampen market responses, and their combined effect is non-additive.
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Baye…
Deep neural networks correct Mie scattering in FTIR spectra of biological samples.
problem Mie scattering obscures biochemically relevant spectral information in FTIR spectra of biological samples.
method Deep neural networks to approximate the preprocessing function that removes Mie scattering.
result The model is faster and more generalizable across different tissue types.
This paper improves neural network explanations by quantifying and visualizing semantic compositions.
problem Improving neural network explanations for natural language processing tasks.
method Proposes a formal way to quantify word and phrase importance, introduces SCD and SOC algorithms.
result Our algorithms outperform prior methods in explaining neural network predictions.
High dimensional nonparametric regression is an inherently difficult problem with known lower bounds depending exponentially in dimension. A popular strategy to alleviate this curse of dimensionality has been to use additive models of \emph{first order}, which model the regression function as a sum of independent funct…
Reformulates Ozbagci's algorithm for Lefschetz fibrations signatures.
problem Computing signatures of Lefschetz fibrations efficiently.
method Partial fiber sum decomposition and Wall's non-additivity formula.
result Algorithm simplification and new formula for signature computation.
Research combines econometric, machine learning, and deep learning models for financial forecasting.
problem Improving financial time series forecasting accuracy.
method Hybrid models combining ARIMA, SVM, XGBoost, and LSTM.
result Effective hybrid models outperform individual components and the Buy&Hold strategy.
New bounds and examples for sphere unknotting numbers.
problem Comparing unknotting numbers for 2-spheres in 4-space.
method Algebraic and geometric techniques.
result Stabilization number is bounded above by one more than Casson-Whitney number.
Proposes qPO, a new acquisition strategy for batched Bayesian optimization that maximizes the probability of including the optimum.
problem Efficiently identifying top-performing compounds from a large chemical library.
method qPO (multipoint Probability of Optimality) acquisition strategy that maximizes the probability of including the true optimum.
result Empirical evidence shows that qPO is competitive with and complements other state-of-the-art methods in batched Bayesian optimization.
Framework for transitioning financial models from risk-neutral to real-world measure.
problem Transitioning financial models from risk-neutral to real-world measure to better reflect market dynamics and investor preferences.
method Leveraging probability theory, specifically Girsanov's theorem, to incorporate real-world dynamics into financial models.
result Validation of the robustness and practical relevance of the methodology through case studies involving financial forecasts and stress tests.
MARS via LASSOmath.ST
Proposes a lasso variant of MARS for nonparametric regression.
problem Nonparametric regression with MARS.
method Least squares estimation over convex function combinations with a complexity constraint.
result Achieves logarithmic convergence rate in dimensionality.
A new method ranks uncertainty vectors from multiple measures for robust prediction.
problem Single scalar measures of model reliability are insufficient for comprehensive uncertainty quantification.
method Optimal transport ranks vectors of uncertainty measures, supporting flexible fusion of aleatoric and epistemic uncertainties.
result The method provides a robust ranking of uncertainty that supports various downstream tasks.
MISS identifies influential subsets in ML models.
problem Capturing collective influence of training samples.
method Analyzed and compared influence-based greedy heuristics and adaptive versions.
result Adaptive heuristics can better capture sample interactions.
Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Setscs.LG
Neural network fusion reduces data acquisition costs for multi-fidelity sources.
problem Reducing cost in acquiring information from multiple data sources with varying fidelity.
method Employing a novel neural network architecture for nonlinear manifold learning of multi-fidelity data.
result Our approach provides high predictive power and quantifies various sources uncertainties.
Geometric quantization extended to arbitrary connected spaces using path integration.
problem Constructing a Prequantum Groupoid for arbitrary connected parasymplectic spaces.
method Define a Total Group of Periods and a Prequantum Groupoid with connected isotropy.
result The Prequantum Groupoid is isomorphic to the group of symmetries of the Dynamical System.
Framework uses diffusion models to infer material properties from noisy mechanical measurements.
problem Inference of spatially varying material properties from noisy mechanical responses.
method Conditional score-based diffusion models approximating the score function of a conditional distribution.
result Framework can efficiently solve large-scale physics-based inverse problems.
Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure. Such measures have spurred much interest and pose specific chall…
Study uses RL to optimize risky vs. risk-free asset allocation.
problem Optimal asset allocation in volatile financial markets.
method Formulated as MDP, uses DDPG with TiDE for dynamic policy learning.
result DDPG-TiDE outperforms Q-learning and buy-and-hold strategies.
When recovering an unknown signal from noisy measurements, the computational difficulty of performing optimal Bayesian MMSE (minimum mean squared error) inference often necessitates the use of maximum a posteriori (MAP) inference, a special case of regularized M-estimation, as a surrogate. However, MAP is suboptimal in…
New KNN test improves association analysis of high-dimensional sequencing data.
problem Challenges in using neural networks for high-dimensional sequencing data analysis.
method Kernel-based neural network (KNN) test for complex association analysis.
result KNN test outperforms SKAT in detecting non-linear and interaction effects.
ENN method uses expectile regression for genetic data analysis of complex diseases.
problem Discover additional genetic variants contributing to complex diseases.
method Developed an expectile neural network (ENN) method integrating expectile regression and neural networks.
result ENN method outperforms existing expectile regression in discovering genetic variants predisposing to sub-populations.
The study examines how permutation-based optimization performance varies across different function representations.
problem Understanding how the order of function evaluations affects optimization performance.
method Iterative search setting with sampling without replacement, algebraic function recombination, correlation analysis, hierarchical clustering, PCA, ANOVA.
result Algebraically modified benchmarks yield stable re-rankings and coherent clusters of functions and sampling policies, indicating non-additive search effort.
This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.
problem Risk allocation in financial institutions with non-additive risk measures and layered structures.
method Systematic review of risk allocation strategies, testing in simplified and realistic settings, including Basel 2.5 and FRTB.
result Shapley allocation offers the best compromise between simplicity, mathematical properties, and computational cost.
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learningcs.LG
We analyze stochastic approximation with Markov noise for reinforcement learning.
problem Asymptotic convergence analysis of stochastic approximation with controlled Markov noise.
method Relating to limiting differential inclusions and ergodic occupation measures.
result First informative error bounds on function approximation for policy evaluation.
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.