New methods test discrete distributions faster with local privacy constraints.
problem Testing discrete distributions under local differential privacy constraints.
method Efficient randomized algorithms and test procedures, both non-interactive and interactive.
result Faster separation rates in interactive privacy mechanisms.
In this paper we study dynamic pricing mechanisms of financial derivatives. A typical model of such pricing mechanism is the so-called g--expectation defined by solutions of a backward stochastic differential equation with g as its generating function. Black-Scholes pricing model is a special linear case of this pricin…
This paper considers control systems defined on Lie algebroids. After deriving basic controllability tests for general control systems, we specialize our discussion to the class of mechanical control systems on Lie algebroids. This class of systems includes mechanical systems subject to holonomic and nonholonomic const…
Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon's Mechanical Turk (MTurk) to make MT test sets chea…
In this paper we study dynamic pricing mechanism of contingent claims. A typical model of such pricing mechanism is the so-called g-expectation Es,tg[X] defined by the solution of the backward stochastic differential equation with generator g and with the contingent claim X as terminal condition. The generating f…
Tests whether a treatment's effect is fully mediated by observed outcomes and identifies causal mechanisms.
problem Understanding how a treatment affects an outcome through intermediate variables.
method Proposes a test to evaluate full mediation and causal mechanism identification, extending to non-randomly assigned treatments.
result A conditionally random treatment is conditionally independent of the outcome given mediators and covariates if full mediation and causal mechanism identification hold.
New insights into SGD and generalization via shift-curvature and bias-curvature mechanisms.
problem Understanding the role of curvature in generalization and how SGD affects it.
method Derivation of new SGD steady-state distribution and analysis of shift-curvature and bias-curvature mechanisms.
result Shift-curvature is a significant factor in test performance, especially for small SGD noise.
Study investigates OOD generalization methods for mechanics problems.
problem Real-world mechanics problems with unknown test environments and data distribution shifts.
method Investigates OOD generalization methods for regression problems in mechanics.
result OOD generalization methods perform better than traditional ML methods on mechanics-specific regression problems.
A new framework for private Bayesian tests maintains interpretability and computational efficiency.
problem Lack of interpretability and inability to quantify evidence in confidential data.
method Differentially private Bayesian tests based on test statistics.
result Established results on Bayes factor consistency under the proposed framework.
Decentralized mechanism for collective predictions without sharing data or models.
problem Making predictions jointly among multiple parties without sharing data or models.
method Inspired by social science consensus-making, a decentralized mechanism for test-time collective predictions.
result Our mechanism converges to inverse meansquared-error weighting in the large-sample limit and achieves significant gains over classical model averaging.
Proposes PEMI for online selective conformal prediction with asymmetric rules.
problem Challenges of handling asymmetric selection mechanisms in online selective conformal prediction.
method PEMI: permutation-based framework for selective conformal prediction with arbitrary asymmetric selection rules.
result Achieves exact selection-conditional coverage for any asymmetric selection mechanism and any prediction model.
Study proposes a statistical test for Vision Transformer's attention mechanisms.
problem ViT's attention mechanisms may focus on irrelevant regions, leading to unreliable evidence.
method Selective inference framework to quantify statistical significance of attentions as p-values.
result Proposed method enables reliable quantification of false positive detection probability of attentions.
Private two-sample tests under LDP achieve minimax rates for multinomial and continuous data.
problem Achieving statistical utility while maintaining privacy in two-sample testing.
method Private permutation tests for multinomial data and adaptive tests for continuous data.
result Minimax optimal tests for private two-sample testing under LDP.
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.
DJIA is tested whether it can be described as a mechanical system conserving total energy with K (=v*v/2) + U, where U is calculated as the negative of work done by force obtained in terms of the second derivative of price, assuming unit mass.
Estimates peeking effects in p-values to correct bias.
problem Data peeking biases reported p-values downward.
method Develops mechanisms to estimate running extrema of test statistics.
result Corrects bias in p-values due to peeking.
We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model …
LLMs detect market patterns through causal reasoning, not just temporal association.
problem Detecting structural market patterns in financial data.
method Obfuscation testing using the WHO-WHOM-WHAT framework.
result LLMs achieve 71.5% detection rate of market patterns without temporal context.
New method reveals good classifiers are common in over-parameterized models.
problem Understanding how over-parameterized models generalize well.
method Developed a methodology to compute the full distribution of test errors.
result Test errors concentrate around a small typical value ε* rather than the worst-case model.
Framework for efficient statistical estimation with privacy guarantees.
problem Statistical estimation problems with differential privacy constraints.
method High-dimensional Propose-Test-Release (HPTR) framework combining exponential mechanism, robust statistics, and resilience.
result Near-optimal utility guarantees and tight local sensitivity bounds for various statistical problems.
New method for estimating median and mean with high probability privacy.
problem Estimating median and mean with differential privacy.
method Propose, Test, Release (PTR) mechanism with concentration inequalities.
result First sub-Gaussian high probability bounds for differentially private median and mean estimation.
Test verifies if data meets SCAR assumption for PU learning.
problem Verify if labeling mechanism follows SCAR assumption in PU learning.
method Generate artificial labels, mimic distribution of test statistic.
result Test detects deviations from SCAR and controls type I error.
A new method tests independence for causal discovery on discrete data.
problem Inferring causal directions on discrete and categorical data.
method Subsampling-based method to test independence between cause and mechanism.
result Our method works for both discrete and categorical data without functional model assumptions.
A game-theoretic approach selects features by testing their marginal contributions.
problem Feature selection in econometric and statistical models.
method A coalitional game where features are players and payoff is model performance. Hypothesis test decides feature relevance.
result The approach significantly outperforms existing methods in simulations.
Decision Machines embeds decision trees into vector spaces for improved optimization.
problem Overfitting and difficulty in finding optimal decision tree structure.
method Embedding Boolean tests into a binary vector space and representing tree structure as matrices.
result Optimized decision trees with enhanced predictive power.
Paper tackles informative labels in semi-supervised learning, proposing debiasing methods.
problem Informative labels can bias semi-supervised learning models, especially when some classes are more likely to be labeled.
method Estimates missing-data mechanism and uses inverse propensity weighting to debias SSL algorithms.
result Proposed methods improve SSL performance, demonstrated on various datasets including medical ones.
An example of mechanical system whose configuration space is direct product of a curved space and the local group of rotations, is presented. The system is considered as a model of spinning particle moving in the space. The Hamiltonian formalism for this system and possible method for its quantization are discussed. It…
DP-SPRT improves privacy in sequential tests with near-optimal error rates.
problem Privacy constraints in sequential probability ratio tests.
method A wrapper for SPRT that uses a private mechanism to determine when to stop based on predefined intervals.
result DP-SPRT achieves near-optimal error rates and privacy guarantees.
Estimates network causal effects considering contagion and latent confounding.
problem Determining if correlations in network studies are due to contagion or latent confounding.
method Segregated graph representation, likelihood ratio tests, network causal effect estimation strategies.
result Proposes methods to estimate network causal effects under full interference scenarios.
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
problem Dynamic distribution shifts challenge real-world machine learning systems' risk assurances.
method Sequential hypothesis testing with 'testing by betting' to detect risk violations.
result Effective real-time risk monitoring under various unknown shifts.
Analyzes the concept of fields in classical and quantum physics.
problem Challenges in defining fields in classical and quantum physics.
method Uses groupoid description of quantum mechanics and categorical language.
result Fields as functors among groupoids of test particles and intrinsic system nature.
Attention mechanism learns to focus on sparse tokens efficiently.
problem Detecting weak, rare, and sparsely located features in long sequences.
method Theoretical analysis and training of a single-layer attention classifier in a sparse-token classification model.
result A single-layer attention classifier can achieve vanishing test error with logarithmic signal strength growth, unlike linear classifiers requiring linear growth.
Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from …
We document a mechanism operating in complex adaptive systems leading to dynamical pockets of predictability (``prediction days''), in which agents collectively take predetermined courses of action, transiently decoupled from past history. We demonstrate and test it out-of-sample on synthetic minority and majority game…
Near-optimal private tests for simple and MLR hypotheses developed under Gaussian differential privacy.
problem Developing private tests for simple and MLR hypotheses under Gaussian differential privacy.
method A private mean estimator with data-driven clamping bounds, constructing private test statistics.
result Private tests achieve the same asymptotic relative efficiency as non-private most powerful tests.
Few-shot learning improves time-series forecasting with limited data.
problem Limited data in target tasks degrade forecasting performance.
method A few-shot learning method using recurrent neural networks with attention.
result The model forecasts future values effectively with minimal data.
Paper proposes LMM-PQS for cross-domain few-shot learning.
problem Cross-domain few-shot learning problem.
method Generates pseudo query images and fine-tunes feature extraction modules with a large margin mechanism.
result LMM-PQS outperforms baseline models in cross-domain few-shot learning.
Physics-informed GANs estimate elastic moduli from mechanical tests.
problem Estimating spatially-varying elastic moduli from measured deformations.
method Physics-informed Generative Adversarial Networks (PI-GANs) with PDE constraints.
result Generated stiffness samples match true distribution statistics.
While research on adversarial examples in machine learning for images has been prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work, with only one recent publication in the RF domain [1]. RF adversarial examples (Ad…
Analyzes why neural networks generalize beyond training data.
problem Understanding why neural networks generalize beyond training data.
method Examined loss landscapes of neural networks to identify the mismatch between training and test losses.
result Identified the 'LU mechanism' explaining grokking in various tasks.
Quantum algorithm samples from SDEs using DQCs and quantile mechanics.
problem Sampling from solutions of stochastic differential equations.
method Differentiable quantum circuits (DQCs) encoding latent variables, quantile mechanics.
result Quantum algorithm generates time-series from SDEs.
Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appealing to statistician and social scientists is by means of its statistical hypothesis testing interpretation. Informally, one cannot effectiv…
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
problem Macro-financial stress testing and portfolio risk assessment using traditional methods.
method Hybrid prompt-RAG pipeline combining structured prompting and retrieval of country fundamentals and news.
result LLM-generated scenarios yield stable tail-risk amplification with limited sensitivity to retrieval choices.
A neural network predicts drug interactions using attention mechanisms.
problem Predicting drug-drug interactions from massive combinations of drugs.
method Siamese self-attention multi-modal neural network integrating drug characteristics.
result The model achieves AUPR scores ranging from 0.77 to 0.92 on various benchmark datasets.
GPT learns a causal world model from token predictions, validated in game sequences.
problem Does GPT implicitly learn a causal world model from token predictions?
method Derived a causal interpretation of GPT's attention mechanism and proposed zero-shot causal structure learning.
result GPT can generate legal next moves with high confidence for sequences with encoded causal structures, but fails for illegal moves.
Mamba efficiently learns low-dimensional targets in-context via feature extraction.
problem Learning low-dimensional targets in context for computational efficiency.
method Test-time feature learning of a single-index model using Mamba's pretrained linear-time sequence model.
result Mamba achieves efficient in-context learning of low-dimensional targets via feature extraction.
A modification of the confidence screening mechanism based on adaptive weighing of every training instance at each cascade level of the Deep Forest is proposed. The idea underlying the modification is very simple and stems from the confidence screening mechanism idea proposed by Pang et al. to simplify the Deep Forest …
TNP-KR improves scalability of NPs with Transformer blocks and attention mechanisms.
problem Scalability bottleneck in NPs and GPs, especially for large datasets.
method Introduces TNP-KR with KRBlock, kernel-based attention, and two attention mechanisms.
result TNP-KR with DKA outperforms Performer and achieves state-of-the-art results.