Selection mechanisms impact market volatility in evolving markets.
problem Determining how selection mechanisms affect market volatility in evolving markets.
method Used a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism to analyze the role of selection mechanisms.
result Local fitness-proportionate selection mechanisms correlate with high correlation between risk-aversion and volatility, while quantile-based selection mechanisms show less correlation.
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.
We prove optimal mechanisms for general contract spaces.
problem Optimal mechanism design under adverse selection and ambiguity.
method Existence proof for optimal mechanisms in general contract spaces.
result Centralized contracting is equivalent to delegated contracting.
A new mechanism reduces expert belief regret in online forecasting.
problem Minimizing expert belief regret in strategic forecasting.
method Developed a no-regret mechanism for non-myopic experts using online I-ELF.
result Achieved i l d e O ( T N ) ilde{O}(\sqrt{T N}) i l d e O ( T N ) regret for full-information setting. A new model shows fairness mechanisms can improve selection utility even without implicit bias.
problem Improving selection fairness without introducing a utility trade-off.
method A model with latent quality and group-dependent variance, comparing fairness mechanisms to group-oblivious selection.
result Demographic parity always increases selection utility, while γ γ γ -rules weakly increase it. Proposes a privacy-preserving sign selection method for distributed systems.
problem Sign selection in distributed differentially private settings.
method Iterative peeling of stability function combined with exponential mechanism.
result Recovery of support and signs with optimal signal-to-noise ratio.
Paper introduces efficient top-k selection with differential privacy.
problem Efficiently selecting top-k elements with differential privacy.
method Oneshot Laplace mechanism, generalizing Report Noisy Max.
result Noise level of O(sqrt(k)/eps) for approximate differential privacy.
Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more obviously positive. This paper investigates how learning can be ena BHbled in this setting. We propose and theoretically analyze an empirica…
New algorithm maintains privacy while improving model performance in selective release.
problem Privacy degradation and slow convergence in DPSGD.
method Differentially Private Selective Release based on Clipped Gradients (DPSR-CG).
result Maintains strict privacy guarantees while achieving exceptional model performance.
A novel incentive mechanism improves fairness and participation in federated learning.
problem Low-quality clients and lack of fairness in federated learning.
method Client selection process and money transfer mechanism to ensure fairness and participation.
result The proposed incentive mechanism improves the duration and fairness of federated learning.
Extracts important peaks from XRD spectra using Attention mechanism.
problem Identifying significant peaks in XRD patterns for material properties.
method Convolutional neural network with Attention mechanism to analyze deep features.
result Selected lattice constant predicts cathodic material cell voltage.
Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such mechanism could be implemented and affect the learning process of deep reinforcement learning. We visualize and analyze the feature maps of…
Paper introduces DP methods for high-dimensional variable selection.
problem Sparse variable selection in high-dimensional learning.
method Pure differentially private estimators using Integer Programming.
result Achieves state-of-the-art empirical support recovery.
The paper analyzes ℓ 1 \ell_1 ℓ 1 -LinR for Ising model selection using statistical mechanics.
problem Model selection consistency of ℓ 1 \ell_1 ℓ 1 -LinR for Ising models. method Replica method from statistical mechanics, ℓ 1 \ell_1 ℓ 1 -regularized linear regression ( ℓ 1 \ell_1 ℓ 1 -LinR). result Model selection consistency with sample complexity $M=\mathcal{O}\left(\log N
ight)$ .
FedACS uses attention to select clients with similar data for federated learning.
problem Non-IID data and data scarcity in federated learning.
method FedACS integrates an attention mechanism to prioritize clients with similar data distributions.
result FedACS improves federated learning performance by addressing non-IID data and data scarcity.
A DP method selects best sparse models in high dimensions efficiently.
problem Model selection in high-dimensional sparse linear regression under privacy constraints.
method Differential privacy (DP) with exponential mechanism and Metropolis-Hastings algorithm.
result The method identifies active features quickly under privacy constraints.
LMoE uses LLMs to improve stock trading by selecting experts based on textual and price data.
problem Traditional neural network-based router selection in MoE models is suboptimal and ignores textual data.
method Proposes LLMoE, using LLMs as routers to select experts based on historical price data and stock news.
result LLoM outperforms state-of-the-art MoE models and other deep neural network approaches.
GA-Net selectively attends to parts of a sequence for text classification.
problem Inefficient global attention mechanisms on long sequences.
method Gated Attention Network (GA-Net) using an auxiliary network to dynamically select and attend to important parts of the sequence.
result GA-Net achieves better performance with less computation and interpretability.
Before retiring, looking back to forty years of writing and publishing scientific papers, I decided to present to the scientific community a selection of my scientific works. I chose mostly articles published in prestigious journals or Proceedings that made a certain impact in the scientific world. I have selected thir…
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
Validates conformal prediction for network data under non-uniform sampling.
problem Validity of conformal prediction for network data under non-representative sampling.
method Interprets sampling mechanisms as selection rules, studies validity conditional on selection events, uses permutation invariance and joint exchangeability.
result Finite-sample validity of conformal prediction for certain selection events and asymptotic validity for random walk sampling.
Improved selection of best outputs from LLMs for better accuracy.
problem BoN fails to reliably find the correct answer with imperfect rewards.
method Majority-of-the-Bests (MoB) selects the mode of a bootstrapped output distribution of BoN.
result MoB consistently improves over BoN in 25 out of 30 setups.
In this article, we advocate the ensemble approach for variable selection. We point out that the stochastic mechanism used to generate the variable-selection ensemble (VSE) must be picked with care. We construct a VSE using a stochastic stepwise algorithm, and compare its performance with numerous state-of-the-art algo…
STAS selects optimal spatio-temporal scales for bias correction in precipitation forecasts.
problem Limited prior data and fixed ST scale in existing BCoPs lead to biases in numerical weather predictions.
method End-to-end deep-learning BCoP model STAS with SFM/TFM to automatically adjust spatial and temporal scales.
result STAS outperforms 8 published BCoP methods on threat scores (TS).
A fast, approximate method for variable selection in GLMs tackles correlated data.
problem Variable selection in generalized linear models with correlated data.
method Replica method of statistical mechanics and vector approximate message passing.
result The proposed algorithm provides fast convergence and high approximation accuracy.
Proposes methods to correct bias and missing data in regression models.
problem Nonignorable selection bias and missing response in regression models.
method Imputation-based and importance weighted regression methods, including repeated regression and doubly robust combination.
result Repeated regression can effectively correct bias and outperforms weighted regression in extrapolation.
We find that CNNs with BN and ReLU exhibit feature sparsity through selective feature penalization.
problem Feature sparsity in CNNs trained with specific techniques.
method Empirical study and hypothesis testing on sparsification mechanisms.
result Selective feature penalization leads to feature sparsity in CNNs, comparable to explicit pruning.
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.
Paper introduces Isotonic Mechanism for better item scoring.
problem Noisy reviewer scores; owner prefers not to disclose true scores.
method Uses owner's ranking of items and raw scores to adjust and improve accuracy.
result Adjusted scores are significantly more accurate than raw scores.
GSANet improves semantic segmentation accuracy with selective and global attention.
problem Semantic segmentation accuracy improvement.
method Global and selective attention mechanism with ASPP and sparsemax.
result GSANet achieves state-of-the-art accuracy on ADE20k and Cityscapes datasets.
Private variable selection method controls FDR with simulations showing reasonable power.
problem Performing variable selection with privacy constraints.
method Private knockoff filter using Gaussian and Laplace mechanisms.
result Achieves controlled false discovery rate (FDR) in variable selection.
The study improves the perceptron's storage capacity by optimizing variable selection.
problem Distinguishing genuine structure from random correlations in high-dimensional data.
method Replica method from statistical mechanics for optimal variable selection.
result Optimal variable selection can surpass the Cover--Gardner bound for pattern classification.
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.
New method predicts model performance under selection bias in healthcare.
problem Selection bias limits model generalizability in healthcare.
method Proposes a novel upper bound method for estimating model performance.
result Validates and demonstrates the practical utility of the method.
An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in the aggregation process. A problem with this approach is that poor components are likely to negativel…
This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images. The proposed approach re-uses convolutional activations at different depths, identifying the most informative regions of the spectrum with the help of gating mechanisms. Our attention techniques are mo…
This paper solves optimal market making for multiple goods, including bundling, under adverse selection.
problem Designing optimal market making mechanisms for multiple goods and adverse selection.
method Formulated as an optimal transport problem with geometric constraints, using differentiable economics.
result Optimal market making mechanisms can exploit bundling to improve prices and accept payments in kind.
Transformers can adaptively select and perform various machine learning tasks in context.
problem Transformers' ability to perform multiple machine learning tasks without explicit prompting.
method Statistical theory and efficient implementation of in-context gradient descent, enabling transformers to implement various algorithms and tasks.
result Transformers can implement a broad class of standard machine learning algorithms in context, including algorithm selection.
DMFAW improves multi-view clustering with adaptive weights and feature selection.
problem Lack of effective feature selection and empirical hyperparameter selection in existing deep matrix factorization methods.
method Introduces Deep Matrix Factorization with Adaptive Weights (DMFAW) for multi-view clustering, incorporating feature selection and dynamically updating weights using Control Theory.
result DMFAW outperforms state-of-the-art methods in clustering performance.
New method corrects selection bias in complex models.
problem Selection bias in statistical studies leading to systematic distortions.
method Amortized Bayesian inference with neural posterior estimation.
result Recover well-calibrated posterior distributions across diverse selection mechanisms.
Proposes MEED framework for model interpretation.
problem Improving model interpretability and avoiding undesired characteristics.
method Adversarial Infidelity Learning (AIL) for effective feature selection.
result AIL mechanism helps learn desired conditional distribution.
A new method selects features for clustering without labels.
problem Identifying meaningful features in large datasets.
method Differentiable unsupervised feature selection using a gated Laplacian.
result The method improves clustering performance in noisy data.
Minimal Morse functions on Poincaré dodecahedral space are selected via spectral properties.
problem Identifying minimal Morse functions on the Poincaré dodecahedral space.
method Spectral selection property P, obstruction principle, conformal variations, finite dimensional reduction.
result Restoration of minimal Morse selection on the Poincaré dodecahedral space via spectral mechanisms.
AFS uses attention to select features efficiently.
problem Efficiently selecting features from high-dimensional data.
method AFS combines an attention module and a learning module to address feature selection challenges.
result AFS outperforms state-of-the-art feature selection algorithms in accuracy and stability.
The paper introduces a new method for feature selection without explicit sparsification.
problem Feature selection with implicit sparsity-inducing mechanisms.
method Optimization over a family of kernels with gradient descent.
result The method achieves exact sparsity without explicit penalization techniques.
LLMs can select predictive features without seeing training data.
problem Selecting the best features for prediction tasks.
method Zero-shot prompting LLMs with feature names and task descriptions.
result LLMs consistently identify predictive features across various mechanisms.
Benchmarking 18 deep neural network selective classification models on various datasets.
problem Designing a model that can abstain from making predictions when there's high risk of error.
method Evaluation of 18 selective classification models using multiple criteria.
result No single model is the clear winner; best model depends on objectives.
LLMs can be influenced by unseen dataset subtexts, revealing new ways to select data subsets.
problem Understanding how datasets subtly influence LLMs and their properties.
method Logit-Linear-Selection (LLS) method to select subsets of datasets.
result LLS reveals hidden effects in LLMs that persist across different models and architectures.