We propose a principle and loss functions for efficient single-class classification.
problem Efficient binary classification for specific classes in high-dimensional data.
method Define Single Logit Classification (SLC) task, propose Principle of Logit Separation, and design loss functions.
result Loss functions aligned with the Principle of Logit Separation improve SLC accuracy by 20%.
Logit-Coordinate models improve representation of mixed data types.
problem Representation of mixed continuous-categorical data in generative models.
method Logit-coordinate framework combining categorical and continuous variables.
result Logit-Coordinate models outperform one-hot encoding in simulations and real data.
MANO normalizes logits to estimate test accuracy without labels.
problem Estimating test accuracy of OOD samples without labels.
method Applies L p L_p L p norm to normalized logits. result Achieves state-of-the-art performance across various architectures.
ELM improves neural model embeddings for long-tail learning.
problem Learning skewed label distributions in neural models.
method Enforces margins in logit space and regularizes embedding distribution.
result ELM reduces generalization gap and tightens tail class embeddings.
New method distills cloud models into edge-friendly ones.
problem Cloud-to-edge model compression with limited data exchange.
method Two-step workflow of deprivatization and distillation.
result Outperforms previous state-of-the-art approaches on various benchmarks.
New algorithm LUMB reduces regret for linear utility multinomial logit bandit.
problem Sequential subset selection with multinomial logit rewards.
method Proposes LUMB algorithm exploiting linear utility model.
result Achieves i l d e O ( d K T ) ilde{O}\big(dK\sqrt{T}\big) i l d e O ( d K T ) regret, independent of N N N . PASTA optimizes assortment selection using pessimism principle.
problem Optimizing assortment selection with limited data coverage.
method Pessimistic Assortment Optimization (PASTA) based on the principle of pessimism.
result PASTA correctly identifies optimal assortment with minimal data coverage.
Logit regularization induces logit clustering, affecting classifier performance.
problem Understanding the mechanism of logit regularization in classification.
method Analysis of logit regularization in linear classification, proving logit clustering leads to Fisher's Linear Discriminant alignment.
result Logit regularization can halve critical sample complexity and induce robust generalization.
Develops a power-calibrated framework for LLM watermarking, optimizing tradeoffs between detectability and distortion.
problem The trade-off between detectability and semantic distortion in logit-based watermarking.
method Power-calibrated statistical framework for watermark hyperparameters, establishing explicit relationships.
result Derives practical parameter selection procedures achieving optimal tradeoffs under constraints.
Improves adversarial robustness by constraining logits with a bounded function.
problem Improving adversarial robustness in deep learning models.
method Addition of a bounded function before softmax to constrain logits.
result Our method improves adversarial robustness without requiring adversarial training.
Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.
problem Training temperature-conditional GFlowNets is numerically challenging.
method Logit-GFN uses a learned function of temperature to scale policy logits.
result Logit-GFN greatly accelerates GFlowNets training and improves generalization and mode discovery.
A new logit model derived from the Weibull manifold.
problem No potential function on the Weibull manifold.
method Extracted a logit model from the two-parameter Weibull model.
result Found a completely integrable Hamiltonian gradient system on the logit model.
Proposes SOVR loss to improve adversarial robustness by increasing logit margins.
problem Adversarial training's difficulty in robustness against sophisticated attacks.
method Introduces SOVR loss function that switches from cross-entropy to one-vs-the-rest loss for important samples.
result SOVR loss increases logit margins of important samples, improving robustness against Auto-Attack.
BoC probe assesses neural network confidence coherence, revealing architecture-specific uncertainty.
problem Poor calibration and OOD detection in neural networks.
method Bag-of-Coins (BoC) probe compares softmax confidence to pairwise dominance probabilities.
result BoC reveals clear ID/OOD separation for some architectures but not others.
Improved defense against adversarial examples using logit pairing.
problem Defending against adversarial examples at scale.
method Adversarial training and logit pairing.
result Adversarial logit pairing achieves state of the art defense on ImageNet.
Logit pairing methods don't provide robustness against adversarial examples.
problem Improving adversarial robustness of classifiers.
method Logit regularization methods (CLP, LSQ, ALP) to craft adversarial examples.
result Adversarial Logit Pairing (ALP) may provide robustness, but the increase is small.
Adversarial Logit Pairing shows 0.6% accuracy in threat model.
problem Evaluating robustness of Adversarial Logit Pairing against adversarial examples.
method Evaluation of Adversarial Logit Pairing's robustness through accuracy testing.
result Adversarial Logit Pairing achieves 0.6% accuracy in threat model.
Generative classifier derived from any discriminative classifier rejects illegal inputs.
problem Detecting and rejecting illegal inputs like adversarial examples and out-of-distribution samples.
method SDIM-logit: learns generative classifier from logits of any discriminative classifier, imposing statistical constraints.
result SDIM-logit inherits performance of base classifier without loss and can reject illegal inputs.
This paper compares machine learning and logit models for predicting travel mode choice.
problem Comparing interpretability of machine learning and logit models for travel mode choice.
method Empirical evaluation of machine learning and logit models on stated preference survey data.
result Machine learning produces significantly higher predictive accuracy than logit models.
Unified kernel for prediction markets reduces belief variance forecast error.
problem Lack of standardized tools for quoting and hedging belief risk in prediction markets.
method Logit jump-diffusion model with risk-neutral drift, calibration pipeline, and coherent derivative layer.
result Model reduces forecast error compared to diffusion-only and probability-space baselines.
Logit dynamics formula reveals self-regulation in softmax policy gradient methods.
problem Understanding the stability and convergence of softmax policy gradient methods.
method Deriving the exact formula for the L2 norm of the logit update vector.
result Logit update magnitudes are modulated by action probability and policy concentration.
Generative model captures how individuals process travel information under uncertainty.
problem Travel decisions are sensitive to uncertainty and information processing constraints.
method Data-driven generative learning model based on rational inattention theory.
result Generative model demonstrates strong correlation with rational inattention theory.
Proposes a convex model for mixed logit to handle individual heterogeneity.
problem Non-convex optimization in mixed logit models for individual heterogeneity.
method Sparse and low-rank decomposition for convex formulation.
result Convex formulation avoids simulation-based approximation and unstable model interpretation.
New algorithm tackles non-linear utility in MNL bandits with i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret.
problem Sequential assortment selection with intricate user-item interactions.
method Upper Confidence Bound principle for non-linear parametric utility functions, including neural networks.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret bound for neural network-based utilities. New method uses low logit rank to simplify complex language models.
problem Understanding and learning from modern language models.
method Exploiting the low logit rank structure of language models for efficient learning.
result An efficient algorithm for learning low logit rank models from queries.
Logit distance bounds representational similarity of models.
problem Approximating linear similarity when distributions are close.
method Defined a logit distance and proved its relationship to representational dissimilarity.
result Logit distance bounds representational similarity, providing nontrivial control in practice.
New models improve choice prediction accuracy.
problem Model misspecifications in discrete choice models lead to limited predictability and biased estimates.
method Proposes a new approach to estimate choice models by dividing the systematic part into knowledge-driven and data-driven components, which learns a new representation from available variables.
result The new models (L-MNL and L-NL) outperform traditional models in predictive performance and parameter estimation.
LAWN normalizes logits to improve deep network adaptability and generalization.
problem Large logits and weights lead to overfitting in deep networks.
method Logit Attenuating Weight Normalization (LAWN) constrains weight norms in the final sub-network.
result LAWN improves generalization and adaptability of deep networks.
A new framework evaluates LLM calibration in open-ended QA.
problem Evaluating LLM calibration in open-ended QA settings.
method Sem-ECE framework: sampling answers, grouping by semantic classes, and using frequencies as confidence.
result Sem-ECE estimators are unbiased and Sem 2 _2 2 achieves smaller calibration error. A two-layer network corrects logits to defend adversarial attacks.
problem Detecting and defending adversarial attacks on deep learning models.
method A two-layer network trained on mixed logits to recover original predictions.
result Shows promising results in defense with high accuracy and interpretability.
New method bypasses statistical and classifier-based detection of adversarial examples.
problem Vulnerability of deep learning classifiers to adversarial examples.
method Classifier-based adaptation of statistical test method and Logit Mimicry Attack.
result Reduces detection performance to less than 2.2% TPR and 1.6% TPR for statistical test and classifier-based methods, respectively, even at 5% FPR.
New algorithm reduces switching costs in multinomial logit bandit problems.
problem Minimizing switching costs in multinomial logit bandit problems.
method Proposed AT-DUCB and FH-DUCB algorithms with low assortment switching costs.
result AT-DUCB and FH-DUCB algorithms achieve almost optimal minimax regret with low switching costs.
The paper provides guarantees for a tangent transform algorithm in logistic regression models.
problem Finding theoretical guarantees for statistical optimality and algorithmic convergence in non-conjugate models.
method Exploiting convex duality and minorizing the marginal likelihood, the paper derives non-asymptotic upper bounds and convergence guarantees for a tangent transform algorithm in logistic regression models.
result The tangent transform algorithm is shown to be locally asymptotically stable without assumptions on the data-generating process.
Paper tackles long-tailed labels in classification problems.
problem Imbalanced or long-tailed label distribution in real-world classification problems.
method Logit adjustment applied post-hoc or during training to encourage a large relative margin between rare and dominant labels.
result Unified and generalised techniques for coping with long-tailed labels, improving generalisation and performance.
Detects misclassifications and adversarial examples using neural network logits.
problem Unable to detect misclassifications and adversarial examples in neural networks.
method Introspection using pretrained neural network logits.
result Simple 3-layer neural network trained on logits detects misclassifications competitively.
The paper proposes an efficient method to scale Bayesian inference for mixed multinomial logit models to very large datasets.
problem Efficiency in Bayesian inference for mixed multinomial logit models on large datasets.
method Amortized Variational Inference with stochastic backpropagation, automatic differentiation, and GPU acceleration.
result The proposed method achieves significant computational speedups over traditional methods for large datasets.
Proposes a new model for context-dependent decision-making.
problem Constant preference parameters in decision models are too rigid.
method Introduces Context-aware Bayesian mixed multinomial logit model using neural networks.
result Models context-dependent intra-respondent heterogeneity effectively.
The paper proposes logit regularization for improving neural network robustness against adversarial attacks.
problem Vulnerability of neural networks to adversarial examples, especially in safety-critical applications.
method Logit regularization techniques combined with other methods to enhance adversarial robustness.
result Logit regularization can improve the effectiveness of existing adversarial defenses and create stronger black-box attacks.
New method estimates sparse covariance matrices in logit mixtures.
problem Estimating correlations among random coefficients in logit models.
method Mixed-integer optimization (MIO) with Markov Chain Monte Carlo (MCMC) for posterior draws.
result Correctly recovers true covariance structure from synthetic data.
Logit correction improves model performance by correcting spurious correlations.
problem Spurious correlations lead to poor model performance during inference.
method Proposes logit correction (LC) loss to mitigate spurious correlations.
result LC loss outperforms state-of-the-art solutions by 5.5% absolute improvement.
This article presents a proof of the existence of Bertrand-Nash equilibrium prices with multi-product firms and under the Logit model of demand that does not rely on restrictive assumptions on product characteristics, firm homogeneity or symmetry, product costs, or linearity of the utility function. The proof is based …
The paper models network formation using mixed logit models.
problem Modeling network formation in various fields.
method Mixed logit models, specifically the repeated-choice (RC) model.
result The RC model outperforms the multinomial logit (MNL) model in estimating network formation.
Method identifies over-optimized adversarial examples using IQR-based logit thresholding.
problem Over-optimized adversarial examples are hard to detect due to model optimization.
method IQR-based logit thresholding method for identifying over-optimized adversarial examples.
result The logits of the model are more useful in detecting over-optimized adversarial examples.
Bayesian MoE framework improves LLMs' uncertainty detection.
problem Brittleness and overconfidence in deterministic routing of LLMs.
method Structured Bayesian routing in weight-space, logit-space, and selection-space.
result Significant improvements in routing stability, calibration, and OoD detection.
The paper calibrates uncertainty in dropout variational inference models.
problem Miscalibration of model uncertainty in dropout variational inference.
method Logit scaling methods are extended to recalibrate model uncertainty.
result Logit scaling reduces miscalibration, improving reliability of predictions.
Analyzes how inclusion/exclusion from STOXX Europe 600 Index affects company prices.
problem Understanding price dynamics of companies in STOXX Europe 600 Index.
method Used logit models and neural networks to analyze data.
result Identified independent variables affecting price changes.
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
problem Adapting pre-trained models for new tasks efficiently.
method Supplementing pre-trained model with an auxiliary model that shifts output distribution.
result Achieved similar results to training from scratch with fewer resources.
Proposes PG-DA for Bayesian MMNL estimation to handle non-conjugacy.
problem Non-conjugacy in the Bayesian estimation of MMNL models.
method Pólygamma data augmentation technique applied to MMNL estimation.
result Similar posterior estimates for binary choice scenarios, but empirical identification issues for J ≥ 3 J \geq 3 J ≥ 3 alternatives.