The study provides foundations for naive diversification, a preference for equal treatment of alternatives.
problem Understanding and mathematically grounding naive diversification preferences.
method Axiomatization of naive diversification as a preference for equality over inequality, and derivation of its relationship to classical diversification.
result Naive diversification is a preference for equality over inequality, and it is characterized by convex and permutation invariant preferences.
Paper analyzes venture capital exit decisions under inconsistent preferences.
problem Time-inconsistent preferences in venture capital exit timing.
method Modeling four types of venture capitalists with varying levels of inconsistency.
result Time-inconsistent venture capitalists exit earlier than consistent ones.
Study shows naive recommendation models are flawed due to user feedback.
problem Flaws in naive recommendation models due to user feedback.
method Proposed a model with heterogeneous user preferences and proved the inconsistency of naive estimators.
result Consistent estimators are efficient in the presence of myopic agents.
This work frames reward modelling from preferences as a causal problem.
problem Reward modelling from preference data for AI alignment.
method Causal inference approach to identify challenges and assumptions.
result Causally-inspired approaches improve model robustness.
Bal-PM reduces preference labeling costs for LLMs.
problem Efficiently acquiring human feedback for preference modeling in large language models.
method Bayesian Active Learning with entropy maximization in feature space.
result Bal-PM reduces the number of required preference labels by 33% to 68%.
Study on investment strategy for agents with periodic preferences and discounting.
problem Investment decisions by agents with periodic S-shaped preferences and present bias.
method Infinite-horizon, continuous-time portfolio selection problem with quasi-hyperbolic discounting.
result Time-consistent planning strategy can be formulated as an equilibrium to a static mean field game.
A new method reduces preference distortion in LLM alignment.
problem Vulnerability of traditional LLM alignment methods to human preference heterogeneity.
method Sign Estimator: A simple, provably consistent, and efficient estimator using binary classification loss.
result Substantially reduces preference distortion over a panel of simulated personas.
Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied t…
This paper extends the classical consumption and portfolio rules model in continuous time (Merton 1969, 1971) to the framework of decision-makers with time-inconsistent preferences. The model is solved for different utility functions for both, naive and sophisticated agents, and the results are compared. In order to so…
Paper proposes a data augmentation method for LLM-generated data in market research.
problem Bias in LLM-generated data in market research.
method Statistical data augmentation approach integrating LLM-generated and real data.
result Statistically robust estimators with reduced bias and cost savings.
Tests for Esophageal cancer can be expensive, uncomfortable and can have side effects. For many patients, we can predict non-existence of disease with 100% certainty, just using demographics, lifestyle, and medical history information. Our objective is to devise a general methodology for customizing tests using user pr…
MPANF improves naive forecast by incorporating directional information.
problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.
Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes primary weakness - attribute independence - and improve the performance of the algo…
Improved Naive Bayes for text classification with small datasets.
problem Poor performance of Naive Bayes in small training datasets.
method Introducing a correlation factor to Naive Bayes estimator.
result Our method achieves better accuracy than traditional Naive Bayes.
Naive investors make riskier choices than optimal strategies in continuous-time finance.
problem Continuous-time Markowitz portfolio selection with naive reoptimization.
method Analytical derivation of naive policies from discretely naive policies.
result Naive policies are always riskier and less efficient than equilibrium policies.
Paper introduces Generalized Naive Bayes for better data fitting.
problem Improving Naive Bayes for better data fitting.
method Developed new greedy and optimal algorithms for GNB.
result Proves GNB fits data at least as well as classical NB.
Improved Naive Bayes classifier with neural network models.
problem Limited complexity handling and independence assumption in Naive Bayes.
method Introducing Neural Naive Bayes and Neural Pooled Markov Chain models.
result Error rate reduced by 4.5 on IMDB dataset.
Sophisticated volatility models outperform naive portfolio strategies.
problem Improving mean-variance portfolio performance over the naive 1/N strategy.
method Investigated various econometric and portfolio models across multiple datasets.
result Most models achieve higher Sharpe ratios and lower portfolio volatility than the naive rule.
A nearly tight convex relaxation for sparse Naive Bayes features.
problem Feature selection in large-scale Naive Bayes classification.
method Proposes a convex relaxation for the combinatorial maximum-likelihood problem of feature selection in Naive Bayes.
result The convex relaxation bounds become tight as marginal feature contributions decrease, providing a nearly optimal solution.
RWS outperforms current methods in learning SCFMs.
problem Learning models with stochastic control flow is challenging.
method Revisited reweighted wake-sleep algorithm for SCFMs.
result RWS learns better models and inference networks with more particles.
Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take plac…
New metrics using Laplace approximation improve Gaussian process model selection.
problem Finding a balance between model accuracy, interpretability, and simplicity.
method Introducing multiple metrics based on the Laplace approximation to evaluate Gaussian process models.
result Our metrics provide comparable performance to dynamic nested sampling but are significantly faster.
The paper compares one-hot encoding to Naïve Bayes for categorical variables.
problem Incorrect one-hot encoding affects Naïve Bayes performance.
method Mathematical and experimental analysis of PoB vs. categorical Naïve Bayes.
result Posterior probabilities are usually greater in the PoB case, but agree on the maximum a posteriori class label.
New bounds improve pruning for BDeu score in BNSL.
problem Improving pruning for BDeu score in BNSL.
method Deriving new upper bounds for BDeu score.
result New bounds are tighter and computationally efficient.
Proposes a non-convex optimization method for a parsimonious weighted naive Bayes classifier.
problem Improving naïve Bayes classifier performance with a large number of input variables.
method Sparse regularization of model log-likelihood for direct estimation of variable weights.
result Optimization-based weighted naïve Bayes classifiers achieve equivalent performance to averaging-based classifiers.
New algorithms reduce matching regret by limiting frequent updates.
problem Minimizing regret in stochastic matching with rare optimization updates.
method Batched algorithms that limit matching updates to Θ(log log T) rounds.
result Achieve a regret bound of \(\widetilde{\mathcal{O}}(\sqrt{T})\) with reduced computational cost.
Proposes a sparse Naïve Bayes classifier to improve performance and interpretability.
problem Naïve Bayes assumes feature independence, which is violated in real data.
method Integrates feature correlation and performance measures for feature selection.
result Competitive results in accuracy, sparsity, and running times for balanced datasets.
RAVEN improves weak-to-strong generalization under distribution shifts.
problem Weak models fail to supervise strong models effectively under distribution shifts.
method RAVEN dynamically learns optimal combinations of weak models and strong model parameters.
result RAVEN outperforms existing methods by over 30% on out-of-distribution tasks.
Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroscedastic, and asymmetric. In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challeng…
Draft proposes adapting neural networks to match naive Bayes classifiers.
problem Bridge between neural networks and naive Bayes classifiers.
method Class-conditional compression and disentanglement using variational bounds.
result Latent representations enable naive Bayes classifier performance.
New method for identifying best designs in vector optimization with uncertain feedback.
problem Optimizing vector-valued outcomes with uncertain preferences.
method Stochastic bandit feedback, polyhedral ordering cone, (ε,δ)-PAC Pareto set identification. result Sample complexity characterized and matched by the naïve elimination algorithm.
We study the (standard) cohomology Hst∙(E) of a Courant algebroid E. We prove that if E is transitive, the standard cohomology coincides with the naive cohomology Hnaive∙(E) as conjectured by Stienon and Xu. For a general Courant algebroid we define a spectral sequence converging to its stan…
This paper proposes an efficient method for calculating Shapley values in Naive Bayes classifiers.
problem The need for explaining machine learning model decisions.
method An exact analytic expression of Shapley values for Naive Bayes classifiers.
result The proposed Shapley values provide informative results with low complexity and low computation time.
Naive Bayes can be used as a discriminative classifier, matching the definition of logistic regression.
problem The definition of generative and discriminative classifiers.
method Comparing Naive Bayes and logistic regression, showing they can be used in either generative or discriminative ways.
result Naive Bayes can be used as a discriminative classifier.
Data-driven approaches outperform random choices in multi-label classification with Naive Bayes.
problem Improving multi-label classification performance with Naive Bayes classifiers.
method Comparison of data-driven, a priori, and random approaches on 12 benchmark datasets.
result Data-driven methods significantly outperform random baselines on F1 scores and Subset Accuracy.
Two new Hie-TAN and Hie-TAN-Lite algorithms improve TAN for hierarchical feature spaces.
problem Learning dependencies in hierarchical feature spaces.
method Exploits hierarchical parent-child relationships as constraints to learn a dependency tree.
result Hie-TAN-Lite outperforms Hie-TAN and other methods in predictive accuracy.
PrefOpt simplifies optimization with human preference feedback.
problem Design and optimization guided by human preferences.
method Extends latent variable model for binary preferences to include equivalent preference observations.
result Simplified sequential optimization tasks incorporating human feedback.
The paper proposes a novel approach to music analysis using text mining techniques.
problem Analyzing musical documents using traditional text mining methods.
method Developed a Naive Dictionary of 'muselets' (musical words) of uniform length.
result Demonstrated reasonable topic modeling and pattern recognition results with a simplified dictionary.
Develops a model for gambling decisions under time inconsistency.
problem Time inconsistency in gambling decisions due to probability weighting in CPT.
method Formulates the problem as a mathematical program, derives optimal precommitted rule.
result Gambler may enter the casino even with limited play, behavior varies based on gains/losses.
Locally private Naive Bayes works for personal data.
problem Training Naive Bayes on personal data with privacy concerns.
method Local differential privacy, dimensionality reduction, and perturbation techniques.
result Naive Bayes accuracy maintained under local differential privacy.
Optimizes molecular generation for chemist preferences.
problem Models lack inherent preferences for chemist-desired structures.
method Fine-tuning with Direct Preference Optimization.
result Approach is simple, efficient, and highly effective.
Improved LSTM models predict wind power more accurately with weather data.
problem Poor performance of generic LSTM models on wind power data.
method Contextualized LSTM models using weather forecast data and modifications to address model shortcomings.
result Increased accuracy and reduced naive character in predictions.
Improved Naive Bayes for better phone call behavior classification.
problem Noise in mobile phone data affects phone call behavior classification accuracy.
method Improved naive Bayes classifier with behavioral pattern analysis and dynamic noise threshold.
result Our technique improves classification accuracy by 15%.
New method adapts to user preferences dynamically, improving recommendation models.
problem Current recommendation models lack dynamic adaptation to changing user preferences.
method Preference Discerning with LLM-Enhanced Generative Retrieval
result Mender achieves state-of-the-art performance in adapting to evolving user preferences.
Study on mapping class groups of infinite type surfaces.
problem Property Pextnaive for mapping class groups of infinite type surfaces. method Analyzes the existence of elements g and hi satisfying specific group properties. result Establishes the existence of g for any finite collection of non-trivial elements hi. Investment and consumption strategies optimized with uncertain parameters.
problem Investment and consumption preferences in an incomplete financial market with uncertain parameters.
method PDE characterization and semi-explicit saddle-point construction of forward preferences and optimal strategies.
result A specific relationship between initial investment preference and forward consumption preference is necessary.
DPS uses posterior sampling for preference-based RL, achieving a first regret guarantee.
problem Formal frameworks for preference-based RL with theoretical analysis.
method Preference-based posterior sampling, Bayesian credit assignment.
result First asymptotic Bayesian no-regret rate for preference-based RL.
Enhances preference learning by incorporating response times into binary choices.
problem Limited information from binary choices about preference strength.
method Combines choices and response times using the EZ diffusion model.
result Response times improve utility estimation for strong preferences.