Aggregator learns optimal forecast aggregation in partial evidence environments.
problem Forecast aggregation in repeated binary event settings with expert forecasts.
method Bayesian experts with partial evidence, aggregator learns optimal aggregation.
result Optimal aggregation can be learned in polynomial time for a wide range of partial evidence environments.
New MLMC method reduces evidence estimation cost.
problem Efficiently estimating model evidence in Bayesian inference.
method Multilevel Monte Carlo (MLMC) sampling for unbiased estimation.
result Significant computational savings in estimating model evidence.
This article introduces a framework to estimate the value of evidence-based decision making.
problem Lack of empirical tools to assess the value of evidence-based decision making and optimize statistical precision.
method Empirical framework using parametric and nonparametric empirical Bayes methods.
result The value of statistical evidence depends on how organizations translate it into policy decisions.
Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.
problem Understanding how people make decisions with uncertain timing of payments.
method Normative model of growth-optimality, revisiting experimental evidence on time lotteries.
result Growth-optimality better explains experimental data on time lotteries than expected discounted utility theory.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
problem Fact-checking decisions should consider temporal information of claims and evidence.
method Investigated four temporal ranking methods to optimize evidence ranking for fact-checking models.
result Time-aware evidence ranking surpasses relevance assumptions and improves veracity predictions for time-sensitive claims.
New method optimizes portfolios by dynamically integrating ESG constraints.
problem Static ESG scores mismatch sequential portfolio decisions.
method MACF-X, a family of adapters that learns ESG costs from multimodal evidence.
result Reduces tail ESG budget pressure while maintaining financial performance.
Bayesian evidence framework selects best pre-trained CNN for transfer learning.
problem Selecting the best pre-trained deep representation for transfer learning.
method Formulated on LS-SVM classifier, evidence maximization for regularization parameters, Aitken's delta-squared process for convergence, greedy algorithm for ensemble selection.
result Competitive performance in visual recognition datasets, state-of-the-art performance in accuracy and efficiency.
Evidence Networks simplify Bayesian model comparison for complex models.
problem Bayesian model comparison challenges with intractable likelihoods or priors.
method Loss functions and neural networks for fast, amortized estimation of Bayes factors.
result Evidence Networks provide accurate and scalable Bayes factor estimation.
The natural gradient of ELBO vanishes in unconstrained optimization, simplifying learning.
problem The gap between evidence and ELBO has a vanishing natural gradient.
method Analyzes the Fisher-Rao gradient of ELBO and its implications for learning.
result Maximizing ELBO is equivalent to minimizing KL divergence, simplifying learning.
Calibrating a trading rule using a historical simulation (also called backtest) contributes to backtest overfitting, which in turn leads to underperformance. In this paper we propose a procedure for determining the optimal trading rule (OTR) without running alternative model configurations through a backtest engine. We…
Stochastic Bayesian Neural Network improves scalability and performance.
problem Challenges in calculating posterior distribution in Bayesian Neural Networks.
method Maximizes Evidence Lower Bound using Stochastic Evidence Lower Bound objective function.
result Demonstrates improved performance and scalability over previous algorithms.
This paper develops a framework for efficient decision-making under time pressure.
problem Efficient decision-making under time pressure and subjective tradeoffs.
method Unified framework for evidence-based decision-making under time pressure.
result Ability to model and understand decision-making behavior under time constraints.
This work improves regression performance by using distributional losses, finding better optimization leads to improved generalization.
problem Improving regression performance in reinforcement learning.
method Introduced a novel distributional regression loss and investigated its effects on optimization and generalization.
result The novel distributional regression loss leads to improved prediction accuracy and better optimization.
Algorithm reduces audit costs by identifying best service configurations from biased textual evidence.
problem Designing service systems from textual evidence requires accurate selection despite biased automated scoring.
method Developed PP-LUCB algorithm combining LLM scores and selective audits to minimize costs.
result Correctly identified the best model in 40/40 trials with 90% cost reduction.
New model predicts user ratings using chains of evidence.
problem Predicting user ratings from sparse data.
method Recursive evidence chains linking users and items.
result Competitive results in accuracy and speed.
IVON optimizes large neural networks, matching or outperforming Adam.
problem The inefficacy of variational learning in large neural networks.
method Improved Variational Online Newton (IVON) optimizer.
result IVON consistently matches or outperforms Adam for large networks.
Bayesian PINNs optimize loss weights for PDEs and data.
problem Optimizing loss weights in physics-informed neural networks.
method Laplace approximation for efficient model evidence computation.
result Unified Bayesian setting for PDEs and noisy measurements.
The paper analyzes optimal execution strategies for traders with inventory processes influenced by Brownian motion.
problem Optimal execution strategies for traders with inventory processes influenced by Brownian motion.
method Statistical tests and empirical analysis of intra-day data from the Toronto Stock Exchange.
result Empirical evidence supports the presence of a non-zero Brownian motion component in inventories and wealth processes.
Develops ELBD for efficient feature selection in VAE latent variables.
problem Feature selection in latent variables of VAE and its variants.
method ELBD score algorithm and weak convergence approximation for optimization.
result Effective feature selection and optimization of VAE models.
AdaBoost is one of the most popular ML algorithms. It is simple to implement and often found very effective by practitioners, while still being mathematically elegant and theoretically sound. AdaBoost's interesting behavior in practice still puzzles the ML community. We address the algorithm's stability and establish m…
Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases. Though DDI is investigated in domai…
The paper explores how to handle uncertain evidence in probabilistic models.
problem Handling uncertain evidence in probabilistic models and stochastic simulators.
method The paper considers distributional evidence, Jeffrey's rule, and virtual evidence as methods for interpreting uncertain evidence.
result The paper provides guidelines on how to account for uncertain evidence and highlights the importance of careful consideration.
This paper improves SAM by reformulating it as a bilevel optimization problem.
problem Improving Sharpness-Aware Minimization (SAM) for better performance.
method Reformulate SAM as a bilevel optimization problem using a 0-1 loss surrogate.
result BiSAM consistently results in improved performance compared to SAM and its variants.
GEAR uses graphs to integrate and reason over multiple evidence for fact verification.
problem Fact verification requires integrating and reasoning over multiple pieces of evidence.
method GEAR employs a graph-based framework to transfer information among evidence and uses BERT for improved performance.
result GEAR achieves a promising test FEVER score of 67.10% on a large-scale benchmark dataset.
SA-BCP combines long-term and local evidence for efficient, adaptive online prediction.
problem Balancing fast adaptation and stable coverage in online prediction.
method State-Adaptive Bayesian Conformal Prediction (SA-BCP) using gated convex combination of temporal inertia and spatial evidence.
result SA-BCP achieves at-or-above-nominal coverage with substantially sharper intervals compared to discounted Bayesian CP.
OPAA estimates probability densities using functional analysis.
problem Estimating probability density functions efficiently and accurately.
method OPAA uses a parallelizable algorithm based on functional analysis to estimate probability distributions.
result OPAA provides an efficient method to estimate probability density functions and normalizing weights.
Evidence acquisition costs influence disclosure behavior and preference.
problem How evidence acquisition costs affect disclosure behavior and preference.
method Analyzes sender-receiver interactions with covert and overt evidence acquisition, varying certification costs.
result Equilibria converge to the Pareto-worst free-learning equilibrium as costs vanish, and receivers prefer covert to overt acquisition.
Paper proposes cross-coding to improve conditional inference in VAEs.
problem Challenges in conditional inference with VAEs, especially for arbitrary queries.
method Cross-coding to approximate latent distributions after conditioning.
result Cross-coding variations outperform Hamiltonian Monte Carlo.
Optimizer choice affects neural scaling laws, changing the exponent α.
problem The exponent α in neural scaling laws L(N)∝N−α varies with the optimizer used. method Controlled random-feature regression experiments with five optimizer variants and six spectral conditions.
result Preconditioned optimizers yield steeper scaling (larger α), with the α-shift increasing across most of the tested spectral range. Enhances clustering by using external categorical data.
problem Improving clustering outcomes using external categorical evidence.
method Evidence transfer method that manipulates autoencoder latent representations based on external categorical data.
result Our method effectively manipulates latent representations with real evidence and remains robust with low quality evidence.
This paper evaluates a method to improve representations using incomplete external evidence across tasks.
problem Increasing labelled data quality and quantity is challenging due to manual labelling errors and noise.
method Evidence Transfer method using incomplete categorical external evidence.
result Evidence Transfer proves effective and robust against different levels of incompleteness.
This study benchmarks fifteen deep learning optimizers and identifies a subset that generally performs well.
problem Choosing the best optimizer in deep learning is challenging and often based on anecdotes.
method An extensive, standardized benchmark of fifteen popular optimizers, analyzing over 50,000 runs.
result A subset of optimizers and parameter choices generally leads to competitive results.
Accumulator module improves reinforcement learning by delaying decisions based on evidence.
problem Incomplete information, limited sensing, and stochastic environments lead to risky decisions.
method Integrates evidence for each action, delays action until confident, using dynamic competition.
result Accumulator module outperforms traditional reinforcement learning methods in a guessing game.
Asynchronous methods converge as well as synchronous ones for convex optimization.
problem Optimizing convex functions efficiently in parallel systems.
method Completely asynchronous stochastic gradient procedures.
result Achieve optimal convergence rates under similar conditions as synchronous methods.
A new method reduces GP classification complexity for big data.
problem High computational complexity for standard GP classification methods.
method Variational inducing inputs with quadratic approximation for optimization.
result Improved computational efficiency for big data problems.
Develops an anytime-valid framework for optimal policy identification from logged contextual bandit data.
problem Selecting the optimal policy from a candidate policy class while monitoring evidence continuously.
method Constructs a time-indexed set that retains the true optimal policy set uniformly over time.
result The procedure allows the analyst to monitor policy values, eliminate clearly suboptimal policies, and stop at data-dependent times without invalidating inference.
System validates truthfulness of statements with evidence.
problem Unverified contents in accessible information sources.
method Inference method on a knowledge graph (KG) combined with ontologies.
result System provides valid and concise evidence for false statements.
We present a novel Newton-type method for distributed optimization, which is particularly well suited for stochastic optimization and learning problems. For quadratic objectives, the method enjoys a linear rate of convergence which provably \emph{improves} with the data size, requiring an essentially constant number of…
New optimization method speeds up learning from data.
problem Efficiently optimizing large datasets for machine learning.
method Minibatch stochastic variance reduced proximal iterations.
result Improved convergence speed for quadratic objectives.
Study assesses neural nets for optimization problems, highlighting SiLU's effectiveness.
problem Using neural nets for optimization problems, especially for accurate approximations.
method Determined best activation function (SiLU) for nonlinear optimization problems. Analyzed function approximations using neural networks and interpolation/regression models.
result Neural nets can deliver competitive zero- and first-order approximations but underperform on second-order approximations.
Bayes factors and relative belief ratios are compared as measures of statistical evidence.
problem Which measure of evidence is more appropriate: Bayes factors or relative belief ratios?
method Comparison of Bayes factors and relative belief ratios, considering properties and restrictions.
result Relative belief ratio has better properties as a measure of evidence.
Method estimates Bayesian evidence from posterior samples using normalizing flows.
problem Estimating Bayesian evidence from posterior samples.
method Normalizing flows for evidence estimation.
result Method is more robust to sharp features in posterior distributions, especially in higher dimensions.
Interactive learning of automata models with human input.
problem Learning finite state automata from noisy, incomplete data.
method Evidence-driven state-merging algorithm with human interaction.
result Human input significantly improves automata learning accuracy.
A new upper bound for variational inference improves the efficiency of Bayesian deep learning.
problem Improving variational inference in Bayesian deep learning.
method Presented a new upper bound (EUBO) for evidence, derived from KL-divergence and log marginal likelihood, and used SGD for optimization.
result The new upper bound (EUBO) is tighter than previous methods and outperforms state-of-the-art results in Bayesian neural networks.
Audit financial machine learning workflows to detect spurious predictability.
problem Spurious predictability in financial machine learning models.
method Falsification audit testing predictive workflows against synthetic environments.
result Many apparent financial predictions are artifacts, not genuine.
New methods for equity fund selection and portfolio construction using mutual fund top holdings.
problem Classic equity fund selection and portfolio construction problems.
method Propose an easy-to-implement framework to produce a long-short portfolio from mutual fund top holdings.
result Generate impressive results and show statistical evidence.
Revisits Gaussian process model with spherical harmonics for scalable deep learning.
problem Scaling Gaussian process models to large input dimensions with high frequency learning.
method Introduces new kernels related to deep models, variational learning of spherical harmonic phases, and sparseness in eigenbasis.
result Enables scaling to larger input dimensions and learning of high frequency variations.
Machine learning forecasts show bias at long horizons, contrary to standard tests.
problem Forecast efficiency tests misinterpret machine learning performance.
method Theoretical and empirical analysis of regularization and measurement noise.
result Machine learning forecasts exhibit overreaction at longer horizons, not bias.