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Found 44 papers, 18 papers with code
A Learnable Color Correction Matrix for RAW Reconstruction
Autonomous driving algorithms usually employ sRGB images as model input due to their compatibility with the human visual system.
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
Invariant models are also supposed to generalize to shifts in the marginal distribution $p(X_{\text{inv}})$ of the extracted features $X_{\text{inv}}$, a type of shift we call an $\textit{invariant covariate shift}$.
Core: Robust Factual Precision Scoring with Informative Sub-Claim Identification
Hallucinations -- the generation of untrue claims -- pose a challenge to the application of large language models (LLMs) [1] thereby motivating the development of metrics to evaluate factual precision.
A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)
As artificial intelligence (AI) / machine learning (ML) gain widespread adoption, practitioners are increasingly seeking means to quantify and control the risk these systems incur.
Density-Regression: Efficient and Distance-Aware Deep Regressor for Uncertainty Estimation under Distribution Shifts
Morden deep ensembles technique achieves strong uncertainty estimation performance by going through multiple forward passes with different models.
RORA: Robust Free-Text Rationale Evaluation
This is achieved by assessing the conditional V-information \citep{hewitt-etal-2021-conditional} with a predictive family robust against leaky features that can be exploited by a small model.
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits
Central to our methodology is the application of robust regression, a distributionally robust technique tailored here to improve the estimation of conditional reward distribution from logging data.
Evaluating and Enhancing Large Language Models for Conversational Reasoning on Knowledge Graphs
We further introduce LLM-ARK, a LLM grounded KG reasoning agent designed to deliver precise and adaptable predictions on KG paths.
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics
By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results
Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10, 000 training subjects without double-dipping.
MegaWika: Millions of reports and their sources across 50 diverse languages
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials.
Double-Weighting for Covariate Shift Adaptation
Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $\mathrm{p}_\text{tr}(x)$ and $\mathrm{p}_\text{te}(x)$ are different but the label conditionals coincide.
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data
Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data.
Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts
Sampling-based methods, e. g., Deep Ensembles and Bayesian Neural Nets have become promising approaches to improve the quality of uncertainty estimation and robust generalization.
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.
Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection
Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation.
Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling
The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario.
Ambiguous Images With Human Judgments for Robust Visual Event Classification
However, humans are frequently presented with visual data that they cannot classify with 100% certainty, and models trained on standard vision benchmarks achieve low performance when evaluated on this data.
A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure
The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects.
Repeated Environment Inference for Invariant Learning
The EI step uses a reference model which focuses on spurious correlations to efficiently reach a good environment partition.
JAWS: Auditing Predictive Uncertainty Under Covariate Shift
We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on the core method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with data-dependent likelihood-ratio weights.
A Simplified System Model for Optical Camera Communication
Data rate and communication distance are two important criteria for measuring the performance of optical camera communication (OCC) systems.
Cost-Sensitive Hierarchical Classification through Layer-wise Abstentions
For each layer, we also achieve higher accuracy when the overall accuracy is kept fixed across different methods.
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions
However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics.
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
Robust Fairness under Covariate Shift
We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.
Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach
We propose a framework for learning calibrated uncertainties under domain shifts, where the source (training) distribution differs from the target (test) distribution.
Distributionally Robust Learning for Unsupervised Domain Adaptation
This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network.
Active Learning under Label Shift
We address the problem of active learning under label shift: when the class proportions of source and target domains differ.
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates
Online harassment is a significant social problem.
Triply Robust Off-Policy Evaluation
Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method.
Robust Regression for Safe Exploration in Control
To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.
Regularized Learning for Domain Adaptation under Label Shifts
We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class.
Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multiclass classification.
Learning Gibbs-regularized GANs with variational discriminator reparameterization
We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.
Kernel Robust Bias-Aware Prediction under Covariate Shift
Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution.
Robust Covariate Shift Prediction with General Losses and Feature Views
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions.
Adversarial Multiclass Classification: A Risk Minimization Perspective
Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses.
Etude acoustique et repr\'esentation phonologique sur /ə˞/ suffixe rhotique en mandarin (Acoustic study and phonological representation of the rhotic suffix /ə˞/ in mandarin)
Historiquement, le suffixe /ə˞/ est un suffixe diminutif correspondant au mot 儿 ({\textless}er{\textgreater} en pinyin) qui signifie {''}petitesse{''}.
Robust Classification Under Sample Selection Bias
In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions.
