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Found 43 papers, 20 papers with code
Deep Linear Hawkes Processes
Marked temporal point processes (MTPPs) are used to model sequences of different types of events with irregular arrival times, with broad applications ranging from healthcare and social networks to finance.
Benchmark Data Repositories for Better Benchmarking
In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets.
ELBOing Stein: Variational Bayes with Stein Mixture Inference
Stein variational gradient descent (SVGD) [Liu and Wang, 2016] performs approximate Bayesian inference by representing the posterior with a set of particles.
EventFlow: Forecasting Continuous-Time Event Data with Flow Matching
Continuous-time event sequences, in which events occur at irregular intervals, are ubiquitous across a wide range of industrial and scientific domains.
Perceptions of Linguistic Uncertainty by Language Models and Humans
We find that 7 out of 10 models are able to map uncertainty expressions to probabilistic responses in a human-like manner.
JANET: Joint Adaptive predictioN-region Estimation for Time-series
Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data.
Anomaly Detection of Tabular Data Using LLMs
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning.
Dynamic Conditional Optimal Transport through Simulation-Free Flows
We study the geometry of conditional optimal transport (COT) and prove a dynamical formulation which generalizes the Benamou-Brenier Theorem.
What Large Language Models Know and What People Think They Know
Here we explore the calibration gap, which refers to the difference between human confidence in LLM-generated answers and the models' actual confidence, and the discrimination gap, which reflects how well humans and models can distinguish between correct and incorrect answers.
Probabilistic Modeling for Sequences of Sets in Continuous-Time
In this work, we develop a general framework for modeling set-valued data in continuous-time, compatible with any intensity-based recurrent neural point process model.
Bayesian Online Learning for Consensus Prediction
Given a pre-trained classifier and multiple human experts, we investigate the task of online classification where model predictions are provided for free but querying humans incurs a cost.
Functional Flow Matching
We propose Functional Flow Matching (FFM), a function-space generative model that generalizes the recently-introduced Flow Matching model to operate in infinite-dimensional spaces.
Capturing Humans' Mental Models of AI: An Item Response Theory Approach
Improving our understanding of how humans perceive AI teammates is an important foundation for our general understanding of human-AI teams.
Zero-Shot Anomaly Detection via Batch Normalization
Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
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Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection.
Diffusion Generative Models in Infinite Dimensions
We generalize diffusion models to operate directly in function space by developing the foundational theory for such models in terms of Gaussian measures on Hilbert spaces.
Probabilistic Querying of Continuous-Time Event Sequences
Continuous-time event sequences, i. e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many applications, e. g., in clinical medicine or user behavior modeling.
Predictive Querying for Autoregressive Neural Sequence Models
In reasoning about sequential events it is natural to pose probabilistic queries such as "when will event A occur next" or "what is the probability of A occurring before B", with applications in areas such as user modeling, medicine, and finance.
Variable-Based Calibration for Machine Learning Classifiers
In this paper we introduce the notion of variable-based calibration to characterize calibration properties of a model with respect to a variable of interest, generalizing traditional score-based metrics such as expected calibration error (ECE).
Fair Generalized Linear Models with a Convex Penalty
In addition, we demonstrate that the fair GLM can generate fair predictions for a range of response variables, other than binary and continuous outcomes.
Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration
An increasingly common use case for machine learning models is augmenting the abilities of human decision makers.
Automating Data Science: Prospects and Challenges
Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process.
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning
We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity.
Variational Beam Search for Novelty Detection
We consider the problem of online learning in the presence of sudden distribution shifts, which may be hard to detect and can lead to a slow but steady degradation in model performance.
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Modeling such data can be very challenging, in particular for applications with many different types of events, since it requires a model to predict the event types as well as the time of occurrence.
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful.
Active Bayesian Assessment for Black-Box Classifiers
Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications.
Dropout as a Structured Shrinkage Prior
We propose a novel framework for understanding multiplicative noise in neural networks, considering continuous distributions as well as Bernoulli noise (i. e. dropout).
Mondrian Processes for Flow Cytometry Analysis
Analysis of flow cytometry data is an essential tool for clinical diagnosis of hematological and immunological conditions.
Learning Approximately Objective Priors
Informative Bayesian priors are often difficult to elicit, and when this is the case, modelers usually turn to noninformative or objective priors.
Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling
We extend such models to introduce additional non-homogeneity into the emission distribution using a generalized linear model (GLM), with data augmentation for sampling-based inference.
Stick-Breaking Variational Autoencoders
We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes.
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Corrupting the input and hidden layers of deep neural networks (DNNs) with multiplicative noise, often drawn from the Bernoulli distribution (or 'dropout'), provides regularization that has significantly contributed to deep learning's success.
Hot Swapping for Online Adaptation of Optimization Hyperparameters
We describe a general framework for online adaptation of optimization hyperparameters by `hot swapping' their values during learning.
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method.
The Author-Topic Model for Authors and Documents
A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors.
On Smoothing and Inference for Topic Models
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data.
Continuous-Time Regression Models for Longitudinal Networks
The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science.
Learning concept graphs from text with stick-breaking priors
We describe a generative model that is based on a stick-breaking process for graphs, and a Markov Chain Monte Carlo inference procedure.
Particle-based Variational Inference for Continuous Systems
Since the development of loopy belief propagation, there has been considerable work on advancing the state of the art for approximate inference over distributions defined on discrete random variables.
Asynchronous Distributed Learning of Topic Models
Distributed learning is a problem of fundamental interest in machine learning and cognitive science.
