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Found 24 papers, 17 papers with code
Evaluating Interventional Reasoning Capabilities of Large Language Models
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system.
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
Capture the Flag: Uncovering Data Insights with Large Language Models
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making.
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations.
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation
Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference.
Causal Discovery with Language Models as Imperfect Experts
Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making.
Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature.
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts
Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i. e., functions that are minimal in expectation for the ground-truth distribution.
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine.
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings
This article introduces byteSteady -- a fast model for classification using byte-level n-gram embeddings.
In Search of Robust Measures of Generalization
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Differentiable Causal Discovery from Interventional Data
This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16\% points.
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
Maximum Margin Interval Trees
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine.
Large scale modeling of antimicrobial resistance with interpretable classifiers
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide.
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers.
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies.
