New algorithms improve label complexity for active multi-distribution learning.
problem Active multi-distribution learning with improved label complexity.
method Developed new algorithms for active multi-distribution learning and established improved label complexity upper and lower bounds.
result Improved label complexity upper and lower bounds for active multi-distribution learning.
Paper settles sample complexity for learning from multiple distributions.
problem Learning from multiple data distributions with a hypothesis class of bounded VC dimension.
method Introduced an algorithm with sample complexity of O((d+k)ε^-2)·(k/ε)^o(1).
result Algorithm matches lower bound up to sub-polynomial factor.
Unified framework for estimating density ratios across multiple distributions.
problem Binary density ratio estimation for multiple distributions.
method Unified framework based on Bregman divergence minimization.
result Generalization of binary DRE methods to multiple distributions.
Study shows multi-distribution learning has slower rates than single-task learning.
problem Understanding the statistical complexity of learning from heterogeneous sources.
method Structured hypothesis-testing framework to capture the statistical cost of certifying near-optimality under bounded noise.
result Learning across multiple distributions incurs slow rates scaling with k/ε2, even under constant noise levels. New bounds on learning from multiple distributions for VC classes.
problem Understanding the sample complexity of learning from multiple data distributions.
method Analyzing the gap between known upper and lower bounds for PAC-learnable classes.
result Recent progress on sample complexity for VC dimension d classes on k distributions.
New algorithm reduces sample complexity for multi-distribution learning.
problem Achieving data-efficient multi-distribution learning with robustness and fairness.
method Proposes a novel algorithm with sample complexity (d+k)/varepsilon^2 for Vapnik-Chervonenkis (VC) dimension d, matching lower bounds.
result Algorithm matches best-known lower bound and resolves open problems in COLT 2023.
New framework improves learning across multiple distributions.
problem Modeling uncertainty in sensitive machine learning applications.
method Inspired by multi-armed bandits, provides distribution-dependent guarantees.
result Enhanced dependence on suboptimality gaps and sample size.
A new family of multi-distribution divergences is characterized for fairness and other problems.
problem Comparing more than two probability distributions in a consistent way.
method Characterized a new family of multi-way coincidence divergences.
result The new family of multi-distribution Rényi divergences is necessary and arises from several independent routes.
Method constructs uniformly valid prediction sets across multiple distributions.
problem Uniformly valid prediction sets across multiple distributions.
method Max-p aggregation scheme and optimization programs.
result Optimal and efficient prediction sets for multiple distributions.
Study generalizes property elicitation to imprecise probabilities.
problem Minimizing risk over imprecise probability distributions.
method Maximin risk minimization over a set of imprecise probabilities.
result Conditions for elicitability of IP-properties.
The paper explores how regularization can improve multi-objective learning with high-dimensional data.
problem Improving multi-objective learning with high-dimensional and costly data.
method A two-stage MOL framework that leverages low-dimensional structure.
result Vanilla regularization approaches often fail in multi-objective learning, and a two-stage framework can successfully exploit low-dimensional structure.
Optimizes sample and round complexity in adaptive sampling from multiple distributions.
problem Adaptive sampling from multiple distributions with limited rounds and samples.
method Introduces OODS framework and analyzes tradeoffs between sample and round complexity.
result Achieves near-optimal sample complexity and sub-polynomial round complexity.
All you need is log
problem Comparing multiple probability distributions
method Characterizing multi-distribution Rényi divergences
result Found a canonical multi-distribution Rényi calculus
Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity…
Method improves treatment effect prediction robust to unknown covariate shifts.
problem Estimating heterogeneous treatment effects for different populations.
method Post-processing CATE T-learners with multi-accurate predictors to handle unknown covariate shifts.
result Improves bias and mean squared error in simulations with covariate shifts.
New MMOTs define a generalized metric for multi-distribution transport.
problem Lack of metric properties in multi-marginal optimal transport.
method Proved generalized metric properties for a family of pairwise MMOTs.
result Generalized triangle inequality of MMOTs cannot be improved.
KEEL improves causal discovery with fuzzy knowledge and complex data.
problem Challenges in causal discovery due to prior knowledge, domain inconsistencies, and small sample sizes.
method Weakly-supervised fuzzy knowledge and data co-driven causal discovery method (KEEL).
result KEEL outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency.