Test verifies if data meets SCAR assumption for PU learning.
problem Verify if labeling mechanism follows SCAR assumption in PU learning.
method Generate artificial labels, mimic distribution of test statistic.
result Test detects deviations from SCAR and controls type I error.
Study proposes a clustering and logistic regression algorithm for PU classification under Non-SCAR.
problem PU classification under Non-SCAR condition when SCAR condition is unsatisfied.
method 2-means clustering followed by logistic regression.
result Efficacy of the proposed algorithm demonstrated on 11 real data sets and a synthetic set.
Generic scarring occurs along stable minimal hypersurfaces in 3-7 dimensional manifolds.
problem Understanding scarring behavior of minimal hypersurfaces along stable ones.
method Analyzing a generic metric on a manifold to show scarring of minimal hypersurfaces.
result Closed, embedded minimal hypersurfaces scarring along stable ones, with diverging area and Morse index.
Minimal hypersurfaces scarring along a fixed one in certain manifolds.
problem Scarring of minimal hypersurfaces in specific manifolds.
method Generic scarring phenomenon for minimal hypersurfaces in thick-at-infinity manifolds with thin foliation.
result Existence of sequences of minimal hypersurfaces scarring along a fixed one, with diverging area and renormalized convergence to the fixed hypersurface.
Study on random surfaces in hyperbolic 3-manifolds, focusing on geometric and topological properties.
problem Distribution of nearly geodesic surfaces in hyperbolic 3-manifolds.
method Invariant measures on the Grassmann bundle G(M) derived from limits of random minimal surfaces.
result Topological limiting measures are totally scarring if M contains a totally geodesic subsurface, while geometrical limiting measures are not.
Proposes methods to estimate posterior probability and propensity score functions without assuming constant propensity score.
problem Learning from biased positive-unlabeled data.
method Parametric approach to joint estimation of posterior probability and propensity score functions using maximum likelihood and alternating maximization.
result Proposed methods are comparable or better than existing methods based on Expectation-Maximisation scheme.
This work improves LePU by modeling the annotation process more realistically.
problem Improving semi-supervised learning from positive and unlabeled data.
method Incorporates realistic models for the annotation process and proposes an inference algorithm.
result Established the identifiability of annotation process and classification function.
Paper proposes LC-Checkpoint for efficient deep learning model checkpoints.
problem Efficient construction of checkpoints for deep learning models.
method Lossy compression scheme using quantization and priority promotion with Huffman coding.
result LC-Checkpoint achieves up to 28x compression and 5.77x speedup over SCAR.
New model tackles PU data with better accuracy.
problem Addressing positive and unlabeled data challenges.
method Double Exponential Tilting Model (DETM)
result DETM effectively handles selected at random PU data.
Improved classifier for PU data using logistic regression.
problem Analysis of Positive Unlabeled data under SCAR assumption.
method Fitting misspecified logistic regression model to PU data.
result The classifier performs on par or better than competitors on real data sets.
Machine learning (ML) training algorithms often possess an inherent self-correcting behavior due to their iterative-convergent nature. Recent systems exploit this property to achieve adaptability and efficiency in unreliable computing environments by relaxing the consistency of execution and allowing calculation errors…
Proposes a weaker faithfulness assumption for causal discovery.
problem Violation of the faithfulness assumption in causal discovery.
method Proposes a new assumption called 2-adjacency faithfulness and a modified Grow and Shrink algorithm.
result Proves the correctness of the modified algorithm under weaker assumptions.
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
Study finds rigidity of biconservative hypersurfaces in space forms without curvature assumptions.
problem Investigating biconservative hypersurfaces in space forms without scalar curvature assumptions.
method Introduced a novel divergence-free tensor to derive results without curvature assumptions.
result Rigidity results for biconservative hypersurfaces in space forms without scalar curvature assumptions.
Directed graphical models provide a useful framework for modeling causal or directional relationships for multivariate data. Prior work has largely focused on identifiability and search algorithms for directed acyclic graphical (DAG) models. In many applications, feedback naturally arises and directed graphical models …
Paper relaxes independence assumption for non-centered data.
problem Failing to account for dependencies in data leads to model failures.
method Proposes 'Kronecker-sum-structured mean' assumption to relax zero-mean requirement.
result Models with nonconvex but unimodal log-likelihoods can be solved efficiently.
New algorithm estimates causal effects for non-Gaussian data.
problem Estimating causal effects in non-Gaussian distributions.
method Generalized k-Triangle Faithfulness Assumption and Edge Estimation Algorithm.
result Uniformly consistent estimates of causal effects.
New ITID assumption improves generalization in practical tasks.
problem Generalization theories fail to interpret many generalization phenomena and guide practical learning tasks due to the IID assumption.
method Proposes ITID assumption to consider task properties, derives a new generalization bound, and introduces an invariance enhancement algorithm.
result Demonstrates the effectiveness of the new generalization theory in improving practical generalization performance.
Improved k-NN active learning with local smoothness assumption.
problem Active learning convergence rates under smoothness assumptions.
method Designing an active learning algorithm with better convergence rate using local smoothness assumption for k-NN.
result Better convergence rate than in passive learning.
For many interesting tasks, such as medical diagnosis and web page classification, a learner only has access to some positively labeled examples and many unlabeled examples. Learning from this type of data requires making assumptions about the true distribution of the classes and/or the mechanism that was used to selec…
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
New assumptions and algorithm solve offline two-player zero-sum Markov games.
problem Solving offline two-player zero-sum Markov games under insufficient assumptions.
method Proposed unilateral concentration assumption and pessimism-type algorithm.
result Algorithm efficiently learns Nash equilibrium under unilateral concentration.
The paper relaxes assumptions for analyzing stochastic optimization algorithms.
problem Analyzing the convergence of stochastic gradient algorithms under weaker variance assumptions.
method Building on and extending a connection to the Halpern iteration, the paper analyzes algorithms for convex nonsmooth optimization and min-max problems.
result Rates for optimality measures are obtained without requiring boundedness of the feasible set for problems beyond simple constrained optimization.
Unified parametric assumption improves convergence guarantees for nonconvex optimization.
problem Weak convergence guarantees for nonconvex optimization.
method Introducing a novel unified parametric assumption.
result Unified convergence theorem for gradient-based methods.
Revisits causal inference identifiability with positivity assumption.
problem General identifiability in causal inference without positivity assumption.
method Introduces new algorithm sound and complete under positivity assumption.
result New algorithm connects general identifiability to classical identifiability.
Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assumptions have focused on estimation of effects for a single treatment. In this work, we construct techniques for estimation with multiple treat…
Emputation learns imputation models guided by missingness assumptions.
problem Learning imputation models for missing data given observed data.
method Guided by specific missingness assumptions, Emputation trains a deep generative model to learn the extrapolation distribution of missing variables.
result The population minimizer of the emputation risk recovers the target extrapolation distribution under various identification assumptions.
A new learning method uses data to learn from large model sets.
problem Learning with large sets of candidate models where uniform convergence is hard.
method Data-dependent learning that incorporates empirical data less reliant on prior assumptions.
result Demonstrates improved generalization in various learning assumptions.
The paper bounds and identifies joint probabilities in causal inference with monotonicity assumptions.
problem Bounding and identifying joint probabilities of potential outcomes and observed variables under monotonicity assumptions.
method Proposes new families of monotonicity assumptions, formulates bounding problem as linear programming, introduces new monotonicity assumption for identification.
result Validated methods through numerical experiments and applied to real-world datasets.
This paper introduces PM and PMLP to enhance SSL by considering probability density and cluster assumptions.
problem Insufficient utilization of unlabeled data in SSL.
method Introduces PM to discern similarity and PMLP to consider cluster assumption in label propagation.
result PMLP outperforms other methods in SSL tasks.
For binary classification we establish learning rates up to the order of n−1 for support vector machines (SVMs) with hinge loss and Gaussian RBF kernels. These rates are in terms of two assumptions on the considered distributions: Tsybakov's noise assumption to establish a small estimation error, and a new geometr…
Neurosymbolic predictors fail to model uncertainty under independence assumption.
problem Neurosymbolic predictors' reliance on independence assumption limits their ability to model uncertainty.
method Formal analysis of NeSy predictors under independence assumption.
result Assuming independence among symbolic concepts prevents NeSy predictors from representing uncertainty.
This paper tackles non-convex phase retrieval with structured assumptions.
problem Phase retrieval with limited measurements and structure assumptions.
method Non-convex approaches with sample complexity guarantees.
result Sample-efficient recovery with structured signals/images.
Derives formulas from Green function Hessian assumption.
problem Deriving formulas from Green function Hessian assumption.
method Assumption on Hessian of Green function leads to monotonicity formulas.
result Explicit examples of manifolds satisfying assumption.
Improved causal discovery methods for large graphs without strict assumptions.
problem Sub-optimal solutions due to faithfulness assumption violations.
method Super-structure estimation and local search strategies.
result The proposed method scales to hundreds of nodes with high accuracy.
Clarifies the theory of the deconfounder by Imai and Jiang.
problem Theoretical requirements for the deconfounder algorithm.
method Clarifies the assumption of 'no unobserved single-cause confounders' using empirical studies.
result Imai and Jiang's clarification of the assumption does not hold for counterexamples proposed by Ogburn et al. (2020).
Study reward-free RL in non-linear settings, improving efficiency and removing assumptions.
problem Improving sample efficiency in reward-free reinforcement learning for non-linear function approximation.
method Proposed RFOLIVE algorithm for minimal structural assumptions, analyzed hardness results for reward-free and reward-aware exploration.
result Statistical efficiency and hardness results under various structural assumptions, no need for reachability or explorability assumptions.
Bagging stabilizes models without distributional assumptions.
problem Stability of machine learning models without distributional assumptions.
method Derives a finite-sample guarantee on bagging stability for any model.
result Guarantee applies to many bagging variants and is optimal.
A new method ReCPE removes the need for a distributional assumption in PU learning.
problem Training binary classifiers with only positive and unlabeled data without negative data.
method Regrouping CPE (ReCPE) that constructs an auxiliary distribution to ensure positive data support is never in negative data support.
result ReCPE improves all state-of-the-art CPE methods on various datasets, indicating the need for the distributional assumption.
The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of consistency, and show that simple consistent algorithms exist, under most general non-parametric assumptions. The notion of consistency is as f…
The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of consistency, and show that simple consistent algorithms exist, under most general non-parametric assumptions. The notion of consistency is as f…
Improves online learning algorithms for functional models with capacity assumptions.
problem Convergence rates of online stochastic gradient descent algorithms for functional linear models.
method Characterizations of slope function regularity, kernel space capacity, and sampling process covariance operator.
result Capacity assumptions can alleviate saturation of convergence rates as function regularity increases.
There is a large body of work on convergence rates either in passive or active learning. Here we outline some of the results that have been obtained, more specifically in a nonparametric setting under assumptions about the smoothness and the margin noise. We also discuss the relative merits of these underlying assumpti…
We obtain sharp quantitative Laplacian upper and lower estimates under no assumption on curvatures. As a result, we derive quantitative Laplacian, area and volume comparison theorems for tubes in Riemannian and Kähler manifolds under weak integral curvature assumptions. We also give some applications, such as a general…
Develops methods for constructing likelihoods and priors for Bayesian networks.
problem Learning parameters and structure of Bayesian networks from limited data.
method Introduces assumptions for constructing likelihoods and priors from small assessments.
result Allows construction of likelihoods and priors for a wide range of network structures.
This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been show…
New active learning algorithm adapts to data without strict assumptions.
problem Efficiently label data with expensive labeling costs.
method Nonparametric adaptive active learning under local smoothness condition.
result Achieves minimax rate of convergence, performs almost as well as best non-adaptive algorithms.
Paper tackles offline RL with weak assumptions on both function classes and data coverage.
problem Achieve sample-efficient offline RL with weak assumptions on both factors.
method Simple algorithm based on primal-dual formulation of MDPs, with density-ratio function modeling dual variables.
result Polynomial sample complexity achieved under realizability and single-policy concentrability.