New method detects causal relationships from noisy measurements.
problem Discover causal relationships from noisy, imperfect measurements.
method Transformed Independent Noise (TIN) condition and ordered group decomposition.
result Identifies causal graph structure without over-complete ICA.
TCRI improves domain generalization by enforcing conditional independence constraints.
problem Limitations of existing domain generalization methods due to incomplete constraints.
method TCRI implements regularizers motivated by conditional independence constraints.
result TCRI achieves cross-domain stability and outperforms baselines in worst-domain accuracy.
This paper tackles target-dependent label complexity gap in active learning.
problem Target-dependent label complexity gap in Agnostic Active Learning.
method Introduces a novel distribution-splitting strategy based on number density to reduce label complexity and error rate.
result Provides theoretical guarantees and practical advantages for reducing label complexity and error rate.
BBVI converges nearly dimensionally independent for log-concave targets.
problem Efficiently optimizing variational parameters in high-dimensional spaces.
method Proved convergence rate of BBVI with reparametrization gradient for log-concave targets.
result BBVI converges with nearly independent dimension dependence for log-concave targets.
HSIC loss improves robust regression and classification models.
problem Learning robust models in the absence of target domain data.
method Adapted HSIC loss for unsupervised covariate shift.
result Models outperform standard methods on covariate shift tasks.
Paper tackles spooky effect in OSPA estimation, showing GOSPA solves it.
problem Spooky effect in optimal estimation of multiple targets with OSPA metric.
method Introduces GOSPA metric (α=2) to penalize false and missed targets. result GOSPA avoids the spooky effect and optimally lowers target estimation errors.
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to …
We describe a novel non-parametric statistical hypothesis test of relative dependence between a source variable and two candidate target variables. Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbe…
Improved Langevin algorithms with prior diffusion achieve dimension-independent convergence for non-log-concave distributions.
problem Understanding the dimension dependency of computational complexity in high-dimensional sampling.
method Investigation of prior diffusion technique for log-Sobolev inequality target distributions.
result Modified Langevin algorithm achieves dimension-independent KL divergence convergence.
Study shows mimicry attacks fail even with ASV-assisted target selection.
problem Can mimicry attacks be successful with ASV-assisted target selection?
method Used ASV to select target speakers for mimicry attacks and tested with x-vector system.
result Mimics did not succeed in spoofing the x-vector system, but relative ordering of targets was consistent.
Local method identifies causal relations in Markov equivalent DAGs.
problem Identifying causal relations when multiple DAGs are Markov equivalent.
method Graphical condition and local criteria for identifying causal paths.
result Local learning algorithm efficiently identifies causal variables.
Paper introduces EO_k for quantifying accuracy-fairness trade-offs in FRL.
problem Tackles the trade-off between accuracy and fairness in FRL.
method Kernel-based formulation of EO criterion for FRL.
result Offers a unified analytical characterization of fairness tradeoffs.
Introduce a variance-weighted batch distribution for diverse sampling in diffusion models.
problem Independent sampling in diffusion models.
method Introduce a variance-weighted batch distribution.
result Sampler with a transparent probabilistic target.
Generative multitask learning mitigates confounders causing targets.
problem Unobserved confounders causing targets but not inputs.
method Generative multitask learning (GMTL) modifies inference objective to remove joint target influence.
result Improved robustness to target shift across multitask learning methods.
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation. While such operators and bounds are readily available for a diversity of univariate targets, few multivariate targ…
HappyMap improves fairness and learning across domains by generalizing multi-calibration.
problem Improving fairness and learning across different domains for predictions.
method Proposes HappyMap, a generalized approach to multi-calibration.
result Unified understanding of fairness and learning across domains.
GraphITE estimates individual effects of graph-structured treatments.
problem Estimating individual effects of complex treatment structures.
method Graph neural networks and Hilbert-Schmidt Independence Criterion regularization.
result GraphITE outperforms baselines in estimating treatment effects for large numbers of treatments.
LCIT tests conditional independence using latent representations.
problem Detecting conditional independencies in statistical and machine learning tasks.
method Generative framework for learning latent representations of target variables X and Y, then testing for remaining dependencies.
result LCIT outperforms state-of-the-art baselines consistently under different metrics and settings.
New analysis for learning and applying preconditioners in MCMC improves efficiency.
problem Improving efficiency of MCMC algorithms by modifying them with preconditioners.
method Analyzes and compares computational costs of MCMC schemes with and without preconditioners.
result Establishes non-asymptotic guarantees for MCMC algorithms that learn and use preconditioners.
Analyzes learning and applying preconditioners in MCMC for efficiency.
problem Improving efficiency of MCMC algorithms.
method Non-asymptotic analysis of schemes that learn preconditioners.
result Established non-asymptotic guarantees for preconditioned ULA.
This study compares two methods for sampling with transport maps, finding flow-based proposals work better for multimodal distributions.
problem Sampling from distributions with complex geometries.
method Compares two approaches: (i) proposal draws from the flow and (ii) reparametrization.
result Flow-based proposals are more effective for multimodal distributions in high dimensions, while reparametrization methods are more robust in other scenarios.
The independence clustering problem is considered in the following formulation: given a set S of random variables, it is required to find the finest partitioning {U1,…,Uk} of S into clusters such that the clusters U1,…,Uk are mutually independent. Since mutual independence is the target, pairwise …
The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning. Transfer learning is often based on the assumption that objects in both target and source domains share some common feature and/or data space. In this p…
Self-training avoids spurious features in domain adaptation.
problem Domain shift with large differences between source and target domains.
method Entropy minimization on unlabeled target data, initialized with a source classifier.
result Entropy minimization avoids using spurious features in large domain shifts.
New method optimizes neural network learning by adjusting random parameters to target function features.
problem Difficulty in setting optimal random parameters for neural network learning.
method Adjusts sigmoid slopes and positions to target function features in a randomized learning method.
result Significantly better approximation of complex target functions compared to standard methods.
In this paper, we study the problem of transfer learning with the attribute data. In the transfer learning problem, we want to leverage the data of the auxiliary and the target domains to build an effective model for the classification problem in the target domain. Meanwhile, the attributes are naturally stable cross d…
Enhances source domain knowledge with target data for transfer learning.
problem Limited data in target domains and rigid model assumptions in transfer learning.
method Transfer learning through Enhanced Sufficient Representation (TESR).
result TESR enhances source domain knowledge with target data, improving transfer learning performance.
Method separates data into class and style factors using semi-supervised learning.
problem Separating generative factors of data into class and style vectors.
method Independent Vector Variational Autoencoders with semi-supervised learning and independence term.
result Improves classification performance and generation controllability.
Estimates population size using capture-recapture designs with binary indicators.
problem Estimating population size from capture-recapture data with binary indicators.
method Proposes a modern method using undersmoothed lasso model to estimate the target parameter of interest.
result The choice of constraint on the K-dimensional distribution significantly impacts the value of the estimand.
Paper presents MTTDSC for better target-specific sentiment classification.
problem Improving accuracy in detecting and aggregating sentiments towards specific targets in social media.
method MTTDSC uses a multi-task learning approach with an auxiliary task for passage-level sentiment classification and a main task for target-specific sentiment classification.
result MTTDSC outperforms state-of-the-art baselines in sentiment classification.
ISAAC audits deep models for drug-target interactions, revealing structural differences.
problem Deep models for DTI often use irrelevant features, making them hard to evaluate.
method ISAAC uses intervention-based structural auditing to evaluate model sensitivity.
result ISAAC reveals significant structural differences in DTI models' reasoning.
Stein showed that the multivariate sample mean is outperformed by "shrinking" to a constant target vector. Ledoit and Wolf extended this approach to the sample covariance matrix and proposed a multiple of the identity as shrinkage target. In a general framework, independent of a specific estimator, we extend the shrink…
RLLS corrects label shifts between source and target domains.
problem Correcting label shifts between source and target domains.
method Estimate importance weights using labeled source data and unlabeled target data; train classifier on weighted source samples; derive generalization bound.
result Improves classification accuracy, especially in low sample and large-shift regimes.
The paper studies how quickly samples from Langevin dynamics become independent.
problem Understanding the dependence between samples along Langevin dynamics and related algorithms.
method Measures dependence via Φ-mutual information and proves strong data processing inequalities. result The Φ-mutual information between samples decreases exponentially to zero. SNAP efficiently identifies causal effects without needing full graph learning.
problem Efficiently estimating causal effects on a subset of variables.
method Sequential Non-Ancestor Pruning (SNAP) framework.
result SNAP reduces independence tests and computation time without sacrificing causal effect estimations.
Jeffrey guidance extends diffusion-model control to more complex applications.
problem Controlling diffusion models beyond simple cases like conditional sampling.
method Leveraging Jeffrey's rule of conditioning to update marginal distributions towards a target distribution.
result Significant reductions in FID on CIFAR-10 and FFHQ with Inception embeddings as the target.
Deep neural networks can generate any 2D distribution with high accuracy.
problem Generating accurate high-dimensional distributions from random noise.
method A deep neural network with a space-filling property of sawtooth functions.
result The network can approximate any 2D Lipschitz-continuous distribution arbitrarily closely.
Improves probability distribution compression with KT algorithm.
problem Efficiently compressing probability distributions.
method Kernel thinning (KT) algorithm with four improvements.
result KT yields tighter, dimension-free guarantees for any kernel.
ATL learns from many streaming processes without labeled data.
problem Knowledge transfer across many streaming processes with covariate shift and drifts.
method Autonomous transfer learning with generative and discriminative phases, KL divergence optimization, and elastic network structure.
result Improved performance and faster training speed compared to existing methods.
Quantile normalisation is a popular normalisation method for data subject to unwanted variations such as images, speech, or genomic data. It applies a monotonic transformation to the feature values of each sample to ensure that after normalisation, they follow the same target distribution for each sample. Choosing a "g…
Paper proposes singing voice conversion without parallel data.
problem Convert singing voices without parallel data.
method Phonetic posterior feature, DBLSTM, vocoder.
result Successfully converts singing voices without parallel data.
Generates samples from a target distribution using a discriminator.
problem Sampling from complex target distributions.
method Implicit Metropolis-Hastings algorithm using GAN discriminator.
result Discriminator loss bounds the distance to target distribution.
Theory explains how AI models can predict unseen tasks without labeled data.
problem Understanding how AI models can generalize to unseen tasks.
method Developed a theoretical framework to analyze zero-shot prediction.
result Identified key quantities and independence relationships for generalization.
The paper improves importance sampling and MCMC methods for complex distributions.
problem Improving sampling efficiency for distributions with atoms or heavy tails.
method Develops minimax optimal trial distributions and importance-tempered MCMC.
result Importance-tempered MCMC can be uniformly ergodic for certain distributions.
This work introduces a new method for coupling base and target densities in generative models.
problem Generating samples from complex target distributions using simple base distributions.
method Developed a framework of stochastic interpolants with data-dependent couplings.
result Constructing dynamical transport maps that serve as conditional generative models.
New method accelerates diffusion models for broader target distributions.
problem Current diffusion models have limited acceleration for certain target distributions.
method Developed a novel accelerated stochastic DDPM sampler.
result Achieved accelerated performance for three broad distribution classes.
CIRCE measures conditional independence for learning invariant features.
problem Learning invariant features while being conditionally independent of a distractor.
method CIRCE is a measure of conditional independence applied as a regularizer in feature learning.
result CIRCE provides a zero value if and only if features are conditionally independent of the distractor given the target.
Study improves regularity estimates for harmonic maps into ellipsoids.
problem Independence of regularity estimates on harmonic maps with varying target dimensions.
method Analyzes harmonic maps into ellipsoids, uses Palais-Smale sequences, and critical metrics.
result Enhanced regularity estimates for Laplace harmonic eigenmaps.