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Found 12 papers, 2 papers with code
Confidence Self-Calibration for Multi-Label Class-Incremental Learning
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unavailable.
Query lower bounds for log-concave sampling
Log-concave sampling has witnessed remarkable algorithmic advances in recent years, but the corresponding problem of proving lower bounds for this task has remained elusive, with lower bounds previously known only in dimension one.
Fisher information lower bounds for sampling
We prove two lower bounds for the complexity of non-log-concave sampling within the framework of Balasubramanian et al. (2022), who introduced the use of Fisher information (FI) bounds as a notion of approximate first-order stationarity in sampling.
Rejection sampling from shape-constrained distributions in sublinear time
We consider the task of generating exact samples from a target distribution, known up to normalization, over a finite alphabet.
The query complexity of sampling from strongly log-concave distributions in one dimension
We establish the first tight lower bound of $\Omega(\log\log\kappa)$ on the query complexity of sampling from the class of strongly log-concave and log-smooth distributions with condition number $\kappa$ in one dimension.
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm
Conventional wisdom in the sampling literature, backed by a popular diffusion scaling limit, suggests that the mixing time of the Metropolis-Adjusted Langevin Algorithm (MALA) scales as $O(d^{1/3})$, where $d$ is the dimension.
Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
We study community detection in the contextual stochastic block model arXiv:1807. 09596 [cs. SI], arXiv:1607. 02675 [stat. ME].
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence
Stein Variational Gradient Descent (SVGD), a popular sampling algorithm, is often described as the kernelized gradient flow for the Kullback-Leibler divergence in the geometry of optimal transport.
Exponential ergodicity of mirror-Langevin diffusions
Motivated by the problem of sampling from ill-conditioned log-concave distributions, we give a clean non-asymptotic convergence analysis of mirror-Langevin diffusions as introduced in Zhang et al. (2020).
Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor
Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks.
Robotics
Nonparametric Heterogeneous Treatment Effect Estimation in Repeated Cross Sectional Designs
Identifying heterogeneity in a population's response to a health or policy intervention is crucial for evaluating and informing policy decisions.
Methodology
Interplay of Sensor Quantity, Placement and System Dimensionality on Energy Sparse Reconstruction of Fluid Flows
In this effort, we explore the interplay of data sparsity, sparsity of the underlying flow system and sensor placement on energy sparse reconstruction performance enabled by data- driven SVD basis.
Computational Physics
