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Every paper Pith has read. Search by title, abstract, or pith.
900 papers in stat.ML · page 1
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Valiant learnability equals poly-size query compression
What is Learnable in Valiant's Theory of the Learnable?
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Package turns anomaly scores into calibrated p-values
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'
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Two auxiliary environments identify any nonlinear causal graph
Causal Learning with the Invariance Principle
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Adaptive internal preprocessing beats external searches on 42 of 57 NIRS datasets
Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
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Reused diffusion latents incur error from subspace misalignment
On the Limits of Latent Reuse in Diffusion Models
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Learned continuous perturbations boost LLM extrapolation to new domains
Learning Perturbations to Extrapolate Your LLM
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Change-point sample needs depend on both jumps and positions
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
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Entropy rises with missing context in LLMs
LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information
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The paper proposes a likelihood-free Bayesian filtering method using coupling-informed…
Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems
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Kernels on parameters deliver bounds for nonlinear BO models
Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
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GP construction keeps mean identical across repetitions with smooth variation
Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process
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Hallucination limits in AI imaging fixed by forward model alone
On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods
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Neural nets learn time series clusters from simulations
Amortized Neural Clustering of Time Series based on Statistical Features
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Wavelet DPPs deliver better minibatch variance reduction
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
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Pre-trained net selects kernels for high-dim density estimates
Adaptive Kernel Density Estimation with Pre-training
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Coreset surrogate equals Wasserstein gap in flow matching
Coreset-Induced Conditional Velocity Flow Matching
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Orthogonal transformations keep weight singular values fixed
Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation
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Sampler matches smooth-case rate for composite log-concave densities
A proximal gradient algorithm for composite log-concave sampling
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Bootstrap yields valid CIs for offline RL value functions
Model-based Bootstrap of Controlled Markov Chains
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Conformal prediction optimizes sets without data splits
Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting
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Online deferral algorithm manages varying experts with sublinear regret
Online Learning-to-Defer with Varying Experts
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Budget-coverage policy learning reduces to affine threshold rule
Optimal Policy Learning under Budget and Coverage Constraints
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Self-predicted data calibrates Bayesian regression better than Laplace
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
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Sequential CMI bounds adaptive generalization gaps
Information-Theoretic Generalization Bounds for Sequential Decision Making
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Score gradients cut simulation needs for neural surrogates
Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
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Laplacian neural operators learn PDE maps with polynomial cost
Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System
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GNNs output random sets over classes to quantify epistemic uncertainty
Random-Set Graph Neural Networks
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Anchor quantization stabilizes Schrödinger bridge couplings
QDSB: Quantized Diffusion Schr\"odinger Bridges
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LOFT improves orthogonal fine-tuning via task-aware support selection
LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
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Two anchors make reward variance identifiable from preferences
Variance-aware Reward Modeling with Anchor Guidance
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Kernel eigenvalue decay determines random forest rates
Minimax Rates and Spectral Distillation for Tree Ensembles
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W-Flow reaches 1.29 FID in one ImageNet generation step
One-Step Generative Modeling via Wasserstein Gradient Flows
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Sparse Bayesian KANs achieve near-minimax contraction
Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces
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Active label queries cut U-statistic variance with fixed budget
Learning U-Statistics with Active Inference
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Noise-subspace estimator matches minimax rate for probabilistic PLS
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
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Composite function stabilizes training of binary-activation networks
A Composite Activation Function for Learning Stable Binary Representations
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Post-ADC inference restores valid stats after adaptive sampling
Post-ADC Inference: Valid Inference After Active Data Collection
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Calibration algorithms adapt error bounds to unknown non-stationarity
Adaptive Calibration in Non-Stationary Environments
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Vector codebook cuts KV cache to 34x compression at 0.95 similarity
FibQuant: Universal Vector Quantization for Random-Access KV-Cache Compression
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Barrier smoothing yields O(K^{-2/3}) stationarity for constrained bilevel opt
A Barrier-Metric First-Order Method for Linearly Constrained Bilevel Optimization
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PPO reformulated to beat SAC in multi-task RL
TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing
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Adapter adds closed-form spatial covariance to frozen predictors
Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictors
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Causal model recovers recourse effects from observational data
Causal Algorithmic Recourse: Foundations and Methods
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Decompositions isolate bias pathways in generative models
Causal Bias Detection in Generative Artifical Intelligence
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Causal paths break down survival disparities over time
Causal Fairness for Survival Analysis
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Algorithm identifies ε-good subtrees without knowing ε
$\varepsilon$-Good Action Identification in Fixed-Budget Monte Carlo Tree Search
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Coupled noises lift diversity in diffusion batches at zero added cost
Couple to Control: Joint Initial Noise Design in Diffusion Models
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Dual form computes influence functions from data size not parameters
Extending Kernel Trick to Influence Functions
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Stable barcodes track how dependency clusters evolve in dynamic Bayesian networks
A Stable Distance Persistence Homology for Dynamic Bayesian Network Clustering
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Thompson sampling learns unknown networks while optimizing treatments
Adaptive Policy Learning Under Unknown Network Interference