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Tag: topic/machine-learning
64 items with this tag.
Sep 23, 2026
Q: Multi-armed and contextual bandits, Bayesian optimisation, sequential Bayesian experimental design / deep adaptive design, dynamic treatment regimes with Q- and A-learning, switchback experiments and RLHF all choose actions from accumulating data. Laid out on one map, what is each optimizing, what is the state, and what feedback does it assume?
type/qa
topic/multi-armed-bandits
topic/bayesian-experimental-design
topic/causal-inference
topic/online-experimentation
topic/large-language-models
topic/machine-learning
Sep 23, 2026
Q: Partial pooling shows up as hierarchical models, James–Stein / empirical Bayes shrinkage, global-local shrinkage priors, the Gamma-Gamma and NBD customer models, global forecasting models and LLM pretraining. What is the shared mechanism, and when does pooling hurt?
type/qa
topic/bayesian-statistics
topic/hierarchical-models
topic/machine-learning
topic/customer-lifetime-value
topic/forecasting
Sep 23, 2026
Q: Doubly-robust estimation appears in the vault as AIPW, the DML interactive-model score, the Callaway–Sant'Anna doubly-robust ATT(g,t), SDID's double robustness, the X-learner and weighted conformal prediction. What is the common structure, and in what sense is each one 'doubly' robust?
type/qa
topic/causal-inference
topic/econometrics
topic/treatment-effects
topic/machine-learning
topic/bayesian-statistics
Sep 23, 2026
Q: S-, T-, X- and R-learners, causal forests / GRF, BART and Bayesian causal forests, moderation analysis, hierarchical models and conformal ITE intervals all address heterogeneous treatment effects. Which question does each actually answer, and what does its uncertainty interval mean?
type/qa
topic/treatment-effects
topic/causal-inference
topic/machine-learning
topic/uncertainty-quantification
topic/conformal-prediction
Sep 23, 2026
Neural Simulation-Based Inference - Overview
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/overview
doc/paper
Sep 23, 2026
Neural Simulation-Based Inference - Index
type/index
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
Sep 23, 2026
Asymptotic Normality and Inference for Forests
source/ingested
topic/causal-inference
topic/machine-learning
topic/random-forests
topic/asymptotics
type/theorem
doc/paper
Sep 23, 2026
Causal Machine Learning - Overview
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
topic/semiparametric-inference
type/overview
doc/paper
Sep 23, 2026
Causal Machine Learning - Index
type/index
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
Sep 23, 2026
Conformal Prediction - Index
type/index
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
Sep 23, 2026
Chain-of-Thought Prompting
source/ingested
topic/machine-learning
topic/large-language-models
topic/prompting
type/method
doc/paper
Sep 23, 2026
Evaluating LLM Systems - Benchmarks, Hallucination and Human Preference
source/ingested
topic/machine-learning
topic/large-language-models
topic/model-evaluation
type/concept
doc/paper
Sep 23, 2026
LLM Reasoning, Retrieval and Agents - Overview
source/ingested
topic/machine-learning
topic/large-language-models
topic/llm-agents
type/overview
doc/paper
Sep 23, 2026
RLHF and Instruction Tuning
source/ingested
topic/machine-learning
topic/large-language-models
topic/rlhf
topic/reinforcement-learning
type/method
doc/paper
Sep 23, 2026
ReAct - Reasoning and Acting Agents
source/ingested
topic/machine-learning
topic/large-language-models
topic/llm-agents
type/method
doc/paper
Sep 23, 2026
Retrieval-Augmented Generation (RAG)
source/ingested
topic/machine-learning
topic/large-language-models
topic/retrieval
type/method
doc/paper
Sep 23, 2026
Reward Modeling from Human Preferences
source/ingested
topic/machine-learning
topic/large-language-models
topic/rlhf
topic/preference-learning
type/concept
doc/paper
Sep 23, 2026
Tool Use and the Agent Loop
source/ingested
topic/machine-learning
topic/large-language-models
topic/llm-agents
type/concept
doc/paper
Sep 23, 2026
LLM Reasoning, Retrieval and Agents - Index
type/index
source/ingested
topic/machine-learning
topic/large-language-models
topic/llm-agents
Sep 23, 2026
Probabilistic Forecasting - Index
type/index
source/ingested
topic/forecasting
topic/time-series
topic/machine-learning
Sep 23, 2026
Transformers and LLM Foundations - Index
type/index
source/ingested
topic/machine-learning
topic/transformers
topic/large-language-models
Sep 18, 2026
Q: Are pre-registration, multiplicity control, hierarchical partial pooling, cross-fitting, honest trees and train/calibration splits all the same cure for the garden of forking paths?
type/qa
topic/research-methodology
topic/multiple-comparisons
topic/machine-learning
topic/causal-inference
topic/bayesian-workflow
Sep 18, 2026
Metalearner Simulation Results
source/ingested
topic/causal-inference
topic/treatment-effects
topic/machine-learning
type/example
doc/paper
Sep 18, 2026
Metalearners for CATE
source/ingested
topic/causal-inference
topic/treatment-effects
topic/machine-learning
type/concept
type/definition
doc/paper
Sep 18, 2026
S-Learner
source/ingested
topic/causal-inference
topic/treatment-effects
topic/machine-learning
type/concept
type/definition
doc/paper
Sep 18, 2026
T-Learner and Minimax Rate
source/ingested
topic/causal-inference
topic/treatment-effects
topic/machine-learning
type/concept
type/theorem
doc/paper
Sep 18, 2026
X-Learner
source/ingested
topic/causal-inference
topic/treatment-effects
topic/machine-learning
type/concept
type/theorem
doc/paper
Sep 18, 2026
Amortized vs Sequential Inference
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/concept
doc/paper
Sep 18, 2026
Benchmarking and Diagnosing SBI (SBC, Coverage, C2ST)
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Neural Likelihood Estimation and Sequential Neural Likelihood
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Neural Posterior Estimation (NPE)
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Neural Ratio Estimation
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Neural SBI for Agent-Based and Economic Models
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/agent-based-modeling
topic/machine-learning
type/application
doc/paper
Sep 18, 2026
Normalizing Flows as Conditional Density Estimators
source/ingested
topic/bayesian-statistics
topic/likelihood-free-inference
topic/machine-learning
type/concept
doc/paper
Sep 18, 2026
Normalizing Flows for Variational Inference
source/ingested
topic/bayesian-statistics
topic/variational-inference
topic/machine-learning
topic/normalizing-flows
type/method
doc/paper
Sep 18, 2026
Reparameterization Trick and Variational Autoencoders
source/ingested
topic/bayesian-statistics
topic/variational-inference
topic/machine-learning
topic/deep-generative-models
type/method
doc/paper
Sep 18, 2026
Cross-Fitting and Sample Splitting
source/ingested
topic/causal-inference
topic/machine-learning
topic/semiparametric-inference
type/method
doc/paper
Sep 18, 2026
DML Estimators for ATE and the Interactive Model
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
type/method
type/theorem
doc/paper
Sep 18, 2026
Generalized Random Forests - Local Moment Equations
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
topic/random-forests
type/method
doc/paper
Sep 18, 2026
Honest Trees and Causal Forests
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
topic/random-forests
type/method
doc/paper
Sep 18, 2026
Neyman Orthogonality
source/ingested
topic/causal-inference
topic/machine-learning
topic/semiparametric-inference
type/concept
type/theorem
doc/paper
Sep 18, 2026
R-Learner and Orthogonal CATE Estimation
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
type/method
doc/paper
Sep 18, 2026
Regularization Bias and the Partially Linear Model
source/ingested
topic/causal-inference
topic/machine-learning
topic/semiparametric-inference
type/concept
doc/paper
Sep 18, 2026
Conformal Inference for Counterfactuals and ITEs
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/causal-inference
topic/uncertainty-quantification
type/application
doc/paper
Sep 18, 2026
Conformal Prediction - Overview
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/overview
doc/paper
Sep 18, 2026
Conformal Prediction Under Covariate Shift
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
topic/distribution-shift
type/method
doc/paper
Sep 18, 2026
Conformalized Quantile Regression
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
topic/regression
type/method
doc/paper
Sep 18, 2026
Marginal vs Conditional Coverage
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/concept
doc/paper
Sep 18, 2026
Split Conformal Prediction and the Coverage Guarantee
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/method
doc/paper
Sep 18, 2026
DeepAR and Global Autoregressive Neural Forecasters
source/ingested
topic/forecasting
topic/time-series
topic/deep-learning
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Forecast Evaluation and Backtesting
source/ingested
topic/forecasting
topic/time-series
topic/model-evaluation
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Local vs Global Forecasting Models
source/ingested
topic/forecasting
topic/time-series
topic/machine-learning
type/concept
doc/paper
Sep 18, 2026
Probabilistic Forecasting - Overview
source/ingested
topic/forecasting
topic/time-series
topic/machine-learning
type/overview
doc/paper
Sep 18, 2026
Proper Scoring Rules (CRPS, Log Score, Pinball Loss)
source/ingested
topic/forecasting
topic/scoring-rules
topic/machine-learning
type/concept
doc/paper
Sep 18, 2026
Time-Series Foundation Models (Chronos)
source/ingested
topic/forecasting
topic/time-series
topic/foundation-models
topic/transformers
topic/machine-learning
type/method
doc/paper
Sep 18, 2026
Autoregressive Language Modeling and Pretraining
source/ingested
topic/machine-learning
topic/large-language-models
topic/language-modeling
type/concept
doc/paper
Sep 18, 2026
Compute-Optimal Training (Chinchilla)
source/ingested
topic/machine-learning
topic/large-language-models
topic/scaling-laws
topic/resource-allocation
type/method
doc/paper
Sep 18, 2026
In-Context Learning and Few-Shot Prompting
source/ingested
topic/machine-learning
topic/large-language-models
topic/in-context-learning
topic/prompting
type/concept
doc/paper
Sep 18, 2026
Neural Scaling Laws
source/ingested
topic/machine-learning
topic/large-language-models
topic/scaling-laws
type/concept
doc/paper
Sep 18, 2026
Transformers and LLM Foundations - Overview
source/ingested
topic/machine-learning
topic/transformers
topic/large-language-models
type/overview
doc/paper
Sep 18, 2026
Conformity Scores and Adaptive Prediction Sets
source/ingested
topic/machine-learning
topic/conformal-prediction
topic/uncertainty-quantification
type/concept
doc/paper
Sep 18, 2026
Scaled Dot-Product and Multi-Head Attention
source/ingested
topic/machine-learning
topic/transformers
topic/attention
type/concept
doc/paper
Sep 18, 2026
Transformer Architecture and Positional Encoding
source/ingested
topic/machine-learning
topic/transformers
topic/neural-network-architecture
type/concept
doc/paper
Apr 11, 2026
Künzel 2019 - Overview
source/ingested
topic/causal-inference
topic/treatment-effects
topic/machine-learning
type/overview
doc/paper