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Found 9 papers, 4 papers with code
Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
LLM unlearning aims to eliminate the influence of undesirable data from the pre-trained model while preserving the model's utilities on other tasks.
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
In settings where it is conjectured that no efficient algorithm can find this local neighborhood, we prove analogous geometric properties for a local minimizer of the TAP free energy reachable by AMP, and show that posterior inference based on this minimizer remains correctly calibrated.
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
This provides the first quantitative analysis of the ICRL capabilities of transformers pretrained from offline trajectories.
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to exhibit asymptotic normality for single coordinate estimation and have inflated error.
Plug-in Performative Optimization
A complementary family of solutions makes use of explicit \emph{models} for the feedback, such as best-response models in strategic classification, enabling significantly faster rates.
Semi-parametric inference based on adaptively collected data
Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals.
Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
In this paper, we develop near-optimal multiple testing procedures for high dimensional Bayesian linear models with isotropic covariates.
What causes the test error? Going beyond bias-variance via ANOVA
This leads to discovering the unimodality of variance as a function of the level of parametrization, and to decomposing the variance into that arising from label noise, initialization, and randomness in the training data to understand the sources of the error.
Second-order Information in First-order Optimization Methods
To the best of our knowledge, this is the first paper that seriously considers the necessity of square root among all adaptive methods.
