acmi-lab Goto Github PK

10732-robustness-adaptation-distribution-shift

chils

Code and results accompanying our paper titled CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets

cmu-10717-the-art-of-the-paper

Official repository for CMU Machine Learning Department's 10717: "The Art of the Paper".

cmu-10721-philosophy-machine-intelligence

Official repository for CMU Machine Learning Department's 10721: "Philosophical Foundations of Machine Intelligence".

cmu-10732-robustness-adaptivity-shift

Official repository for CMU Machine Learning Department's 10732: Robustness and Adaptivity in Shifting Environments

counterfactually-augmented-data

Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

evaluationovertime

Python package for Evaluating Medical Datasets Over Time (EMDOT).

imp-regularizers

Official implementation for the ICLR 2023 paper: Disentangling the Mechanisms Behind Implicit Regularization in SGD.

latent-label-shift-ddfa

Code and results accompanying our paper titled Unsupervised Learning under Latent Label Shift at NeurIPS 2022

local-causal-discovery

Code for the paper "Local Causal Discovery for Estimating Causal Effects".

missingness-shift

Code accompanying our paper "Domain Adaptation under Missingness Shift" at AISTATS 2023.

modular-summarization

code for modular summarization work published in ACL2021 by Krishna et al

mvp

Official Repository for the paper "Model-tuning Via Prompts Makes NLP Models More Robust"

online-moment-selection

Code for the paper "Efficient Online Estimation of Causal Effects by Deciding What to Observe".

onlinelabelshift

Code accompanying our paper titled Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms

open-set-label-shift

Code and results accompanying our paper titled Domain Adaptation under Open Set Label Shift

papers-worth-reading

A Curated Set of Papers Actually Worth Reading

pretraining-with-nonsense

Pretraining summarization models using a corpus of nonsense

pu_learning

Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)

ratt_generalization_bound

Code and results accompanying our paper titled RATT: Leveraging Unlabeled Data to guarantee generalization at ICML 2021 (Long Talk)

rlsbench

Code and results accompanying our paper titled RLSbench: Domain Adaptation under Relaxed Label Shift

self-pretrain

superheroes-deep-learning

Thrilling tales of heroic feats by ML's larger-than-life champions.