kw2934 / ARMULLinks
Adaptive and Robust Multi-Task Learning
☆10Updated last year
Alternatives and similar repositories for ARMUL
Users that are interested in ARMUL are comparing it to the libraries listed below
Sorting:
- SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks☆13Updated 2 years ago
- ☆13Updated 5 years ago
- Accompanying code for our NeurIPS 2019 paper☆12Updated 6 years ago
- A straightforward implementation of EGBM-based Generalized Additive Model☆14Updated 5 years ago
- ☆32Updated 7 years ago
- Enhanced Explainable Neural Network☆10Updated 3 years ago
- A novel general non-stationary point process model based on neural networks.☆11Updated 3 years ago
- An optimization-based algorithm to accurately estimate the causal effects and robustly predict under distribution shifts. It leverages th…☆14Updated last year
- ☆39Updated 6 years ago
- ☆10Updated 3 years ago
- Official Implementation of "Doubly Mixed-Effects Gaussian Process Regression" (Jun Ho Yoon, Daniel P. Jeong, Seyoung Kim) (AISTATS 2022, …☆13Updated 3 years ago
- ☆14Updated 2 years ago
- ☆43Updated 7 years ago
- A community repository for benchmarking Bayesian methods☆12Updated 2 years ago
- A comparison of some conformal quantile regression methods.☆14Updated 6 years ago
- Code for the paper "Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers" published in ICLR 2019☆14Updated 6 years ago
- This repository is the implementation of Deep Dirichlet Process Mixture Models (UAI 2022)☆13Updated 3 years ago
- Online Convex Optimization algorithms in Python☆12Updated 3 years ago
- Code to reproduce the numerical experiments in the paper Domain adaptation under structural causal models by Yuansi Chen and Peter Bühlma…☆18Updated 4 years ago
- Code for the paper "XAI Beyond Classification: Interpretable Neural Clustering" (JMLR 2022)☆13Updated 3 years ago
- Code for the NIPS 2018 paper "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"☆18Updated 4 years ago
- Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)☆21Updated 2 years ago
- Code for our ICLR19 paper "Wasserstein Barycenters for Model Ensembling", Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Cicero…☆22Updated 6 years ago
- Codebase for the Paper "Deep Semi-supervised Learning (SSL) for Time Series Classification (TSC)" to appear at the ICMLA '21☆12Updated 3 years ago
- MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".☆18Updated 2 years ago
- Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems☆23Updated 3 years ago
- Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting☆12Updated 2 years ago
- Repository for Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification (NeurIPS 2024)☆44Updated 11 months ago
- Code accompanying our ICML 2020 paper on choice set optimization in group decision-making.☆11Updated 5 years ago
- Connecting Interpretability and Robustness in Decision Trees through Separation☆16Updated 4 years ago