538 citations · 2.5k across the 90 of their papers we have counts for
7 papers · 2 filters
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Han Zhao, Chen Dan, Bryon Aragam +3
A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invaria…
The Risks of Invariant Risk Minimization
Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski
Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the tra…
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
Sijie He, Xinyan Li, Timothy DelSole +2
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would ha…
Evaluations and Methods for Explanation through Robustness Analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu +4
Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we e…
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen +3
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a la…
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar +1
Machine learning algorithms are known to be susceptible to data poisoning attacks, where an adversary manipulates the training data to degrade performance of the resulting classifi…