Machine Learning Blog | ML@CMU | Carnegie Mellon University

machine learning Research

Counterfactual predictions under runtime confounding

by Amanda Coston / April 16, 2021

Due to feasibility or ethical requirements, a prediction model may only access a subset of the confounding factors that affect both the decision and outcome. We propose a procedure for learning valid counterfactual predictions in this setting.…

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FATE machine learning Research

Tilted Empirical Risk Minimization

April 2, 2021

In this post, we describe a simple extension to empirical risk minimization---tilted ERM (TERM), which can be used for enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance, all in a unified framework.…

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machine learning Research

Carnegie Mellon University at the Conference on Fairness, Accountability, and Transparency (ACM FAccT 2021)

March 5, 2021

This week researchers from all across computer science, social sciences and humanities are gathering for the flagship conference of the emerging field of Fairness, Accountability and Transparency in algorithmic systems: FAccT. FAccT (previously FAT*) is dedicated to the inherent risks…

36 1160

deep learning federated learning machine learning Research

An Inferential Perspective on Federated Learning

February 19, 2021

TL;DR: motivated to better understand the fundamental tradeoffs in federated learning, we present a probabilistic perspective that generalizes and improves upon federated optimization and enables a new class of efficient federated learning algorithms.…

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machine learning Research

Representational Aspects of Depth and Conditioning in Normalizing Flows

December 18, 2020

Top and Bottom Right: RealNVP [3] uses checkerboard and channel-wise partitioning schemes in order to factor out parameters and ensure that there aren’t redundant partitions from previous layers. GLOW [4] uses an invertible 1×1 convolution which allows the partitioned to…

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machine learning Research

Carnegie Mellon University at NeurIPS 2020

December 4, 2020

Carnegie Mellon University is proud to present 88 papers at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), which will be held virtually this week. Our faculty and researchers are also giving invited talks at 7 workshops and…

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machine learning Research

Experiments with the ICML 2020 Peer-Review Process

December 1, 2020

The International Conference on Machine Learning (ICML) is a flagship machine learning conference that in 2020 received 4,990 submissions and managed a pool of 3,931 reviewers, area chairs, and expert reviewers. Given that the stakes in the review process are…

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deep learning machine learning machine translation natural language processing Research

On Learning Language-Invariant Representations for Universal Machine Translation

October 23, 2020

Despite the recent improvements in neural machine translation (NMT), training a large NMT model with hundreds of millions of parameters usually requires a collection of parallel corpora at a large scale, on the order of millions or even billions of…

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FATE machine learning Research

FACT Diagnostic: How to Better Understand Trade-offs Involving Group Fairness

October 12, 2020

Figure 1. The FACT diagnostic is a general framework that allows easier and flexible analyses of trade-offs between group fairness and predictive performance (type-1 trade-off), or among different types of group fairness definitions (type-2 trade-off). As machine learning continues to…

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artificial intelligence machine learning reinforcement learning Research

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement Learning

October 6, 2020

To operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly. Reinforcement learning has enabled artificial agents to solve complex tasks both in simulation and real world. However, it requires…

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