There is increasing interest in computer science and elsewhere to understand and improve peer review (see here for an overview). With this motivation, we conducted two experiments regarding peer review which we summarize in this blog post. | Continue reading
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. | Continue reading
Can we build a bridge between the left and right hand side? As datasets continually increase in size and complexity, our ability to uncover meaningful insights from unstructured and unlabeled data is crucial. At the same time, a premium has been placed on delivering simple, human … | Continue reading
With the prevalence of machine learning applications in high-stakes domains (e.g., criminal judgment, medical testing, online advertising, etc.), it is crucial to ensure that these decision-support systems do not propagate existing bias or discrimination that might exist in histo … | Continue reading
In today's post, we will discuss an interesting property concerning the trajectory of gradient descent iterates, namely the length of the Gradient Descent curve. Let us assume we want to minimize the function shown in Figure 1 starting from a point (A). We deploy gradient descent … | Continue reading
Traditional wisdom in machine learning holds that there is a careful trade-off between training error and generalization gap. There is a "sweet spot" for the model complexity such that the model (i) is big enough to achieve reasonably good training error, and (ii) is small enough … | Continue reading
Automated decision-making is one of the core objectives of artificial intelligence. Not surprisingly, over the past few years, entire new research fields have emerged to tackle that task. This blog post is concerned with regret minimization, one of the central tools in online lea … | Continue reading
Machine learning is increasingly used to make critical decisions such as a doctor's diagnosis, a biologist's experimental design, and a lender's loan decision. In these areas, mistakes can be the difference between life and death, can lead to wasted time and money, and can have … | Continue reading
Machine translation is the problem of translating sentences from some source language to a target language. Neural machine translation (NMT), directly models the mapping of a source language to a target language without any need for training or tuning any component of the system … | Continue reading
CMU is a leader in the field of machine learning research, both within the Machine Learning Department (MLD) and across the university in general. Traditional conference and journal publications, along with technical talks, are our primary avenues for disseminating our research. … | Continue reading
The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University. | Continue reading