Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Boolean functions are perhaps the most basic objects of study in theoretical computer
science. They also arise in other areas of mathematics, including combinatorics, statistical …

We study the problem of fair classification within the versatile framework of Dwork et
al.[ITCS'12], which assumes the existence of a metric that measures similarity between pairs …

Quantum data access and quantum processing can make certain classically intractable
learning tasks feasible. However, quantum capabilities will only be available to a select few …

We give a polynomial-time algorithm for learning neural networks with one layer of sigmoids
feeding into any Lipschitz, monotone activation function (eg, sigmoid or ReLU). The …

Multicalibration is a notion of fairness for predictors that requires them to provide calibrated
predictions across a large set of protected groups. Multicalibration is known to be a distinct …

We give an n O (log log n)-time membership query algorithm for properly and agnostically
learning decision trees under the uniform distribution over {±1} n. Even in the realizable …

We create a computationally tractable learning algorithm for contextual bandits with
continuous actions having unknown structure. The new reduction-style algorithm composes …

We study the power of query access for the fundamental task of agnostic learning under the
Gaussian distribution. In the agnostic model, no assumptions are made on the labels of the …

Consider the following heuristic for building a decision tree for a function $ f:\{0, 1\}^ n\to\{\pm
1\} $. Place the most influential variable $ x_i $ of $ f $ at the root, and recurse on the …