174 citations · 766 across the 37 of their papers we have counts for
11 papers · 1 filter
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
Aldo Pacchiano, Christoph Dann, Claudio Gentile +1
We propose a simple model selection approach for algorithms in stochastic bandit and reinforcement learning problems. As opposed to prior work that (implicitly) assumes knowledge o…
Optimal Mean Estimation without a Variance
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett +1
We study the problem of heavy-tailed mean estimation in settings where the variance of the data-generating distribution does not exist. Concretely, given a sample $\mathbf{X} = \{X…
When does gradient descent with logistic loss find interpolating two-layer networks?
Niladri S. Chatterji, Philip M. Long, Peter L. Bartlett
We study the training of finite-width two-layer smoothed ReLU networks for binary classification using the logistic loss. We show that gradient descent drives the training loss to…
Failures of model-dependent generalization bounds for least-norm interpolation
Peter L. Bartlett, Philip M. Long
We consider bounds on the generalization performance of the least-norm linear regressor, in the over-parameterized regime where it can interpolate the data. We describe a sense in…
Optimal Robust Linear Regression in Nearly Linear Time
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni +3
We study the problem of high-dimensional robust linear regression where a learner is given access to samples from the generative model (with $X…
Accelerated Message Passing for Entropy-Regularized MAP Inference
Jonathan N. Lee, Aldo Pacchiano, Peter Bartlett +1
Maximum a posteriori (MAP) inference in discrete-valued Markov random fields is a fundamental problem in machine learning that involves identifying the most likely configuration of…