An important problem today is how to allow multiple distributed
entities to train a shared neural network on their private data
while protecting data privacy. Federated learning is a standard
framework for distributed deep learning Federated...
In the last couple of years, a lot of progress has been made to
enhance robustness of models against adversarial attacks. However,
two major shortcomings still remain: (i) practical defenses are
often vulnerable against strong “adaptive” attack...
Some believe that truly effective and efficient reinforcement
learning algorithms must explicitly construct and explicitly reason
with models that capture the causal structure of the world. In
short, model-based reinforcement learning is not...
In this talk I will discuss two lines of work involving learning
in the presence of biased data and strategic behavior. In the
first, we ask whether fairness constraints on learning algorithms
can actually improve the accuracy of the classifier...
Langevin diffusions are continuous-time stochastic processes
that are based on the gradient of a potential function. As such
they have many connections---some known and many still to be
explored---to gradient-based machine learning. I'll discuss...
Large-scale vision benchmarks have driven---and often even
defined---progress in machine learning. However, these benchmarks
are merely proxies for the real-world tasks we actually care about.
How well do our benchmarks capture such tasks?
Epidemiological forecasting is critically needed for decision
making by national and local governments, public health officials,
healthcare institutions and the general public. The Delphi group at
Carnegie Mellon University was founded in 2012 to...
In deep generative models, the latent variable is generated by a
time-inhomogeneous Markov chain, where at each time step we pass
the current state through a parametric nonlinear map, such as a
feedforward neural net, and add a small independent...
AlphaZero learns to play go, chess and shogi at a superhuman
level through self play given only the rules of the game. This
raises the question of whether a similar thing could be done for
mathematics --- a MathZero. MathZero would require a formal...
Existing generative models are typically based on explicit
representations of probability distributions (e.g., autoregressive
or VAEs) or implicit sampling procedures (e.g., GANs). We propose
an alternative approach based on modeling directly the...
