Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment is no longer stationary, t...
Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron, and synaptic depression when the presynaptic neuron ...
Statistical language models estimate the probability of a word occurring in a given context. The most common language models rely on a discrete enumeration of predictive contexts ...
John Blitzer, Kilian Q. Weinberger, Lawrence K. Sa...
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unkno...
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Motor control depends on sensory feedback in multiple modalities with different latencies. In this paper we consider within the framework of reinforcement learning how different s...
Fredrik Bissmarck, Hiroyuki Nakahara, Kenji Doya, ...
We propose a convex optimization based strategy to deal with uncertainty in the observations of a classification problem. We assume that instead of a sample (xi, yi) a distributio...
Chiranjib Bhattacharyya, Pannagadatta K. Shivaswam...
We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes super...
Peter L. Bartlett, Michael Collins, Benjamin Taska...
We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suf...