<div dir="ltr"><div><span style="font-size:12.8000001907349px">When: Thursday, Apr 23, 4:00PM</span></div><div><span style="font-size:12.8000001907349px">Where: LOM 215</span></div><span style="font-size:12.8000001907349px"><div><span style="font-size:12.8000001907349px"><br></span></div><div><span style="font-size:12.8000001907349px">Speaker: Sekhar Tatikonda (Yale)</span></div><div><span style="font-size:12.8000001907349px"><br></span></div>Title: Learning Sparse Gaussian Graphical Models</span><div><br style="font-size:12.8000001907349px"><span style="font-size:12.8000001907349px">Abstract:   We provide an algorithm and analysis for learning the</span><br style="font-size:12.8000001907349px"><span style="font-size:12.8000001907349px">topology of sparse Gaussian Markov random fields.  The notion of</span><br style="font-size:12.8000001907349px"><span style="font-size:12.8000001907349px">sparsity we consider is that of k-separability. </span><br><div><span style="font-size:12.8000001907349px"><br></span></div><div><span style="font-size:12.8000001907349px">For more info: <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__sites.google.com_a_yale.edu_combprob_home&d=AwMFaQ&c=-dg2m7zWuuDZ0MUcV7Sdqw&r=LF0MQT9lkPQlp3gHlW9D2fTc0d4apDhCuC758tavUvQ&m=vMPn8pLA-D9vN3LfAhlL5YEg91oNpf5wAuYL-ddm_zY&s=wIciEUf6Y7MenzK5lH8tVVivj5oG2Om7QC8Q-so3WyU&e=">https://sites.google.com/a/yale.edu/combprob/home</a></span></div><div><span style="font-size:12.8000001907349px"><br></span></div><div><span style="font-size:12.8000001907349px">  --Dan</span></div><div><span style="font-size:12.8000001907349px"><br></span></div></div></div>