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TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20270314T030000 RDATE:20271107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20290311T030000 RDATE:20291104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20300310T030000 RDATE:20301103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20310309T030000 RDATE:20311102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:25acae5e123e4eacd2d1e8cbed8e351f CATEGORIES:PhD Defense CREATED:20190823T084024 SUMMARY:Optimization in Sparse Learning: from Convexity to Non-convexity LOCATION:CBIM 22 DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Powerful machine learning models and large-scale training data motivate the rapid popularization of AI method in various applications such as data science, computer vision and natural language processing. The explosive mod el complexity and training data scale increase propose an urgent requiremen t for highly efficient model training algorithms. Optimization algorithm re search for model training, as a fundamental issue in machine learning, keep s on getting extensive attention from academia and industry.
In this presentation, I will introduce my research on optimization algorithm design and analysis for sparse model learning problems. The model learning object ive includes optimizing convex model with sparse inducing regularizer and m odel cardinality constrained minimization which is a non-convex problem. In addition to the single machine algorithms, I will also introduce my recent research progress in communication efficient distributed sparse model lear ning. The designed algorithm targeted for each specific problem significant ly improves the model training efficiency compared to baseline algorithms p> DTSTAMP:20240329T084409Z DTSTART;TZID=America/New_York:20181002T120000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR