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<a href="https://zoom.us/j/717069575">https://zoom.us/j/717069575</a></b></span>
</font></font></span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-family:verdana,sans-serif"> </span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-family:verdana,sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><b>Who: </b> Filip Hanzely, KAUST<br></font></font></span></p><p class="MsoNormal" style="margin:0in 0in 0.0001pt;text-align:justify;line-height:normal;background-image:initial;background-position:initial;background-size:initial;background-repeat:initial;background-origin:initial;background-clip:initial"><span style="font-family:verdana,sans-serif"><font style="vertical-align:inherit"><font style="vertical-align:inherit"><br></font></font></span></p></div></div></div></div></div></div><div><span style="font-family:verdana,sans-serif">
</span><div><span style="font-family:verdana,sans-serif"><b>
</b></span><div><div><span style="font-family:verdana,sans-serif"><b>Title:</b> One Method to Rule Them All: Variance Reduction for Data, Parameters and Many New Methods</span></div><div><span style="font-family:verdana,sans-serif"><br></span></div><span style="font-family:verdana,sans-serif"><b>Abstract:</b>
We propose a remarkably general variance-reduced method suitable for
solving regularized empirical risk minimization problems with either a
large number of training examples, or a large model dimension, or both.
In special cases, our method reduces to several known and previously
thought to be unrelated methods, such as SAGA, LSVRG, JacSketch, SEG,
ISEGA, variance reduced local SGD and their arbitrary sampling and
proximal generalizations. However, we also highlight a large number of
new specific algorithms with interesting properties. We provide a single
theorem establishing linear convergence of the method under smoothness
and quasi strong convexity assumptions. With this theorem, we recover
best-known and sometimes improved rates for known methods arising in
special cases. Lastly, we show that variance reduced finite sum
optimization algorithms (i.e., SAGA) can be recovered as a special case
of variance reduced algorithms subsampling the space (i.e., SEGA). </span><br></div></div><div><br></div><div>__________________________________________________________________________________________________________________</div><div><br></div><div>Nati Srebro is inviting you to a scheduled Zoom meeting.<br><br>Topic: ML Talk at TTIC: Filip Hanzely<br>Time: Mar 20, 2020 10:30 AM Central Time (US and Canada)<br><font size="4"><span style="background-color:rgb(255,255,0)"><b><br>Join Zoom Meeting<br><a href="https://zoom.us/j/717069575">https://zoom.us/j/717069575</a></b></span><br><font size="2"><br>Meeting ID: 717 069 575</font><br></font><br>One tap mobile<br>+13126266799,,717069575# US (Chicago)<br>+16468769923,,717069575# US (New York)<br><br>Dial by your location<br> +1 312 626 6799 US (Chicago)<br> +1 646 876 9923 US (New York)<br> +1 253 215 8782 US<br> +1 301 715 8592 US<br> +1 346 248 7799 US (Houston)<br> +1 408 638 0968 US (San Jose)<br> +1 669 900 6833 US (San Jose)<br></div><div><br></div><div>Meeting ID: 717 069 575<br></div><div><br></div><div>Find your local number: <a href="https://zoom.us/u/acq5LpLBaY">https://zoom.us/u/acq5LpLBaY</a><br></div><div><br></div><div>Cheers,<br></div></div>
</div><div dir="ltr"><font face="arial, helvetica, sans-serif"><b><br></b></font></div><div dir="ltr"><font face="arial, helvetica, sans-serif"><b><br></b></font></div><div dir="ltr"><font face="arial, helvetica, sans-serif"><b>Jerome Allen</b><br></font><div><font face="arial, helvetica, sans-serif">Executive Assistant to the President<br></font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 518</font></div><div><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></div><div><font face="arial, helvetica, sans-serif">p:(773) 702-2311<br></font></div><div><i><b><a href="mailto:jallen@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">jallen@ttic.edu</font></a></b></i></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>