JMLR<p>'The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise', by Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang.</p><p><a href="http://jmlr.org/papers/v26/24-0100.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v26/24-0100.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/stochastic" class="mention hashtag" rel="tag">#<span>stochastic</span></a> <a href="https://sigmoid.social/tags/stochastically" class="mention hashtag" rel="tag">#<span>stochastically</span></a> <a href="https://sigmoid.social/tags/martingale" class="mention hashtag" rel="tag">#<span>martingale</span></a></p>
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#stochastically
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JMLR<p>'Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance', by Jordan Awan, Aishwarya Ramasethu.</p><p><a href="http://jmlr.org/papers/v25/23-1624.html" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">http://</span><span class="ellipsis">jmlr.org/papers/v25/23-1624.ht</span><span class="invisible">ml</span></a> <br /> <br /><a href="https://sigmoid.social/tags/privacy" class="mention hashtag" rel="tag">#<span>privacy</span></a> <a href="https://sigmoid.social/tags/stochastically" class="mention hashtag" rel="tag">#<span>stochastically</span></a> <a href="https://sigmoid.social/tags/stochastic" class="mention hashtag" rel="tag">#<span>stochastic</span></a></p>
Published papers at TMLR<p>Expected Worst Case Regret via Stochastic Sequential Covering</p><p>Changlong Wu, Mohsen Heidari, Ananth Grama, Wojciech Szpankowski</p><p>Action editor: Shinichi Nakajima.</p><p><a href="https://openreview.net/forum?id=H1SekypXKA" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=H1Seky</span><span class="invisible">pXKA</span></a></p><p><a href="https://sigmoid.social/tags/stochastically" class="mention hashtag" rel="tag">#<span>stochastically</span></a> <a href="https://sigmoid.social/tags/stochastic" class="mention hashtag" rel="tag">#<span>stochastic</span></a> <a href="https://sigmoid.social/tags/regret" class="mention hashtag" rel="tag">#<span>regret</span></a></p>
New Submissions to TMLR<p>Expected Worst Case Regret via Stochastic Sequential Covering</p><p><a href="https://openreview.net/forum?id=H1SekypXKA" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="ellipsis">openreview.net/forum?id=H1Seky</span><span class="invisible">pXKA</span></a></p><p><a href="https://sigmoid.social/tags/stochastically" class="mention hashtag" rel="tag">#<span>stochastically</span></a> <a href="https://sigmoid.social/tags/stochastic" class="mention hashtag" rel="tag">#<span>stochastic</span></a> <a href="https://sigmoid.social/tags/regret" class="mention hashtag" rel="tag">#<span>regret</span></a></p>
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