This is a list of my papers with known issues, including some discussion of the issues;

• Bala Kalyanasundaram, Kirk Pruhs: Constructing Competitive Tours from Local Information. Theor. Comput. Sci. 130(1): 125-138 (1994).  ICALP 1993: 102-113
• Explanation of the issues: We add a jump edge (x, y) when certain conditions are met that insure that we can afford to travel to vertex y from vertex x. These conditions may however later not hold. So later it may be too expensive to travel from x to y. A counter example to the correctness of the algorithm, as well as a fix are given in: Nicole Megow, Kurt Mehlhorn, Pascal Schweitzer: Online graph exploration: New results on old and new algorithms. Theor. Comput. Sci. 463: 62-72 (2012). ICALP (2) 2011: 478-489.
• Negative spin: Our algorithm was wrong.
• Positive spin: All the necessary ideas are there in the original paper, and they just needed to be applied with more care.
• Antonios Antoniadis, Neal Barcelo, Michael Nugent, Kirk Pruhs, Kevin Schewior, Michele Scquizzato: Chasing Convex Bodies and Functions. LATIN 2016: 68-81
• Explanation of the issues: The proof of Lemma 2, which reduces online convex optimization to online lazy convex body chasing, implicitly assumes radial symmetry for the convex function. It isn't too hard to come up with functions that do not have radial symmetry and that can not be decomposed into lazy convex bodies as in the proof.
• Negative spin: Convex functions with radial symmetry are very special, and this significantly weakens the evidence that online convex optimization isn't harder than online convex body chasing.
• Positive spin: There is no reason to think that radial symmetry makes online convex optimization any easier, and thus this does not significantly weak the evidence that online convex optimization isn't harder than online convex body chasing. And in fact the claimed reduction is possible, but it requires more sophisticated methods. See Chasing Convex Bodies with Linear Competitive Ratio C.J. Argue, Anupam Gupta, Guru Guruganesh, Ziye Tang and trace the references backwards if its not there.
  
• Jeff Edmonds, Kirk Pruhs: Balanced Allocations of Cake. FOCS 2006: 623-634. Jeff Edmonds, Kirk Pruhs, Jaisingh Solanki: Confidently Cutting a Cake into Approximately Fair Pieces. AAIM 2008: 155-164
• Explanation of issues: In trying to combine these papers into one journal paper, I came to the conclusion that some of the arguments need to be handled a bit more carefully. I thought all the tools/insights to do this were there, but it was a pain. So when I ended up getting busy, and this got set aside. And now its been enough time that I can't remember specifics of the issues.