text:   Networks",6,"['[1/4] Everybody knows adversarial examples are a problem, and a lot of people tried to provably verify NN robustness. But seems convex relaxation alone runs into a theoretical and empirical barrier --- not tight enough! See our new paper <LINK> <LINK>', '[2/4] This may be obvious to some, but at least 10 papers last year kept pushing convex relaxation, like @RICEric22 and @zicokolter’s LP method and recently @pushmeet’s January paper on interval bound propagation.', '[3/4] Algorithms that bypass this barrier include Raghunathan’s SDP formulations, MILP from Tedrake’s group, SMT (reluplex), Lipschitz constant bound, and hybrid approaches ← we encourage folks to explore these ideas further!', '[4/4] Collaboration with our super duper awesome AI resident @hadisalman94, along with Huan Zhang, Cho-Jui Hsieh, and Pengchuan Zhang. Also special thanks to @ilyaraz2!', '@zicokolter Thanks Zico! You are right, and indeed we discuss this more carefully in the conclusion of the paper: ""In general, none of [the above] are strictly better than the convex relaxation approach, sacrificing either speed or accuracy"" --- so 100% on the same page here!', '@zicokolter Yep big fans of the randomized smoothing @deepcohen (very impressed by this) and Wasserstein robustness @RICEric22 papers! :)']",19,02,1275