text: 184,78,1506020820769914880,45724845,Swarat Chaudhuri,"How do you learn neural networks that respect end-to-end safety requirements of larger systems of which they are a part? Our new ICLR paper, led by @ChenxiYang001 (<LINK>), explores this question. <LINK> (1/n) <LINK> The SOTA answer to the question is to train the NNs first, then show that the resulting system is safe. But why would you expect it to safe if the NNs haven't seen the property during training? And what do you do if safety checking fails? (2/n) Our answer: give the learner access to a signal from a formal safety analyzer during training. This idea was previously used to prove properties of isolated NNs: <LINK>. Does it also apply to systems where NNs coexist with nondifferentiable symbolic code? (3/n) Our paper takes a first step on this problem. The main ideas are to compute a safety loss using a probabilistic symbolic executor and to backprop gradients of this loss through nondifferentiable operations using a stochastic gradient estimator. (4/n) I am really excited about this direction. The distinction between ""ML/AI"" and ""software"" is spurious in 2022 -- soon, all systems will have ML components. Since you can't debug NNs manually, connecting learning and verification is key. (5/n) And there are so many open technical challenges! We need better sound approximation techniques for NNs. We need to figure out how to handle soft requirements and unknown unknowns. We need to balance precision and scalability. (6/n) Fortunately, more and more folks are working on FM + ML, so I am optimistic about the field. Feel free to ping us if you want to chat about the area or our work. (7/7)",https://arxiv.org/abs/2203.07671,"We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many safety-critical domains. However, because they can use nondifferentiable operations, it is hard to learn their parameters using existing gradient-based approaches to safe learning. Our approach to this problem, Differentiable Symbolic Execution (DSE), samples control flow paths in a program, symbolically constructs worst-case ""safety losses"" along these paths, and backpropagates the gradients of these losses through program operations using a generalization of the REINFORCE estimator. We evaluate the method on a mix of synthetic tasks and real-world benchmarks. Our experiments show that DSE significantly outperforms the state-of-the-art DiffAI method on these tasks. ",Safe Neurosymbolic Learning with Differentiable Symbolic Execution,7,"['How do you learn neural networks that respect end-to-end safety requirements of larger systems of which they are a part? Our new ICLR paper, led by @ChenxiYang001 (<LINK>), explores this question. <LINK> (1/n) <LINK>', ""The SOTA answer to the question is to train the NNs first, then show that the resulting system is safe. But why would you expect it to safe if the NNs haven't seen the property during training? And what do you do if safety checking fails? (2/n)"", 'Our answer: give the learner access to a signal from a formal safety analyzer during training. This idea was previously used to prove properties of isolated NNs: https://t.co/31vipcjNZl. Does it also apply to systems where NNs coexist with nondifferentiable symbolic code? (3/n)', 'Our paper takes a first step on this problem. The main ideas are to compute a safety loss using a probabilistic symbolic executor and to backprop gradients of this loss through nondifferentiable operations using a stochastic gradient estimator.  (4/n)', 'I am really excited about this direction. The distinction between ""ML/AI"" and ""software"" is spurious in 2022 -- soon, all systems will have ML components. Since you can\'t debug NNs manually, connecting learning and verification is key. (5/n)', 'And there are so many open technical challenges! We need better sound approximation techniques for NNs. We need to figure out how to handle soft requirements and unknown unknowns. We need to balance precision and scalability. (6/n)', 'Fortunately, more and more folks are working on FM + ML, so I am optimistic about the field. Feel free to ping us if you want to chat about the area or our work. (7/7)']",22,03,1616