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  • Withdraw - OpenReview
    Review: The paper is a theoretical analysis of adversarial robustness of Graph Laplacian based semi-supervised learning Pros: The paper addresses an important problem of adversarial robustness by exploiting unlabeled data It studies the theoretical robustness of graph Laplacian approach Cons: The attack model is not very well motivated and connected to some realistic situations The
  • Evaluating the Robustness of Neural Networks: An Extreme Value. . .
    Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks
  • Latent Visual Reasoning - OpenReview
    Furthermore, the methodology is well-conceived, featuring a logical two-stage training pipeline and a clever technical adaptation of reinforcement learning (GRPO_latent) to handle reasoning in a continuous latent space
  • The Clever Hans Mirage: A Comprehensive Survey on Spurious. . .
    This survey on spurious correlations uses the Clever Hans metaphor to motivate the problem, formalizes a group-based setup g= (y,a) with core metrics (worst-group, average-group, bias-conflicting), and explains why models latch onto shortcuts (simplicity bias, training dynamics)
  • EVALUATING THE ROBUSTNESS OF NEURAL NET : A E VALUE THEORY APPROACH
    ABSTRACT The robustness of neural networks to adversarial examples has received great attention due to security implications Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness In this paper, we provide a theoretical justification for converting robustness analysis into a local
  • Clever: A Curated Benchmark for Formally Verified Code Generation
    1 We introduce CLEVER, the first curated benchmark for evaluating the generation of specifications and formally verified code in Lean The benchmark comprises of 161 programming problems; it evaluates both formal speci- fication generation and implementation synthesis from natural language, requiring formal correctness proofs for both
  • On the Planning Abilities of Large Language Models : A Critical . . .
    While, as we mentioned earlier, there can be thorny “clever hans” issues about humans prompting LLMs, an automated verifier mechanically backprompting the LLM doesn’t suffer from these We tested this setup on a subset of the failed instances in the one-shot natural language prompt configuration using GPT-4, given its larger context window
  • Djork-Arné Clevert - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • CLEVER: A Curated Benchmark for Formally Verified Code Generation
    TL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean It requires full formal specs and proofs No few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning





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