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A Kernel Perspective on Distillation-based Collaborative Learning
To tackle this problem, we rigorously analyze a representative distillation-based algorithm in the view of kernel regression.
Markov Equivalence and Consistency in Differentiable Structure Learning Chang Deng Kevin Bello
These results are then generalized to general models and likelihoods, where the same claims hold.
Noisy Dual Mirror Descent: A Near Optimal Algorithm for Jointly-DP Convex Resource Allocation
However, an emerging issue in allocation problems is data privacy.
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Then we propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies.
Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning
Recent successes in natural language processing have led to the proliferation of large language models (LLMs) by multiple providers. Each LLM offering has different inference accuracy, monetary cost, and latency, and their accuracy further depends on the exact wording of the question ( i .
Robust low-rank training via approximate orthonormal constraints
By modeling robustness in terms of the condition number of the neural network, we argue that this loss of robustness is due to the exploding singular values of the low-rank weight matrices.
