AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,788 papers
Several novel results related to transfer learning are proved, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems and how the degree of probabilistic change in an algorithm using transfer places an upper bound on the...
This work formalizes update promotion as certified paired risk-difference auditing as well as building DISCERN, a sequential two-tier protocol that proves finite-sample validity and matching label-complexity bounds of order rho^2/eps^2 at the rate level.
This work introduces Counterfactual Harness Search and Evolution (CHASE), which casts harness evolution as constraint generation over valid counterfactual benchmarks and formalizes an ideal shortcut-neutralized benchmark $B_0$ and establishes theoretical guarantees linking finite counterfactual archives to $B_0$ and ch...
Clinical data sources such as electronic health records and wearable sensors record patient status repeatedly over follow-up, often at irregular times and on different schedules for different measurements. These data create opportunities for continuously updated, individualized risk prediction. Existing approaches, how...
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This work proposes a model-based imputation method that enables sampling from p via a sampling-importance-resampling (SIR) procedure, which can be further augmented with a joint diffusion model in the latent space, and achieves competitive performance compared to state-of-the-art procedures.
This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate noisy observations as well as attribute structures that may exhibit directional preferences. We introduce the notion of weak spherical symmetry, quantified by second-moment d...
It is shown that for every approximation bound that is valid for a set of multi-spike neural networks, there is an equivalent set of single-spike neural networks with only linearly more (or less) neurons, in the maximum number of spikes, for which the bound holds.
It is proved that the solution of LBA, EMA, LCA, PLSA is unique if and only if the solution of NMF is unique, which means existing uniqueness theorems for NMF directly apply to LBA, EMA, LCA, PLSA, and vice versa.
A novel class of expressive SDEs whose solutions are provably confined to a prescribed compact polyhedral state space, matching the domains of EMA data is developed, paving the way for principled, trustworthy continuous-time models of suicide risk and other clinical time series.
BASIS, a critic-free post-training algorithm designed to address the tradeoff between computational efficiency and sample efficiency in value estimation and policy learning, achieves performance close to multi-rollout GRPO-type baselines and often outperforms single-rollout REINFORCE-type baselines.
Efficient sampling from Boltzmann distributions over discrete variables is a fundamental operation in a wide range of applications. While fast non-MCMC samplers have recently emerged as promising alternatives to conventional MCMC methods, their practical use for probabilistic learning remains hindered by the difficulty...
Conformal policy learning (CPL) is proposed, a policy learning procedure with a new distribution-free safety guarantee that controls the probability of assigning treatment to an individual who would be harmed relative to control and achieves asymptotically optimal welfare subject to the safety constraint.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
As director, Bourg has focused on digital access, open and equitable scholarly publishing, and expanded support for data-intensive research.
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