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Microservices are foundational to modern distributed applications, enabling modular design and scalability. However, they face performance variability due to environmental factors like workload burstiness, resource contention, and shared dependencies. Existing microservice controllers, such as autoscalers and admission controllers, struggle to ensure good performance, often causing several Service Level Objective (SLO) violations. We argue that this is because controller decision-making is uninformed, lacking guidance about robustness to environmental factors. We propose the concept of run-time “performance robustness certificates” (PERCs) to address this limitation. A PERC provides statistical bounds on tail latencies of specific request types under a range of environmental perturbations. We show how to leverage a queueing-theoretic model of microservice performance to quickly derive actionable PERCs. We introduce Galileo, a framework that integrates PERCs with two state-of-the-art learned controllers to guide robust actions toward meeting SLOs. Experimental results with real-world benchmarks validate the effectiveness of PERCs in ensuring robust microservice performance. 

Generative models transform random noise into images, while their inversion aims to reconstruct structured noise for recovery and editing. This paper addresses two key tasks: (i) inversion and (ii) editing of real images using stochastic equivalents of rectified flow models (e.g., Flux). While Diffusion Models (DMs) dominate the field of generative modeling for images, their inversion suffers from faithfulness and editability challenges due to nonlinear drift and diffusion. Existing DM inversion methods require costly training of additional parameters or test-time optimization of latent variables. Rectified Flows (RFs) offer a promising alternative to DMs, yet their inversion remains underexplored. We propose RF inversion using dynamic optimal control derived via a linear quadratic regulator, and prove that the resulting vector field is equivalent to a rectified stochastic differential equation. We further extend our framework to design a stochastic sampler for Flux. Our method achieves state-of-the-art performance in zero-shot inversion and editing, surpassing prior works in stroke-to-image synthesis and semantic image editing, with large-scale human evaluations confirming user preference. See our project page https://rf-inversion.github.io/ for code and demo. 

In pretraining data detection, the goal is to detect whether a given sentence is in the dataset used for training a Large Language Model LLM). Recent methods (such as Min-K % and Min-K%++) reveal that most training corpora are likely contaminated with both sensitive content and evaluation benchmarks, leading to inflated test set performance. These methods sometimes fail to detect samples from the pretraining data, primarily because they depend on statistics composed of causal token likelihoods. We introduce Infilling Score, a new test-statistic based on non-causal token likelihoods. Infilling Score can be computed for autoregressive models without re-training using Bayes rule. A naive application of Bayes rule scales linearly with the vocabulary size. However, we propose a ratio test-statistic whose computation is invariant to vocabulary size. Empirically, our method achieves a significant accuracy gain over state-of-the-art methods including Min-K%, and Min-K%++ on the WikiMIA benchmark across seven models with different parameter sizes. Further, we achieve higher AUC compared to reference-free methods on the challenging MIMIR benchmark. Finally, we create a benchmark dataset consisting of recent data sources published after the release of Llama-3; this benchmark provides a statistical baseline to indicate potential corpora used for Llama-3 training. 

We study a variant of the contextual bandit problem where an agent can intervene through a set of stochastic expert policies. Given a fixed context, each expert samples actions from a fixed conditional distribution. The agent seeks to remain competitive with the “best” among the given set of experts. We propose the Divergence-based Upper Confidence Bound (D-UCB) algorithm that uses importance sampling to share information across experts and provide horizon-independent constant regret bounds that only scale linearly in the number of experts. We also provide the Empirical D-UCB (ED-UCB) algorithm that can function with only approximate knowledge of expert distributions. Further, we investigate the episodic setting where the agent interacts with an environment that changes over episodes. Each episode can have different context and reward distributions resulting in the best expert changing across episodes. We show that by bootstrapping from\(\mathcal {O}(N\log (NT^2\sqrt {E}))\)samples, ED-UCB guarantees a regret that scales as\(\mathcal {O}(E(N+1) + \frac{N\sqrt {E}}{T^2})\)forNexperts overEepisodes, each of lengthT. We finally empirically validate our findings through simulations. 

An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This approach yields strong downstream performance in a variety of contexts, demonstrating that multitask pretraining leads to effective feature learning. Although several recent theoretical studies have shown that shallow NNs learn meaningful features when either (i) they are trained on a single task or (ii) they are linear, very little is known about the closer-to-practice case of nonlinear NNs trained on multiple tasks. In this work, we present the first results proving that feature learning occurs during training with a nonlinear model on multiple tasks. Our key insight is that multi-task pretraining induces a pseudo-contrastive loss that favors representations that align points that typically have the same label across tasks. Using this observation, we show that when the tasks are binary classification tasks with labels depending on the projection of the data onto an r-dimensional subspace within the d k r-dimensional input space, a simple gradient-based multitask learning algorithm on a two-layer ReLU NN recovers this projection, allowing for generalization to downstream tasks with sample and neuron complexity independent of d. In contrast, we show that with high probability over the draw of a single task, training on this single task cannot guarantee to learn all r ground-truth features. 

The authors propose Reference-Based Modulation (RB-Modulation), a plug-and-play, training-free solution for personalization of diffusion models. Existing training-free methods face challenges in (a) extracting style from reference images without additional style or content text descriptions, (b) avoiding unwanted content leakage from style references, and (c) composing style and content effectively. RB-Modulation addresses these issues using a novel stochastic optimal controller, where a style descriptor encodes the desired attributes through a terminal cost. The induced drift ensures high fidelity to the reference style while adhering to the text prompt. Additionally, the authors introduce a cross-attention-based feature aggregation scheme that decouples content and style from the reference image. With both theoretical justification and empirical validation, RB-Modulation demonstrates precise control of content and style in a training-free manner, while enabling seamless composition—eliminating reliance on external adapters or ControlNets.