Debiasing Conditional Stochastic Optimization

In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust learning, causal inference, etc. The sample-averaged gradient of the CSO objective is biased due to its nested structure and therefore requires a high sample complexity to reach convergence. We introduce a general stochastic extrapolation technique that effectively reduces the bias. We show that for nonconvex smooth objectives, combining this extrapolation with variance reduction techniques can achieve a significantly better sample complexity than existing bounds. We also develop new algorithms for the finite-sum variant of CSO that also significantly improve upon existing results. Finally, we believe that our debiasing technique could be an interesting tool applicable to other stochastic optimization problems too

Using Context and Interactions to Verify User-Intended Network Requests

Client-side malware can attack users by tampering with applications or user interfaces to generate requests that users did not intend. We propose Verified Intention (VInt), which ensures a network request, as received by a service, is user-intended. VInt is based on "seeing what the user sees" (context). VInt screenshots the user interface as the user interacts with a security-sensitive form. There are two main components. First, VInt ensures output integrity and authenticity by validating the context, ensuring the user sees correctly rendered information. Second, VInt extracts user-intended inputs from the on-screen user-provided inputs, with the assumption that a human user checks what they entered. Using the user-intended inputs, VInt deems a request to be user-intended if the request is generated properly from the user-intended inputs while the user is shown the correct information. VInt is implemented using image analysis and Optical Character Recognition (OCR). Our evaluation shows that VInt is accurate and efficient

COLA: Decentralized Linear Learning

Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, on an arbitrary communication network, without a central coordinator. We propose COLA, a new decentralized training algorithm with strong theoretical guarantees and superior practical performance. Our framework overcomes many limitations of existing methods, and achieves communication efficiency, scalability, elasticity as well as resilience to changes in data and participating devices

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers. However, a fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. While this problem has received significant attention recently, most current defenses assume that the workers have identical data. For realistic cases when the data across workers are heterogeneous (non-iid), we design new attacks which circumvent current defenses, leading to significant loss of performance. We then propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost. We also theoretically and experimentally validate our approach, showing that combining bucketing with existing robust algorithms is effective against challenging attacks. Our work is the first to establish guaranteed convergence for the non-iid Byzantine robust problem under realistic assumptions.Comment: v4 is a major overhaul of the paper and has significantly stronger theory and experiment

Braided stent-assisted coil embolization versus laser engraved stent-assisted coil embolization in patients with unruptured complex intracranial aneurysms

  Purposes: Braided and laser-cut stents both are efficacious and safe for coiling intracranial aneurysms. The study aimed to compare outcomes following braided stent-assisted coil embolization versus laser engraved stent-assisted coil embolization in 266 patients who were diagnosed with unruptured intracranial aneurysms of different types and locations. Methods: Patients with unruptured complex intracranial aneurysms underwent braided (BSE cohort, n = 125) or laser engraved (LSE cohort, n = 141) stent-assisted embolization. Results: The deployment success rate was higher for patients of the LSE cohort than those of the BSE cohort (140 [99%] vs. 117 [94%], p = 0.0142). Seventy-one (fifty-seven percentages) and 73 (52%) were coil embolization procedure success rates of the BSE and the LSE cohorts. Periprocedural intracranial hemorrhage was higher in patients of the BSE cohort than those of the LSE cohort (8 [6%] vs. 1 [1%], p = 0.0142). Four (three percentages) patients from the LSE cohort and 3 (2%) patients from the BSE cohort had in-stent thrombosis during embolization. Permanent morbidities were higher in patients of the LSE cohort than those of the BSE cohort (8 [6%] vs. 1 [1%], p = 0.0389). Higher successful procedures (76% vs. 68%) and fewer postprocedural intracranial hemorrhage (0% vs. 5%) and mortality (0% vs. 5%) were reported for patients of the BSE cohort in posterior circulation aneurysmal location than those of the LSE cohort. Laser engraved stent has fewer problems with deployment and may have better periprocedural and follow-up outcomes after embolization. Conclusions: Braided stent-assisted embolization should be preferred when the aneurysm is present in the posterior circulation