Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on. In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network. A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset. In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy. In order to optimize this variational objective, we choose a variational family defined in the code space of a deep generative model, trained on a public auxiliary dataset that shares some structural similarity with the target dataset. Empirically, our method substantially improves performance in terms of target attack accuracy, sample realism, and diversity on datasets of faces and chest X-ray images.

Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images.

Coordinate descent methods are popular in machine learning and optimization for their simple sparse updates and excellent practical performance. In the context of large-scale sequential game solving, these same properties would be attractive, but until now no such methods were known, because the strategy spaces do not satisfy the typical separable block structure exploited by such methods. We present the first cyclic coordinate-descent-like method for the polytope of sequence-form strategies, which form the strategy spaces for the players in an extensive-form game (EFG). Our method exploits the recursive structure of the proximal update induced by what are known as dilated regularizers, in order to allow for a pseudo block-wise update. We show that our method enjoys a O(1/T)convergence rate to a two-player zero-sum Nash equilibrium, while avoiding the worst-case polynomial scaling with the number of blocks common to cyclic methods. We empirically show that our algorithm usually performs better than other state-of-the-art first-order methods (i.e., mirror prox), and occasionally can even beat CFR+, a state-ofthe-art algorithm for numerical equilibrium computation in zero-sum EFGs. We then introduce a restarting heuristic for EFG solving. We show empirically that restarting can lead to speedups, sometimes huge, both for our cyclic method, as well as for existing methods such as mirror prox and predictive CFR+.

All the source code can be found at our project website https://sites.google.com/view/ In order to prove Theorem 1, we introduce the following lemma, which uses Assumption 1. Lemma 1. The proof is largely based on [2]. Let Hd = H Hbe a vector-valued RKHS, and F[f] be a functional of f. Pure Task Expansion Results on MPE: VACL contains entity progression in the result of Figure 1. To specifically study the performance of task expansion, we exclude entity progression module from VACL and compare with baselines in Simple-Spread with n= 4 and Push-Ball with n= 2. For a fair comparison, we also provide additional experiments to combine GoalGAN and AMIGo with the initial knowledge of easy tasks.

In this section, we provide a convergence rate analysis for Algorithm 1. Similar to Hazan et al. [36], Algorithm 1 has access to an approximate density oracle and an approximate planner defined below: Visitation density oracle: We assume access to an approximate density estimator that takes in a policy and a density approximation error d 0 as inputs and returns ˆd such that kd ˆd k1 d. Approximate planning oracle: We assume access to an approximate planner that, given any MDP M and error tolerance p 0, returns a policy such that JM() max JM() p. A.1 Proof of Theorem 1 We first give the following proposition that captures certain properties of the proposed objective. The proof is postponed to the end of this section. Taking the above proposition as given for the moment, we prove Theorem 1 following steps similar to those of Hazan et al. [36, Theorem 4.1]. Since k returned by the approximate planning oracle is an p-optimal policy in Mk, we have (1) 1hd k,rki (1) 1hd,rki p for any policy, including?. Therefore, It is straightforward to check that setting 0.1 1, p 0.1, d 0.1 1, 0.1, and the number of iterations K 1 log(10B 1) yields the claim of Theorem 1. Remark 2. Since the temperature parameter k in Proposition 1 goes to zero as k increases, one can show that the expected value of policy returned by Algorithm 1 converges to the maximum performance J(?).