In this paper, we give a faster width-dependent algorithm for mixed packingcovering LPs. Mixed packing-covering LPs are fundamental to combinatorial optimization in computer science and operations research. Our algorithm finds a 1 ` ε approximate solution in time OpNw{εq, where N is number of nonzero entries in the constraint matrix, and w is the maximum number of nonzeros in any constraint. This algorithm is faster than Nesterov's smoothing algorithm which requires OpN? nw{εq time, where n is the dimension of the problem. Our work utilizes the framework of area convexity introduced in [Sherman-FOCS'17] to obtain the best dependence on ε while breaking the infamous l

In Sec. 2, the robustness of perturbations against defenses is discussed. Additional implementation details that we could not fit into main text due to space constraints are given in Sec. 3. We verify our claim that targeted adversarial perturbations are visually imperceptible in Sec. 4. More experimental results on changing the scale of the scene are provided in Sec. 5. In Sec. 6, existence of the successful adversarial attacks for indoor scenes (NYU-V2) is shown for state-of-the-art indoor monocular depth prediction model. In Sec. 7, we examine how predictions behave when linear operations are applied to perturbations (sum of two perturbations and linear scaling of a perturbation). Failure cases for the perturbations are analyzed in Sec. 8. Finally, in Sec. 9, more qualitative and quantitative results are provided for the experiments whose compressed versions are presented in the main text.

Dear reviewers, we greatly appreciate your remarks and suggestions. We will address the comments in the following. I don't think this is the case, since the primal-dual Could the authors clarify this? We will correct this accordingly. If we have an ɛ-optimal solution of Eq(2) (i.e., Definition 4.1), we can read from it a solution (x, y, z) whose Shouldn't the supremum be over w, instead of x? Page 7, line 253.

We study the effect of adversarial perturbations on the task of monocular depth prediction. Specifically, we explore the ability of small, imperceptible additive perturbations to selectively alter the perceived geometry of the scene. We show that such perturbations can not only globally re-scale the predicted distances from the camera, but also alter the prediction to match a different target scene. We also show that, when given semantic or instance information, perturbations can fool the network to alter the depth of specific categories or instances in the scene, and even remove them while preserving the rest of the scene. To understand the effect of targeted perturbations, we conduct experiments on state-of-the-art monocular depth prediction methods. Our experiments reveal vulnerabilities in monocular depth prediction networks, and shed light on the biases and context learned by them. Figure 1: Altering the predicted scene with adversarial perturbations.

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 ˆd 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]. Furthermore, by Taylor's theorem, one has krL Source code is included in the supplemental material. For the bidirectional lock environment, one of the locks (randomly chosen) gives a larger reward of 1 and the other lock gives a reward of 0.1. Further details on this environment can be found in the work [3].

In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In lowdimensional environments, where tabular parameterization is possible, count-based upper confidence bound (UCB) exploration methods achieve minimax near-optimal rates. However, it remains unclear how to efficiently implement UCB in realistic RL tasks that involve nonlinear function approximation. To address this, we propose a new exploration approach via maximizing the deviation of the occupancy of the next policy from the explored regions. We add this term as an adaptive regularizer to the standard RL objective to trade off between exploration and exploitation. We pair the new objective with a provably convergent algorithm, giving rise to a new intrinsic reward that adjusts existing bonuses. The proposed intrinsic reward is easy to implement and combine with other existing RL algorithms to conduct exploration. As a proof of concept, we evaluate the new intrinsic reward on tabular examples across a variety of model-based and model-free algorithms, showing improvements over count-only exploration strategies. When tested on navigation and locomotion tasks from MiniGrid and DeepMind Control Suite benchmarks, our approach significantly improves sample efficiency over state-of-the-art methods.

A Proof of the object in Equation 3 is convex, when α is sufficiently small. To validate this statement, we first prove two factors in the object are convex (Lemma A.1 and Lemma A.2) and the combination of them keeps the convex property (Lemma A.3). Lemma A.1. Similar to the proof of Lemma A.1, we have ( P and Q are positive semidefinite indicates that i [1..N ], 0 λ Thus, P αQ is positive semidefinite. Combining Lemma A.1, Lemma A.2 and Lemma A.3, the objective of Equation 3 is convex when α is small. We assume the observation of the triggered watermark words are independent to each other, as those words are sparsely distributed in our corpus (4 per 1000 words).

Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive success stories in mastering partially observable environments, mostly in the realm of computer games like Dota 2, StarCraft II, or MineCraft. However, existing methods lack interpretability in the sense that it is not comprehensible for humans what the agent stores in its memory. In this regard, we propose a novel memory mechanism that represents past events in human language.

Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), and how this prediction changes with the passage of time.

In particular, high variance is penalized by the UCE which is particularly important during training. Dir model are shown in Figure 1. M. If the size of the RNN's hidden state is D, and we M < 10, the increase is negligible. Gaussian function; and for the GP model to evaluate the kernel function. We will add these discussions to the paper.