Bayes-adaptive planning offers a principled solution to the explorationexploitation trade-off under model uncertainty. It finds the optimal policy in belief space, which explicitly accounts for the expected effect on future rewards of reductions in uncertainty. However, the Bayes-adaptive solution is typically intractable in domains with large or continuous state spaces. We present a tractable method for approximating the Bayes-adaptive solution by combining simulationbased search with a novel value function approximation technique that generalises appropriately over belief space. Our method outperforms prior approaches in both discrete bandit tasks and simple continuous navigation and control tasks.

Branch-and-bound is a widely used method in combinatorial optimization, including mixed integer programming, structured prediction and MAP inference. While most work has been focused on developing problem-specific techniques, little is known about how to systematically design the node searching strategy on a branch-and-bound tree. We address the key challenge of learning an adaptive node searching order for any class of problem solvable by branch-and-bound. Our strategies are learned by imitation learning. We apply our algorithm to linear programming based branch-and-bound for solving mixed integer programs (MIP). We compare our method with one of the fastest open-source solvers, SCIP; and a very efficient commercial solver, Gurobi. We demonstrate that our approach achieves better solutions faster on four MIP libraries.

We provide statistical and computational analysis of sparse Principal Component Analysis (PCA) in high dimensions. The sparse PCA problem is highly nonconvex in nature. Consequently, though its global solution attains the optimal statistical rate of convergence, such solution is computationally intractable to obtain. Meanwhile, although its convex relaxations are tractable to compute, they yield estimators with suboptimal statistical rates of convergence.

The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention.

Neural Information Processing Systems http://nips.cc/

In this paper, we present a new approach for lifted MAP inference in Markov logic networks (MLNs). The key idea in our approach is to compactly encode the MAP inference problem as an Integer Polynomial Program (IPP) by schematically applying three lifted inference steps to the MLN: lifted decomposition, lifted conditioning, and partial grounding. Our IPP encoding is lifted in the sense that an integer assignment to a variable in the IPP may represent a truth-assignment to multiple indistinguishable ground atoms in the MLN. We show how to solve the IPP by first converting it to an Integer Linear Program (ILP) and then solving the latter using state-of-the-art ILP techniques. Experiments on several benchmark MLNs show that our new algorithm is substantially superior to ground inference and existing methods in terms of computational efficiency and solution quality.

Summary and Contributions: - It is known in the literature that optimistic variants of FTRL algorithm can yield better bounds when the sequence of loss functions are predictable. Such results are relatively rare for FTPL. This paper proposes the optimistic variant of the FTPL algorithm, which in the worst case known optimal bounds, but has the potential to achieve better regret for predictable sequence of loss functions. Specifically, the bounds depend on the g_t - abla_t _* where g_t is the estimate of the gradient for the next loss function and abla_t is the observed gradient. They instantiate this generic result for the worst case analysis via treating the future estimate g_t 0 and achieve the optimal O(T {\frac{1}{2}}) regret.

The paper provides a follow the perturbed leader algorithm and analysis that can obtain better regret bounds when loss/gradient sequence is predictable. The proofs relies on using the equivalent regularization view of FTPL. The authors also provide an application of this result to providing a parallelizable algorithms for solving smooth convex concave saddlepoint games Most of the reviewers found the result interesting. Please address the concerns of the reviewer. Personally, I find the predictable sequences result interesting.

Summary and Contributions: The purpose of this paper is to provide new theoretical tools and bounds for the heterogeneous treatment effect (HTE) estimation in causal inference. This work is in line with a fairly current theme: the HTE estimation is experiencing a growing interest in applications, particularly in the field of personalized medicine. To avoid strong assumptions and to benefit from a broader scope of application, the authors focus on nonparametric estimation. As the authors point out, much effort has been devoted to proposing practical methods, but not so much to the statistical study of nonparametric HTE estimation. This paper establishes minimax rates with dependence on both the geometry of the covariates, and parameters related to propensity scores and noise levels.

The knowledgeable reviewers agree that this is a good paper that warrants acceptance. There was no major concern raised during the discussion phase and the rebuttal has further supported the vote for acceptance. The paper is therefore accepted as a spotlight. If the authors have time and agree that it will be beneficial, the important improvement for the paper is to add a simple experiment that demonstrates the effectiveness of the proposed estimator.