Reviewerly/exHarmony huggingface dataset:
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time

Differentially private (DP) stochastic convex optimization (SCO) is a fundamental problem, where the goal is to approximately minimize the population risk with respect to a convex loss function, given a dataset of $n$ i.i.d. samples from a distribution, while satisfying differential privacy with respect to the dataset. Most of the existing works in the literature of private convex optimization focus on the Euclidean (i.e., $\ell_2$) setting, where the loss is assumed to be Lipschitz (and possibly smooth) w.r.t. the $\ell_2$ norm over a constraint set with bounded $\ell_2$ diameter. Algorithms based on noisy stochastic gradient descent (SGD) are known to attain the optimal excess risk in this setting. In this work, we conduct a systematic study of DP-SCO for $\ell_p$-setups under a standard smoothness assumption on the loss. For $1< p\leq 2$, under a standard smoothness assumption, we give a new, linear-time DP-SCO algorithm with optimal excess risk. Previously known constructions with optimal excess risk for $1< p <2$ run in super-linear time in $n$. For $p=1$, we give an algorithm with nearly optimal excess risk. Our result for the $\ell_1$-setup also extends to general polyhedral norms and feasible sets. Moreover, we show that the excess risk bounds resulting from our algorithms for $1\leq p \leq 2$ are attained with high probability. For $2 < p \leq \infty$, we show that existing linear-time constructions for the Euclidean setup attain a nearly optimal excess risk in the low-dimensional regime. As a consequence, we show that such constructions attain a nearly optimal excess risk for $p=\infty$. Our work draws upon concepts from the geometry of normed spaces, such as the notions of regularity, uniform convexity, and uniform smoothness.
Multi-Party Proof Generation in QAP-based zk-SNARKs

Zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) allows a party, known as the prover, to convince another party, known as the verifier, that he knows a private value $v$, without revealing it, such that $F(u,v)=y$ for some function $F$ and public values $u$ and $y$. There are various versions of zk-SNARK, among them, Quadratic Arithmetic Program (QAP)-based zk-SNARK has been widely used in practice, specially in Blockchain technology. This is attributed to two desirable features; its fixed-size proof and the very light computation load of the verifier. However, the computation load of the prover in QAP-based zkSNARKs, is very heavy, even-though it is designed to be very efficient. This load can be beyond the prover's computation power to handle, and has to be offloaded to some external servers. In the existing offloading solutions, either (i) the load of computation, offloaded to each sever, is a fraction of the prover's primary computation (e.g., DZIK), however the servers need to be trusted, (ii) the servers are not required to be trusted, but the computation complexity imposed to each one is the same as the prover's primary computation (e.g., Trinocchio). In this paper, we present a scheme, which has the benefits of both solutions. In particular, we propose a secure multi-party proof generation algorithm where the prover can delegate its task to $N $ servers, where (i) even if a group of $T \in \mathbb{N}$ servers, $T\le N$, collude, they cannot gain any information about the secret value $v$, (ii) the computation complexity of each server is less than $1/(N-T)$ of the prover's primary computation. The design is such that we don't lose the efficiency of the prover's algorithm in the process of delegating the tasks to external servers.
Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories

This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The proposed approach compensates for the loss of information due to the partially observed states by constructing non-Markovian reduced models that make future-state predictions based on a history of reduced states, in contrast to traditional Markovian reduced models that rely on the current reduced state alone to predict the next state. The core contributions of this work are a data sampling scheme to sample partially observed states from high-dimensional dynamical systems and a formulation of a regression problem to fit the non-Markovian reduced terms to the sampled states. Under certain conditions, the proposed approach recovers from data the very same non-Markovian terms that one obtains with intrusive methods that require the governing equations and discrete operators of the high-dimensional dynamical system. Numerical results demonstrate that the proposed approach leads to non-Markovian reduced models that are predictive far beyond the training regime. Additionally, in the numerical experiments, the proposed approach learns non-Markovian reduced models from trajectories with only 20% observed state components that are about as accurate as traditional Markovian reduced models fitted to trajectories with 99% observed components.
How Developers Engineer Test Cases: An Observational Study

One of the main challenges that developers face when testing their systems lies in engineering test cases that are good enough to reveal bugs. And while our body of knowledge on software testing and automated test case generation is already quite significant, in practice, developers are still the ones responsible for engineering test cases manually. Therefore, understanding the developers' thought- and decision-making processes while engineering test cases is a fundamental step in making developers better at testing software. In this paper, we observe 13 developers thinking-aloud while testing different real-world open-source methods, and use these observations to explain how developers engineer test cases. We then challenge and augment our main findings by surveying 72 software developers on their testing practices. We discuss our results from three different angles. First, we propose a general framework that explains how developers reason about testing. Second, we propose and describe in detail the three different overarching strategies that developers apply when testing. Third, we compare and relate our observations with the existing body of knowledge and propose future studies that would advance our knowledge on the topic.
Nonlocal Variable-Strength Measurements of N Qubits Using GHZ-like Entanglement

The direct measurement of nonlocal properties of entangled quantum systems has been the subject of several recent experimental investigations. Of particular interest is the implementation of nonlocal measurements via indirect measurement schemes, which allow for greater flexibility in the control of the measurement strength. Building on previous results established in the bipartite case, we present a scheme to implement genuine nonlocal measurements of N-qubit systems with variable strength, using GHZ-like entangled qubit meters. This method can be applied to the joint measurement of commuting product observables, enabling us to distinguish between orthogonal nonlocal states, such as Bell states, with minimal disturbance and arbitrary resolution. An explicit relation between the overall measurement strength and the meter entanglement as quantified by the $n$-tangle is derived, opening the door to a new interpretation of the $n$-tangle as a resource for nonlocal measurements.
NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers

The increasing accessibility of the internet facilitated social media usage and encouraged individuals to express their opinions liberally. Nevertheless, it also creates a place for content polluters to disseminate offensive posts or contents. Most of such offensive posts are written in a cross-lingual manner and can easily evade the online surveillance systems. This paper presents an automated system that can identify offensive text from multilingual code-mixed data. In the task, datasets provided in three languages including Tamil, Malayalam and Kannada code-mixed with English where participants are asked to implement separate models for each language. To accomplish the tasks, we employed two machine learning techniques (LR, SVM), three deep learning (LSTM, LSTM+Attention) techniques and three transformers (m-BERT, Indic-BERT, XLM-R) based methods. Results show that XLM-R outperforms other techniques in Tamil and Malayalam languages while m-BERT achieves the highest score in the Kannada language. The proposed models gained weighted $f_1$ score of $0.76$ (for Tamil), $0.93$ (for Malayalam), and $0.71$ (for Kannada) with a rank of $3^{rd}$, $5^{th}$ and $4^{th}$ respectively.
Non-invertible Anonymous Communication for the Quantum Era

We introduce a new approach for circuit anonymous communication based on Lizama's non-invertible Key Exchange Protocol (ni-KEP) which has been conceived to work in the quantum era. Lizama's protocol has the smallest key size when compared to main post-quantum schemes thus it becomes a promising alternative for the quantum era. Circuit-based communication can be scaled to support the Hidden Service Protocol (HSP) as well as cross-domain digital certificates that promise greater computing security, speed and efficiency.
Diffusion Means and Heat Kernel on Manifolds

We introduce diffusion means as location statistics on manifold data spaces. A diffusion mean is defined as the starting point of an isotropic diffusion with a given diffusivity. They can therefore be defined on all spaces on which a Brownian motion can be defined and numerical calculation of sample diffusion means is possible on a variety of spaces using the heat kernel expansion. We present several classes of spaces, for which the heat kernel is known and sample diffusion means can therefore be calculated. As an example, we investigate a classic data set from directional statistics, for which the sample Fr\'echet mean exhibits finite sample smeariness.
Learning Human-like Hand Reaching for Human-Robot Handshaking

One of the first and foremost non-verbal interactions that humans perform is a handshake. It has an impact on first impressions as touch can convey complex emotions. This makes handshaking an important skill for the repertoire of a social robot. In this paper, we present a novel framework for learning reaching behaviours for human-robot handshaking behaviours for humanoid robots solely using third-person human-human interaction data. This is especially useful for non-backdrivable robots that cannot be taught by demonstrations via kinesthetic teaching. Our approach can be easily executed on different humanoid robots. This removes the need for re-training, which is especially tedious when training with human-interaction partners. We show this by applying the learnt behaviours on two different humanoid robots with similar degrees of freedom but different shapes and control limits.
On the Initialization for Convex-Concave Min-max Problems

Convex-concave min-max problems are ubiquitous in machine learning, and people usually utilize first-order methods (e.g., gradient descent ascent) to find the optimal solution. One feature which separates convex-concave min-max problems from convex minimization problems is that the best known convergence rates for min-max problems have an explicit dependence on the size of the domain, rather than on the distance between initial point and the optimal solution. This means that the convergence speed does not have any improvement even if the algorithm starts from the optimal solution, and hence, is oblivious to the initialization. Here, we show that strict-convexity-strict-concavity is sufficient to get the convergence rate to depend on the initialization. We also show how different algorithms can asymptotically achieve initialization-dependent convergence rates on this class of functions. Furthermore, we show that the so-called "parameter-free" algorithms allow to achieve improved initialization-dependent asymptotic rates without any learning rate to tune. In addition, we utilize this particular parameter-free algorithm as a subroutine to design a new algorithm, which achieves a novel non-asymptotic fast rate for strictly-convex-strictly-concave min-max problems with a growth condition and H{\"o}lder continuous solution mapping. Experiments are conducted to verify our theoretical findings and demonstrate the effectiveness of the proposed algorithms.
General dependence structures for some models based on exponential families with quadratic variance functions

We describe a procedure to introduce general dependence structures on a set of random variables. These include order-$q$ moving average-type structures, as well as seasonal, periodic, spatial and spatio-temporal dependences. The invariant marginal distribution can be in any family that is conjugate to an exponential family with quadratic variance function. Dependence is induced via a set of suitable latent variables whose conditional distribution mirrors the sampling distribution in a Bayesian conjugate analysis of such exponential families. We obtain strict stationarity as a special case.
QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NLP models that solve simple sentence classification tasks on quantum hardware. We conduct quantum simulations that compare the syntax-sensitive model of Coecke et al. with two baselines that use less or no syntax; specifically, we implement the quantum analogues of a "bag-of-words" model, where syntax is not taken into account at all, and of a word-sequence model, where only word order is respected. We demonstrate that all models converge smoothly both in simulations and when run on quantum hardware, and that the results are the expected ones based on the nature of the tasks and the datasets used. Another important goal of this paper is to describe in a way accessible to AI and NLP researchers the main principles, process and challenges of experiments on quantum hardware. Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing.
RbSyn: Type- and Effect-Guided Program Synthesis

In recent years, researchers have explored component-based synthesis, which aims to automatically construct programs that operate by composing calls to existing APIs. However, prior work has not considered efficient synthesis of methods with side effects, e.g., web app methods that update a database. In this paper, we introduce RbSyn, a novel type- and effect-guided synthesis tool for Ruby. An RbSyn synthesis goal is specified as the type for the target method and a series of test cases it must pass. RbSyn works by recursively generating well-typed candidate method bodies whose write effects match the read effects of the test case assertions. After finding a set of candidates that separately satisfy each test, RbSyn synthesizes a solution that branches to execute the correct candidate code under the appropriate conditions. We formalize RbSyn on a core, object-oriented language $\lambda_{syn}$ and describe how the key ideas of the model are scaled-up in our implementation for Ruby. We evaluated RbSyn on 19 benchmarks, 12 of which come from popular, open-source Ruby apps. We found that RbSyn synthesizes correct solutions for all benchmarks, with 15 benchmarks synthesizing in under 9 seconds, while the slowest benchmark takes 83 seconds. Using observed reads to guide synthesize is effective: using type-guidance alone times out on 10 of 12 app benchmarks. We also found that using less precise effect annotations leads to worse synthesis performance. In summary, we believe type- and effect-guided synthesis is an important step forward in synthesis of effectful methods from test cases.
Towards Unbiased and Accurate Deferral to Multiple Experts

Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for ensuring accuracy and fairness in such prediction systems that combine machine learning model inferences and domain expert predictions. Prior work on "deferral systems" in classification settings has focused on the setting of a pipeline with a single expert and aimed to accommodate the inaccuracies and biases of this expert to simultaneously learn an inference model and a deferral system. Our work extends this framework to settings where multiple experts are available, with each expert having their own domain of expertise and biases. We propose a framework that simultaneously learns a classifier and a deferral system, with the deferral system choosing to defer to one or more human experts in cases of input where the classifier has low confidence. We test our framework on a synthetic dataset and a content moderation dataset with biased synthetic experts, and show that it significantly improves the accuracy and fairness of the final predictions, compared to the baselines. We also collect crowdsourced labels for the content moderation task to construct a real-world dataset for the evaluation of hybrid machine-human frameworks and show that our proposed learning framework outperforms baselines on this real-world dataset as well.
A Framework For Pruning Deep Neural Networks Using Energy-Based Models

A typical deep neural network (DNN) has a large number of trainable parameters. Choosing a network with proper capacity is challenging and generally a larger network with excessive capacity is trained. Pruning is an established approach to reducing the number of parameters in a DNN. In this paper, we propose a framework for pruning DNNs based on a population-based global optimization method. This framework can use any pruning objective function. As a case study, we propose a simple but efficient objective function based on the concept of energy-based models. Our experiments on ResNets, AlexNet, and SqueezeNet for the CIFAR-10 and CIFAR-100 datasets show a pruning rate of more than $50\%$ of the trainable parameters with approximately $<5\%$ and $<1\%$ drop of Top-1 and Top-5 classification accuracy, respectively.
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling

With a better understanding of the loss surfaces for multilayer networks, we can build more robust and accurate training procedures. Recently it was discovered that independently trained SGD solutions can be connected along one-dimensional paths of near-constant training loss. In this paper, we show that there are mode-connecting simplicial complexes that form multi-dimensional manifolds of low loss, connecting many independently trained models. Inspired by this discovery, we show how to efficiently build simplicial complexes for fast ensembling, outperforming independently trained deep ensembles in accuracy, calibration, and robustness to dataset shift. Notably, our approach only requires a few training epochs to discover a low-loss simplex, starting from a pre-trained solution. Code is available at https://github.com/g-benton/loss-surface-simplexes.
LES: Locally Exploitative Sampling for Robot Path Planning

Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples to improve the cost-to-come value of vertices in a neighborhood. The application of proposed algorithm adds an exploitative-bias to sampling and results in a faster convergence to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experiments performed fora variety of higher dimensional robotic planning tasks.
Combining Off and On-Policy Training in Model-Based Reinforcement Learning

The combination of deep learning and Monte Carlo Tree Search (MCTS) has shown to be effective in various domains, such as board and video games. AlphaGo represented a significant step forward in our ability to learn complex board games, and it was rapidly followed by significant advances, such as AlphaGo Zero and AlphaZero. Recently, MuZero demonstrated that it is possible to master both Atari games and board games by directly learning a model of the environment, which is then used with MCTS to decide what move to play in each position. During tree search, the algorithm simulates games by exploring several possible moves and then picks the action that corresponds to the most promising trajectory. When training, limited use is made of these simulated games since none of their trajectories are directly used as training examples. Even if we consider that not all trajectories from simulated games are useful, there are thousands of potentially useful trajectories that are discarded. Using information from these trajectories would provide more training data, more quickly, leading to faster convergence and higher sample efficiency. Recent work introduced an off-policy value target for AlphaZero that uses data from simulated games. In this work, we propose a way to obtain off-policy targets using data from simulated games in MuZero. We combine these off-policy targets with the on-policy targets already used in MuZero in several ways, and study the impact of these targets and their combinations in three environments with distinct characteristics. When used in the right combinations, our results show that these targets can speed up the training process and lead to faster convergence and higher rewards than the ones obtained by MuZero.