mtybilly/Instruct2DS huggingface dataset:
WCLD: Curated Large Dataset of Criminal Cases from Wisconsin Circuit Courts
Authors: Naman Goel, Nianyun Li, Claudia Marangon, Peiyao Sun Elliott Ash
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Datasets and Benchmarks Track), 2023

Abstract: Machine learning based decision-support tools in criminal justice systems are subjects of intense discussions and academic research. There are important open questions about the utility and fairness of such tools. Academic researchers often rely on a few small datasets that are not sufficient to empirically study various real-world aspects of these questions. In this paper, we contribute WCLD, a curated large dataset of 1.5 million criminal cases from circuit courts in the U.S. state of Wisconsin. We used reliable public data from 1970 to 2020 to curate attributes like prior criminal counts and recidivism outcomes. The dataset contains large number of samples from five racial groups, in addition to information like sex and age (at judgment and first offense). Other attributes in this dataset include neighborhood characteristics obtained from census data, detailed types of offense, charge severity, case decisions, sentence lengths, year of filing etc. We also provide pseudo-identifiers for judge, county and zipcode. The dataset will not only enable researchers to more rigorously study algorithmic fairness in the context of criminal justice, but also relate algorithmic challenges with various systemic issues. We also discuss in detail the process of constructing the dataset and provide a datasheet. The WCLD dataset is available at https://clezdata.github.io/wcld/.
Weitzman's Rule for Pandora's Box with Correlations
Authors: Christos Tzamos Evangelia Gergatsouli
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Pandora’s Box is a central problem in decision making under uncertainty that can model various real life scenarios. In this problem we are given n boxes, each with a fixed opening cost, and an unknown value drawn from a known distribution, only revealed if we pay the opening cost. Our goal is to find a strategy for opening boxes to minimize the sum of the value selected and the opening cost paid.In this work we revisit Pandora’s Box when the value distributions are correlated, first studied in [CGT+20]. We show that the optimal algorithm for the independent case, given by Weitzman’s rule, directly works for the correlated case. In fact, our algorithm results in significantly improved approximation guarantees compared to the previous work, while also being substantially simpler. We also show how to implement the rule given only sample access to the correlated distribution of values. Specifically, we find that a number of samples that is polynomial in the number of boxes is sufficient for the algorithm to work.
Compositional Sculpting of Iterative Generative Processes
Authors: Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, Tommi Jaakkola Timur Garipov
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition.A key challenge in composing iterative generative processes, such as GFlowNets and diffusion models, is that to realize the desired target distribution, all steps of the generative process need to be coordinated, and satisfy delicate balance conditions.In this work, we propose Compositional Sculpting: a general approach for defining compositions of iterative generative processes. We then introduce a method for sampling from these compositions built on classifier guidance.We showcase ways to accomplish compositional sculpting in both GFlowNets and diffusion models. We highlight two binary operations $\\unicode{x2014}$ the $\\textit{harmonic mean}\\unicode{x00A0}(p_1 \\otimes p_2$) and the $\\textit{contrast}\\unicode{x00A0}(p_1 \\,\\unicode{x25D1}\\,\\, p_2$) between pairs, and the generalization of these operations to multiple component distributions.We offer empirical results on image and molecular generation tasks. Project codebase: https://github.com/timgaripov/compositional-sculpting.
Face Reconstruction from Facial Templates by Learning Latent Space of a Generator Network
Authors: Sébastien Marcel Hatef Otroshi Shahreza
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: In this paper, we focus on the template inversion attack against face recognition systems and propose a new method to reconstruct face images from facial templates. Within a generative adversarial network (GAN)-based framework, we learn a mapping from facial templates to the intermediate latent space of a pre-trained face generation network, from which we can generate high-resolution realistic reconstructed face images. We show that our proposed method can be applied in whitebox and blackbox attacks against face recognition systems. Furthermore, we evaluate the transferability of our attack when the adversary uses the reconstructed face image to impersonate the underlying subject in an attack against another face recognition system. Considering the adversary's knowledge and the target face recognition system, we define five different attacks and evaluate the vulnerability of state-of-the-art face recognition systems. Our experiments show that our proposed method achieves high success attack rates in whitebox and blackbox scenarios. Furthermore, the reconstructed face images are transferable and can be used to enter target face recognition systems with a different feature extractor model. We also explore important areas in the reconstructed face images that can fool the target face recognition system.
Triangulation Residual Loss for Data-efficient 3D Pose Estimation
Authors: Tao Yu, Liang An, Yipeng Huang, Fang Deng, Qionghai Dai Jiachen Zhao
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper presents Triangulation Residual loss (TR loss) for multiview 3D pose estimation in a data-efficient manner. Existing 3D supervised models usually require large-scale 3D annotated datasets, but the amount of existing data is still insufficient to train supervised models to achieve ideal performance, especially for animal pose estimation. To employ unlabeled multiview data for training, previous epipolar-based consistency provides a self-supervised loss that considers only the local consistency in pairwise views, resulting in limited performance and heavy calculations. In contrast, TR loss enables self-supervision with global multiview geometric consistency. Starting from initial 2D keypoint estimates, the TR loss can fine-tune the corresponding 2D detector without 3D supervision by simply minimizing the smallest singular value of the triangulation matrix in an end-to-end fashion. Our method achieves the state-of-the-art 25.8mm MPJPE and competitive 28.7mm MPJPE with only 5\% 2D labeled training data on the Human3.6M dataset. Experiments on animals such as mice demonstrate our TR loss's data-efficient training ability.
A Long $N$-step Surrogate Stage Reward for Deep Reinforcement Learning
Authors: Ruofan Wu, Jennie Si Junmin Zhong
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We introduce a new stage reward estimator  named the long $N$-step surrogate stage (LNSS) reward for deep reinforcement learning (RL). It aims at mitigating the high variance problem, which has shown impeding successful convergence of learning, hurting task performance, and hindering applications of deep RL in continuous control problems. In this paper we show that LNSS, which utilizes a long reward trajectory of  rewards of future steps, provides consistent performance improvement measured by average reward, convergence speed, learning success rate,and variance reduction in $Q$ values and rewards.  Our evaluations are based on a variety of environments in DeepMind Control Suite and OpenAI Gym  by using  LNSS in baseline deep RL algorithms such as DDPG, D4PG, and TD3. We show  that LNSS reward has enabled good results that have been challenging to obtain by deep RL previously. Our analysis also shows that  LNSS exponentially reduces the upper bound on the variances of $Q$ values from respective single-step methods.
CODA: Generalizing to Open and Unseen Domains with Compaction and Disambiguation
Authors: Luyao Tang, Yue Huang, Xiaoguang Han, Yizhou Yu Chaoqi Chen
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The generalization capability of machine learning systems degenerates notably when the test distribution drifts from the training distribution. Recently, Domain Generalization (DG) has been gaining momentum in enabling machine learning models to generalize to unseen domains. However, most DG methods assume that training and test data share an identical label space, ignoring the potential unseen categories in many real-world applications. In this paper, we delve into a more general but difficult problem termed Open Test-Time DG (OTDG), where both domain shift and open class may occur on the unseen test data. We propose Compaction and Disambiguation (CODA), a novel two-stage framework for learning compact representations and adapting to open classes in the wild. To meaningfully regularize the model's decision boundary, CODA introduces virtual unknown classes and optimizes a new training objective to insert unknowns into the latent space by compacting the embedding space of source known classes. To adapt target samples to the source model, we then disambiguate the decision boundaries between known and unknown classes with a test-time training objective, mitigating the adaptivity gap and catastrophic forgetting challenges. Experiments reveal that CODA can significantly outperform the previous best method on standard DG datasets and harmonize the classification accuracy between known and unknown classes.
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis
Authors: John V. Guttag, Adrian Dalca Jose Javier Gonzalez Ortiz
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Convolutional Neural Networks (CNNs) are the predominant model used for a variety of medical image analysis tasks. At inference time, these models are computationally intensive, especially with volumetric data.In principle, it is possible to trade accuracy for computational efficiency by manipulating the rescaling factor in the downsample and upsample layers of CNN architectures.However, properly exploring the accuracy-efficiency trade-off is prohibitively expensive with existing models.To address this, we introduce Scale-Space HyperNetworks (SSHN), a method that learns a spectrum of CNNs with varying internal rescaling factors.A single SSHN characterizes an entire Pareto accuracy-efficiency curve of models that match, and occasionally surpass, the outcomes of training many separate networks with fixed rescaling factors.We demonstrate the proposed approach in several medical image analysis applications, comparing SSHN against strategies with both fixed and dynamic rescaling factors.We find that SSHN consistently provides a better accuracy-efficiency trade-off at a fraction of the training cost. Trained SSHNs enable the user to quickly choose a rescaling factor that appropriately balances accuracy and computational efficiency for their particular needs at inference.
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts
Authors: Ziyu Ye, Zhe Feng, Ashwinkumar Badanidiyuru Varadaraja, Haifeng Xu Chaoqi Wang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Standard contextual bandit problem assumes that all the relevant contexts are observed before the algorithm chooses an arm. This modeling paradigm, while useful, often falls short when dealing with problems in which additional valuable contexts can be observed after arm selection. For example, content recommendation platforms like Youtube, Instagram, Tiktok receive much additional features about a user's reward after the user clicks a content (e.g., how long the user stayed, what is the user's watch speed, etc.). To improve online learning efficiency in these applications,  we  study a novel contextual bandit problem with post-serving contexts and design a new algorithm, poLinUCB,  that achieves tight regret under standard assumptions. Core to our technical proof is a robustified and generalized version of the well-known Elliptical Potential Lemma (EPL), which can accommodate  noise in data. Such robustification is necessary for tackling our problem, though we believe it could also be of general interest.Extensive empirical tests on both synthetic and real-world datasets  demonstrate the significant benefit of utilitzing post-serving contexts as well as the superior performance of  our   algorithm over the state-of-the-art approaches.
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
Authors: Nathan Kallus, Jason Lee, Wen Sun Masatoshi Uehara
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We consider offline reinforcement learning (RL) where we only have only access to offline data. In contrast to numerous offline RL algorithms that necessitate the uniform coverage of the offline data over state and action space, we propose value-based algorithms with PAC guarantees under partial coverage, specifically, coverage of offline data against a single policy, and realizability of soft Q-function (a.k.a., entropy-regularized Q-function) and another function, which is defined as a solution to a saddle point of certain minimax optimization problem). Furthermore, we show the analogous result for Q-functions instead of soft Q-functions. To attain these guarantees, we use novel algorithms with minimax loss functions to accurately estimate soft Q-functions and Q-functions with -convergence guarantees measured on the offline data. We introduce these loss functions by casting the estimation problems into nonlinear convex optimization problems and taking the Lagrange functions.
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Authors: Qiaomin Xie, Yudong Chen, Weina Wang Yige Hong
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We study the infinite-horizon restless bandit problem with the average reward criterion, in both discrete-time and continuous-time settings.A fundamental goal is to efficiently compute policies that achieve a diminishing optimality gap as the number of arms, $N$, grows large. Existing results on asymptotic optimality all rely on the uniform global attractor property (UGAP), a complex and challenging-to-verify assumption. In this paper, we propose a general, simulation-based framework, Follow-the-Virtual-Advice, that converts any single-armed policy into a policy for the original $N$-armed problem. This is done by simulating the single-armed policy on each arm and carefully steering the real state towards the simulated state. Our framework can be instantiated to produce a policy with an $O(1/\sqrt{N})$ optimality gap. In the discrete-time setting, our result holds under a simpler synchronization assumption, which covers some problem instances that violate UGAP. More notably, in the continuous-time setting, we do not require \emph{any} additional assumptions beyond the standard unichain condition. In both settings, our work is the first asymptotic optimality result that does not require UGAP.
Smooth Flipping Probability for Differential Private Sign Random Projection Methods
Authors: Xiaoyun Li Ping Li
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We develop a series of differential privacy (DP) algorithms from a family of random projection (RP) and sign random projection (SignRP) methods. We first show how to improve the previous DP-RP approach using the ``optimal Gaussian mechanism''. Then, we propose a series of DP-SignRP algorithms that leverage the robustness of the ``sign flipping probability'' of random projections. That is, given $x = \sum_{i=1}^p u_i w_{i}$ where $u$ is a $p$-dimensional data vector and $w$ is a symmetric random vector,  $sign(x)$ only has a fairly small probability to be flipped if there is a small modification on data $u$, depending on the specific distribution of $w$. This robustness leads to our novel design of ``smooth flipping probability'' for SignRP-type algorithms with better utility than using the standard randomized response mechanism. Retrieval and classification experiments demonstrate that,  among the presented DP-RP  algorithms,  \textbf{DP-SignOPORP} (where OPORP  is an improvement over the celebrated  count-sketch algorithms), performs the best in general.In the industrial practice, DP methods were not very popular for machine learning or search, largely because the performance typically would  drop substantially if DP is applied. Since our proposed new DP algorithms have significantly improved the  performance, it is anticipated that our work will motivate a wide adoption of DP in practice. Finally, we   stress that, since our  methods are applied to the original data (i.e., feature vectors), the privacy of  downstream tasks is naturally protected.
Idempotent Learned Image Compression with Right-Inverse
Authors: Tongda Xu, Yan Wang, Jingjing Liu, Ya-Qin Zhang Yanghao Li
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We consider the problem of idempotent learned image compression (LIC).The idempotence of codec refers to the stability of codec to re-compression.To achieve idempotence, previous codecs adopt invertible transforms such as DCT and normalizing flow.In this paper, we first identify that invertibility of transform is sufficient but not necessary for idempotence. Instead, it can be relaxed into right-invertibility. And such relaxation allows wider family of transforms.Based on this identification, we implement an idempotent codec using our proposed blocked convolution and null-space enhancement.Empirical results show that we achieve state-of-the-art rate-distortion performance among idempotent codecs. Furthermore, our codec can be extended into near-idempotent codec by relaxing the right-invertibility. And this near-idempotent codec has significantly less quality decay after $50$ rounds of re-compression compared with other near-idempotent codecs.
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Authors: Yiqin Lv, yanghe feng, Zheng Xie, Jincai Huang Qi Wang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Meta learning is a promising paradigm to enable skill transfer across tasks.Most previous methods employ the empirical risk minimization principle in optimization.However, the resulting worst fast adaptation to a subset of tasks can be catastrophic in risk-sensitive scenarios.To robustify fast adaptation, this paper optimizes meta learning pipelines from a distributionally robust perspective and meta trains models with the measure of tail task risk.We take the two-stage strategy as heuristics to solve the robust meta learning problem, controlling the worst fast adaptation cases at a certain probabilistic level. Experimental results show that our simple method can improve the robustness of meta learning to task distributions and reduce the conditional expectation of the worst fast adaptation risk.
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Authors: Niki Kilbertus, Maximilian Gelbrecht, Niklas Boers Alistair White
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Many successful methods to learn dynamical systems from data have recently been introduced. However, ensuring that the inferred dynamics preserve known constraints, such as conservation laws or restrictions on the allowed system states, remains challenging. We propose stabilized neural differential equations (SNDEs), a method to enforce arbitrary manifold constraints for neural differential equations. Our approach is based on a stabilization term that, when added to the original dynamics, renders the constraint manifold provably asymptotically stable. Due to its simplicity, our method is compatible with all common neural differential equation (NDE) models and broadly applicable. In extensive empirical evaluations, we demonstrate that SNDEs outperform existing methods while broadening the types of constraints that can be incorporated into NDE training.
On the Importance of Exploration for Generalization in Reinforcement Learning
Authors: J. Zico Kolter, Roberta Raileanu Yiding Jiang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Existing approaches for improving generalization in deep reinforcement learning (RL) have mostly focused on representation learning, neglecting RL-specific aspects such as exploration. We hypothesize that the agent's exploration strategy plays a key role in its ability to generalize to new environments.Through a series of experiments in a tabular contextual MDP, we show that exploration is helpful not only for efficiently finding the optimal policy for the training environments but also for acquiring knowledge that helps decision making in unseen environments. Based on these observations, we propose EDE: Exploration via Distributional Ensemble, a method that encourages the exploration of states with high epistemic uncertainty through an ensemble of Q-value distributions. The proposed algorithm is the first value-based approach to achieve strong performance on both Procgen and Crafter, two benchmarks for generalization in RL with high-dimensional observations. The open-sourced implementation can be found at https://github.com/facebookresearch/ede.
Uniform Convergence with Square-Root Lipschitz Loss
Authors: Zhen Dai, Frederic Koehler, Nati Srebro Lijia Zhou
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: We establish generic uniform convergence guarantees for Gaussian data in terms of the Radamacher complexity of the hypothesis class and the Lipschitz constant of the square root of the scalar loss function.  We show how these guarantees substantially generalize previous results based on smoothness (Lipschitz constant of the derivative), and allow us to handle the broader class of square-root-Lipschtz losses, which includes also non-smooth loss functions appropriate for studying phase retrieval and ReLU regression, as well as rederive and better understand “optimistic rate” and interpolation learning guarantees.
A Fractional Graph Laplacian Approach to Oversmoothing
Authors: Raffaele Paolino, Aras Bacho, Gitta Kutyniok Sohir Maskey
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Graph neural networks (GNNs) have shown state-of-the-art performances in various applications. However, GNNs often struggle to capture long-range dependencies in graphs due to oversmoothing. In this paper, we generalize the concept of oversmoothing from undirected to directed graphs. To this aim, we extend the notion of Dirichlet energy by considering a directed symmetrically normalized Laplacian. As vanilla graph convolutional networks are prone to oversmooth, we adopt a neural graph ODE framework. Specifically, we propose fractional graph Laplacian neural ODEs, which describe non-local dynamics. We prove that our approach allows propagating information  between distant nodes while maintaining a low probability of long-distance jumps. Moreover, we show that our method is more flexible with respect to the convergence of the graph’s Dirichlet energy, thereby mitigating oversmoothing. We conduct extensive experiments on synthetic and real-world graphs, both  directed and undirected, demonstrating our method’s versatility across diverse graph homophily levels.  Ourcode is available at https://github.com/RPaolino/fLode
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
Authors: Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury Shuai Zhang
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: This paper provides a theoretical understanding of deep Q-Network (DQN) with the $\varepsilon$-greedy exploration in deep reinforcement learning.Despite the tremendous empirical achievement of the DQN, its theoretical characterization remains underexplored.First, the exploration strategy is either impractical or ignored in the existing analysis.  Second, in contrast to conventional Q-learning algorithms, the DQN employs the target network and experience replay to acquire an unbiased estimation of the mean-square Bellman error (MSBE) utilized in training  the Q-network. However,the existing theoretical analysis of DQNs lacks convergence analysis or bypasses the technical challenges by deploying a significantly overparameterized neural network, which is not computationally efficient. This paper provides the first theoretical convergence and sample complexity analysis of the  practical setting of DQNs with $\epsilon$-greedy policy. We prove an iterative procedure with decaying $\epsilon$ converges to the optimal Q-value function geometrically. Moreover, a higher level of $\epsilon$ values enlarges the region of convergence but slows down the convergence, while the opposite holds for a lower level of $\epsilon$ values. Experiments justify our established theoretical insights on DQNs.
Joint Data-Task Generation for Auxiliary Learning
Authors: Xin Wang, Yuwei Zhou, Yijian Qin, Chaoyu Guan, Wenwu Zhu Hong Chen
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Current auxiliary learning methods mainly adopt the methodology of reweighing losses for the manually collected auxiliary data and tasks. However, these methods heavily rely on domain knowledge during data collection, which may be hardly available in reality. Therefore, current methods will become less effective and even do harm to the primary task when unhelpful auxiliary data and tasks are employed. To tackle the problem, we propose a joint data-task generation framework for auxiliary learning (DTG-AuxL), which can bring benefits to the primary task by generating the new auxiliary data and task in a joint manner. The proposed DTG-AuxL framework contains a joint generator and a bi-level optimization strategy. Specifically, the joint generator contains a feature generator and a label generator, which are designed to be applicable  and expressive for various auxiliary learning scenarios. The bi-level optimization strategy optimizes the joint generator and the task learning model, where the joint generator is effectively optimized in the upper level via the implicit gradient from the primary loss and the explicit gradient of our proposed instance regularization, while the task learning model is optimized in the lower level by the generated data and task. Extensive experiments show that our proposed DTG-AuxL framework consistently outperforms existing methods in various auxiliary learning scenarios, particularly when the manually collected auxiliary data and tasks are unhelpful.
On Proper Learnability between Average- and Worst-case Robustness
Authors: UNIQUE SUBEDI, Ambuj Tewari Vinod Raman
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Recently, Montasser at al. (2019) showed that finite VC dimension is not sufficient for proper adversarially robust PAC learning. In light of this hardness, there is a growing effort to study what type of relaxations to the adversarially robust PAC learning setup can enable proper learnability. In this work, we initiate the study of proper learning under relaxations of the worst-case robust loss. We give a family of robust loss relaxations under which VC classes are properly PAC learnable with sample complexity close to what one would require in the standard PAC learning setup. On the other hand, we show that for an existing and natural relaxation of the worst-case robust loss, finite VC dimension is not sufficient for proper learning. Lastly, we give new generalization guarantees for the adversarially robust empirical risk minimizer.
Distributional Policy Evaluation: a Maximum Entropy approach to Representation Learning
Authors: Alberto Maria Metelli, Marcello Restelli Riccardo Zamboni
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The Maximum Entropy (Max-Ent) framework has been effectively employed in a variety of Reinforcement Learning (RL) tasks. In this paper, we first propose a novel Max-Ent framework for policy evaluation in a distributional RL setting, named Distributional Maximum Entropy Policy Evaluation (D-Max-Ent PE). We derive a generalization-error bound that depends on the complexity of the representation employed, showing that this framework can explicitly take into account the features used to represent the state space while evaluating a policy. Then, we exploit these favorable properties to drive the representation learning of the state space in a Structural Risk Minimization fashion. We employ state-aggregation functions as feature functions and we specialize the D-Max-Ent approach into an algorithm, named D-Max-Ent Progressive Factorization, which constructs a progressively finer-grained representation of the state space by balancing the trade-off between preserving information (bias) and reducing the effective number of states, i.e., the complexity of the representation space (variance). Finally, we report the results of some illustrative numerical simulations, showing that the proposed algorithm matches the expected theoretical behavior and highlighting the relationship between aggregations and sample regimes.
Thin and deep Gaussian processes
Authors: Alexander Nikitin, ST John, Magnus Ross, Mauricio A Álvarez, Marc Deisenroth, João Paulo Gomes, Diego Mesquita, César Lincoln Mattos Daniel Augusto de Souza
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the correlation distance of function values.However, selecting an appropriate kernel can be challenging.Deep GPs avoid manual kernel engineering by successively parameterizing kernels with GP layers, allowing them to learn low-dimensional embeddings of the inputs that explain the output data.Following the architecture of deep neural networks, the most common deep GPs warp the input space layer-by-layer but lose all the interpretability of shallow GPs. An alternative construction is to successively parameterize the lengthscale of a kernel, improving the interpretability but ultimately giving away the notion of learning lower-dimensional embeddings. Unfortunately, both methods are susceptible to particular pathologies which may hinder fitting and limit their interpretability.This work proposes a novel synthesis of both previous approaches: {Thin and Deep GP} (TDGP). Each TDGP layer defines locally linear transformations of the original input data maintaining the concept of latent embeddings while also retaining the interpretation of lengthscales of a kernel. Moreover, unlike the prior solutions, TDGP induces non-pathological manifolds that admit learning lower-dimensional representations.We show with theoretical and experimental results that i) TDGP is, unlike previous models, tailored to specifically discover lower-dimensional manifolds in the input data, ii) TDGP behaves well when increasing the number of layers, and iii) TDGP performs well in standard benchmark datasets.
Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language
Authors: Kevin Ellis
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: A core tension in models of concept learning is that the model must carefully balance the tractability of inference against the expressivity of the hypothesis class. Humans, however, can efficiently learn a broad range of concepts. We introduce a model of inductive learning that seeks to be human-like in that sense.It implements a Bayesian reasoning process where a language model first proposes candidate hypotheses expressed in natural language, which are then re-weighed by a prior and a likelihood.By estimating the prior from human data, we can predict human judgments on learning problems involving numbers and sets, spanning concepts that are generative, discriminative, propositional, and higher-order.
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
Authors: Alexander Neergaard Zahid, Morten Mørup Anders Nørskov
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction. Therefore, a key aim in EEG analysis is to extract the underlying neural activation (content) as well as to account for the individual subject variability (style). We hypothesize that the ability to convert EEG signals between tasks and subjects requires the extraction of latent representations accounting for content and style. Inspired by recent advancements in voice conversion technologies, we propose a novel contrastive split-latent permutation autoencoder (CSLP-AE) framework that directly optimizes for EEG conversion. Importantly, the latent representations are guided using contrastive learning to promote the latent splits to explicitly represent subject (style) and task (content). We contrast CSLP-AE to conventional supervised, unsupervised (AE), and self-supervised (contrastive learning) training and find that the proposed approach provides favorable generalizable characterizations of subject and task. Importantly, the procedure also enables zero-shot conversion between unseen subjects. While the present work only considers conversion of EEG, the proposed CSLP-AE provides a general framework for signal conversion and extraction of content (task activation) and style (subject variability) components of general interest for the modeling and analysis of biological signals.
Delegated Classification
Authors: Inbal Talgam-Cohen, Nir Rosenfeld Eden Saig
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance. In this work, we propose a theoretical framework for incentive-aware delegation of machine learning tasks. We model delegation as a principal-agent game, in which accurate learning can be incentivized by the principal using performance-based contracts. Adapting the economic theory of contract design to this setting, we define budget-optimal contracts and prove they take a simple threshold form under reasonable assumptions. In the binary-action case, the optimality of such contracts is shown to be equivalent to the classic Neyman-Pearson lemma, establishing a formal connection between contract design and statistical hypothesis testing. Empirically, we demonstrate that budget-optimal contracts can be constructed using small-scale data, leveraging recent advances in the study of learning curves and scaling laws. Performance and economic outcomes are evaluated using synthetic and real-world classification tasks.
PTQD: Accurate Post-Training Quantization for Diffusion Models
Authors: Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang Yefei He
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Diffusion models have recently dominated image synthesis and other related generative tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications. Post-training quantization of diffusion models can significantly reduce the model size and accelerate the sampling process without requiring any re-training. Nonetheless, applying existing post-training quantization methods directly to low-bit diffusion models can significantly impair the quality of generated samples. Specifically, for each denoising step, quantization noise leads to deviations in the estimated mean and mismatches with the predetermined variance schedule. Moreover, as the sampling process proceeds, the quantization noise may accumulate, resulting in a low signal-to-noise ratio (SNR) during the later denoising steps. To address these challenges, we propose a unified formulation for the quantization noise and diffusion perturbed noise in the quantized denoising process. Specifically, we first disentangle the quantization noise into its correlated and residual uncorrelated parts regarding its full-precision counterpart. The correlated part can be easily corrected by estimating the correlation coefficient. For the uncorrelated part, we subtract the bias from the quantized results to correct the mean deviation and calibrate the denoising variance schedule to absorb the excess variance resulting from quantization. Moreover, we introduce a mixed-precision scheme for selecting the optimal bitwidth for each denoising step, which prioritizes lower bitwidths to expedite early denoising steps, while ensuring that higher bitwidths maintain a high signal-to-noise ratio (SNR) in the later steps. Extensive experiments demonstrate that our method outperforms previous post-training quantized diffusion models in generating high-quality samples, with only a $0.06$ increase in FID score compared to full-precision LDM-4 on ImageNet $256\times256$, while saving $19.9\times$ bit operations. Code is available at [https://github.com/ziplab/PTQD](https://github.com/ziplab/PTQD).
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Authors: Zhenzhen Liu, Xiangyu Chen, Yurong You, Sagie Benaim, Cheng Perng Phoo, Mark Campbell, Wen Sun, Bharath Hariharan, Kilian Q. Weinberger Katie Luo
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Although very successful for Large Language Models (LLMs), these advancements have not had a comparable impact in research for autonomous vehicles—where alignment with human expectations can be imperative. In this paper, we propose to adapt similar RL-based methods to unsupervised object discovery, i.e. learning to detect objects from LiDAR points without any training labels. Instead of labels, we use simple heuristics to mimic human feedback. More explicitly, we combine multiple heuristics into a simple reward function that positively correlates its score with bounding box accuracy, i.e., boxes containing objects are scored higher than those without. We start from the detector’s own predictions to explore the space and reinforce boxes with high rewards through gradient updates. Empirically, we demonstrate that our approach is not only more accurate, but also orders of magnitudes faster to train compared to prior works on object discovery. Code is available at https://github.com/katieluo88/DRIFT.
Doubly Constrained Fair Clustering
Authors: Seyed Esmaeili, Jamie H Morgenstern, Claire Jie Zhang John Dickerson
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these notions are well-justified, they are often motivated and studied in a disjoint manner where one fairness desideratum is considered exclusively in isolation from the others. This leaves the understanding of the relations between different fairness notions as an important open problem in fair clustering. In this paper, we take the first step in this direction. Specifically, we consider the two most prominent demographic representation fairness notions in clustering: (1) Group Fairness ($\textbf{GF}$), where the different demographic groups are supposed to have close to population-level representation in each cluster and (2) Diversity in Center Selection ($\textbf{DS}$), where the selected centers are supposed to have close to population-level representation of each group. We show that given a constant approximation algorithm for one constraint ($\textbf{GF}$ or $\textbf{DS}$ only) we can obtain a constant approximation solution that satisfies both constraints simultaneously. Interestingly, we prove that any given solution that satisfies the $\textbf{GF}$ constraint can always be post-processed at a bounded degradation to the clustering cost to additionally satisfy the $\textbf{DS}$ constraint while the same statement is not true given a solution that satisfies $\textbf{DS}$ instead. Furthermore, we show that both $\textbf{GF}$ and $\textbf{DS}$ are incompatible (having an empty feasibility set in the worst case) with a collection of other distance-based fairness notions. Finally, we carry experiments to validate our theoretical findings.
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting
Authors: Jianyi Wang, Chen Change Loy Zongsheng Yue
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps. Existing acceleration sampling techniques inevitably sacrifice performance to some extent, leading to over-blurry SR results. To address this issue, we propose a novel and efficient diffusion model for SR that significantly reduces the number of diffusion steps, thereby eliminating the need for post-acceleration during inference and its associated performance deterioration. Our method constructs a Markov chain that transfers between the high-resolution image and the low-resolution image by shifting the residual between them, substantially improving the transition efficiency. Additionally, an elaborate noise schedule is developed to flexibly control the shifting speed and the noise strength during the diffusion process. Extensive experiments demonstrate that the proposed method obtains superior or at least comparable performance to current state-of-the-art methods on both synthetic and real-world datasets, \textit{\textbf{even only with 20 sampling steps}}. Our code and model will be made publicly.
WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding
Authors: Zihao Zhou, Hang Lv, Weiming Liu, Carl Yang Yanchao Tan
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Graphs are widely used to model interconnected entities and improve downstream predictions in various real-world applications. However, real-world graphs nowadays are often associated with complex attributes on multiple types of nodes and even links that are hard to model uniformly, while the widely used graph neural networks (GNNs) often require sufficient training toward specific downstream predictions to achieve strong performance. In this work, we take a fundamentally different approach than GNNs, to simultaneously achieve deep joint modeling of complex attributes and flexible structures of real-world graphs and obtain unsupervised generic graph representations that are not limited to specific downstream predictions. Our framework, built on a natural integration of language models (LMs) and random walks (RWs), is straightforward, powerful and data-efficient. Specifically, we first perform attributed RWs on the graph and design an automated program to compose roughly meaningful textual sequences directly from the attributed RWs; then we fine-tune an LM using the RW-based textual sequences and extract embedding vectors from the LM, which encapsulates both attribute semantics and graph structures. In our experiments, we evaluate the learned node embeddings towards different downstream prediction tasks on multiple real-world attributed graph datasets and observe significant improvements over a comprehensive set of state-of-the-art unsupervised node embedding methods. We believe this work opens a door for more sophisticated technical designs and empirical evaluations toward the leverage of LMs for the modeling of real-world graphs.
Generalizing Nonlinear ICA Beyond Structural Sparsity
Authors: Kun Zhang Yujia Zheng
Published in: Advances in Neural Information Processing Systems 36  (NeurIPS 2023) (Main Conference Track), 2023

Abstract: Nonlinear independent component analysis (ICA) aims to uncover the true latent sources from their observable nonlinear mixtures. Despite its significance, the identifiability of nonlinear ICA is known to be impossible without additional assumptions. Recent advances have proposed conditions on the connective structure from sources to observed variables, known as Structural Sparsity, to achieve identifiability in an unsupervised manner. However, the sparsity constraint may not hold universally for all sources in practice. Furthermore, the assumptions of bijectivity of the mixing process and independence among all sources, which arise from the setting of ICA, may also be violated in many real-world scenarios. To address these limitations and generalize nonlinear ICA, we propose a set of new identifiability results in the general settings of undercompleteness, partial sparsity and source dependence, and flexible grouping structures. Specifically, we prove identifiability when there are more observed variables than sources (undercomplete), and when certain sparsity and/or source independence assumptions are not met for some changing sources. Moreover, we show that even in cases with flexible grouping structures (e.g., part of the sources can be divided into irreducible independent groups with various sizes), appropriate identifiability results can also be established. Theoretical claims are supported empirically on both synthetic and real-world datasets.