mtybilly/Instruct2DS huggingface dataset:
Off-Policy Average Reward Actor-Critic with Deterministic Policy Search
Authors: Saxena, Naman and Khastagir, Subhojyoti and Kolathaya, Shishir and Bhatnagar, Shalabh
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average reward actor-critic algorithms, but average reward off-policy actor-critic is relatively less explored. In this work, we present both on-policy and off-policy deterministic policy gradient theorems for the average reward performance criterion. Using these theorems, we also present an Average Reward Off-Policy Deep Deterministic Policy Gradient (ARO-DDPG) Algorithm. We first show asymptotic convergence analysis using the ODE-based method. Subsequently, we provide a finite time analysis of the resulting stochastic approximation scheme with linear function approximator and obtain an $ε$-optimal stationary policy with a sample complexity of $Ømega(ε^-2.5)$. We compare the average reward performance of our proposed ARO-DDPG algorithm and observe better empirical performance compared to state-of-the-art on-policy average reward actor-critic algorithms over MuJoCo-based environments.
Gibbsian Polar Slice Sampling
Authors: Schär, Philip and Habeck, Michael and Rudolf, Daniel
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Polar slice sampling (Roberts & Rosenthal, 2002) is a Markov chain approach for approximate sampling of distributions that is difficult, if not impossible, to implement efficiently, but behaves provably well with respect to the dimension. By updating the directional and radial components of chain iterates separately, we obtain a family of samplers that mimic polar slice sampling, and yet can be implemented efficiently. Numerical experiments in a variety of settings indicate that our proposed algorithm outperforms the two most closely related approaches, elliptical slice sampling (Murray et al., 2010) and hit-and-run uniform slice sampling (MacKay, 2003). We prove the well-definedness and convergence of our methods under suitable assumptions on the target distribution.
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
Authors: Schlaginhaufen, Andreas and Kamgarpour, Maryam
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present a theoretical framework for Inverse Reinforcement Learning (IRL) in constrained Markov decision processes. From a convex-analytic perspective, we extend prior results on reward identifiability and generalizability to both the constrained setting and a more general class of regularizations. In particular, we show that identifiability up to potential shaping (Cao et al., 2021) is a consequence of entropy regularization and may generally no longer hold for other regularizations or in the presence of safety constraints. We also show that to ensure generalizability to new transition laws and constraints, the true reward must be identified up to a constant. Additionally, we derive a finite sample guarantee for the suboptimality of the learned rewards, and validate our results in a gridworld environment.
Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
Authors: Schnaus, Dominik and Lee, Jongseok and Cremers, Daniel and Triebel, Rudolph
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks. The key idea is to exploit scalable and structured posteriors of neural networks as informative priors with generalization guarantees. Our learned priors provide expressive probabilistic representations at large scale, like Bayesian counterparts of pre-trained models on ImageNet, and further produce non-vacuous generalization bounds. We also extend this idea to a continual learning framework, where the favorable properties of our priors are desirable. Major enablers are our technical contributions: (1) the sums-of-Kronecker-product computations, and (2) the derivations and optimizations of tractable objectives that lead to improved generalization bounds. Empirically, we exhaustively show the effectiveness of this method for uncertainty estimation and generalization.
Deterministic equivalent and error universality of deep random features learning
Authors: Schröder, Dominik and Cui, Hugo and Dmitriev, Daniil and Loureiro, Bruno
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: This manuscript considers the problem of learning a random Gaussian network function using a fully connected network with frozen intermediate layers and trainable readout layer. This problem can be seen as a natural generalization of the widely studied random features model to deeper architectures. First, we prove Gaussian universality of the test error in a ridge regression setting where the learner and target networks share the same intermediate layers, and provide a sharp asymptotic formula for it. Establishing this result requires proving a deterministic equivalent for traces of the deep random features sample covariance matrices which can be of independent interest. Second, we conjecture the asymptotic Gaussian universality of the test error in the more general setting of arbitrary convex losses and generic learner/target architectures. We provide extensive numerical evidence for this conjecture, which requires the derivation of closed-form expressions for the layer-wise post-activation population covariances. In light of our results, we investigate the interplay between architecture design and implicit regularization.
The Acquisition of Physical Knowledge in Generative Neural Networks
Authors: Schulze Buschoff, Luca M. and Schulz, Eric and Binz, Marcel
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: As children grow older, they develop an intuitive understanding of the physical processes around them. Their physical understanding develops in stages, moving along developmental trajectories which have been mapped out extensively in previous empirical research. Here, we investigate how the learning trajectories of deep generative neural networks compare to children’s developmental trajectories using physical understanding as a testbed. We outline an approach that allows us to examine two distinct hypotheses of human development – stochastic optimization and complexity increase. We find that while our models are able to accurately predict a number of physical processes, their learning trajectories under both hypotheses do not follow the developmental trajectories of children.
Modality-Agnostic Variational Compression of Implicit Neural Representations
Authors: Schwarz, Jonathan Richard and Tack, Jihoon and Teh, Yee Whye and Lee, Jaeho and Shin, Jinwoo
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtain compact latent representations non-linearly mapped to a soft gating mechanism. This allows the specialisation of a shared INR network to each data item through subnetwork selection. After obtaining a dataset of such latent representations, we directly optimise the rate/distortion trade-off in a modality-agnostic space using neural compression. Variational Compression of Implicit Neural Representations (VC-INR) shows improved performance given the same representational capacity pre quantisation while also outperforming previous quantisation schemes used for other INR techniques.Our experiments demonstrate strong results over a large set of diverse modalities using the same algorithm without any modality-specific inductive biases. We show results on images, climate data, 3D shapes and scenes as well as audio and video, introducing VC-INR as the first INR-based method to outperform codecs as well-known and diverse as JPEG 2000, MP3 and AVC/HEVC on their respective modalities.
Bigger, Better, Faster: Human-level Atari with human-level efficiency
Authors: Schwarzer, Max and Obando Ceron, Johan Samir and Courville, Aaron and Bellemare, Marc G and Agarwal, Rishabh and Castro, Pablo Samuel
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning
Authors: Sclocchi, Antonio and Geiger, Mario and Wyart, Matthieu
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Understanding when the noise in stochastic gradient descent (SGD) affects generalization of deep neural networks remains a challenge, complicated by the fact that networks can operate in distinct training regimes. Here we study how the magnitude of this noise $T$ affects performance as the size of the training set $P$ and the scale of initialization $α$ are varied. For gradient descent, $α$ is a key parameter that controls if the network is lazy’ ($α≫1$) or instead learns features ($αłl1$). For classification of MNIST and CIFAR10 images, our central results are: *(i)* obtaining phase diagrams for performance in the $(α,T)$ plane. They show that SGD noise can be detrimental or instead useful depending on the training regime. Moreover, although increasing $T$ or decreasing $α$ both allow the net to escape the lazy regime, these changes can have opposite effects on performance. *(ii)* Most importantly, we find that the characteristic temperature $T_c$ where the noise of SGD starts affecting the trained model (and eventually performance) is a power law of $P$. We relate this finding with the observation that key dynamical quantities, such as the total variation of weights during training, depend on both $T$ and $P$ as power laws. These results indicate that a key effect of SGD noise occurs late in training, by affecting the stopping process whereby all data are fitted. Indeed, we argue that due to SGD noise, nets must develop a strongersignal’, i.e. larger informative weights, to fit the data, leading to a longer training time. A stronger signal and a longer training time are also required when the size of the training set $P$ increases. We confirm these views in the perceptron model, where signal and noise can be precisely measured. Interestingly, exponents characterizing the effect of SGD depend on the density of data near the decision boundary, as we explain.
A Fast Optimistic Method for Monotone Variational Inequalities
Authors: Sedlmayer, Michael and Nguyen, Dang-Khoa and Bot, Radu Ioan
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We study monotone variational inequalities that can arise as optimality conditions for constrained convex optimization or convex-concave minimax problems and propose a novel algorithm that uses only one gradient/operator evaluation and one projection onto the constraint set per iteration. The algorithm, which we call fOGDA-VI, achieves a $o(\frac1k)$ rate of convergence in terms of the restricted gap function as well as the natural residual for the last iterate. Moreover, we provide a convergence guarantee for the sequence of iterates to a solution of the variational inequality. These are the best theoretical convergence results for numerical methods for (only) monotone variational inequalities reported in the literature. To empirically validate our algorithm we investigate a two-player matrix game with mixed strategies of the two players. Concluding, we show promising results regarding the application of fOGDA-VI to the training of generative adversarial nets.
Double-Weighting for Covariate Shift Adaptation
Authors: Segovia-Martín, José I. and Mazuelas, Santiago and Liu, Anqi
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Supervised learning is often affected by a covariate shift in which the marginal distributions of instances (covariates $x$) of training and testing samples $p_\texttr(x)$ and $p_\textte(x)$ are different but the label conditionals coincide. Existing approaches address such covariate shift by either using the ratio $p_\textte(x)/p_\texttr(x)$ to weight training samples (reweighted methods) or using the ratio $p_\texttr(x)/p_\textte(x)$ to weight testing samples (robust methods). However, the performance of such approaches can be poor under support mismatch or when the above ratios take large values. We propose a minimax risk classification (MRC) approach for covariate shift adaptation that avoids such limitations by weighting both training and testing samples. In addition, we develop effective techniques that obtain both sets of weights and generalize the conventional kernel mean matching method. We provide novel generalization bounds for our method that show a significant increase in the effective sample size compared with reweighted methods. The proposed method also achieves enhanced classification performance in both synthetic and empirical experiments.
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
Authors: Seidl, Philipp and Vall, Andreu and Hochreiter, Sepp and Klambauer, Günter
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without training or fine-tuning, scientific language models could be used for such low-data tasks through their announced zero- and few-shot capabilities. However, their predictive quality at activity prediction is lacking. In this work, we envision a novel type of activity prediction model that is able to adapt to new prediction tasks at inference time, via understanding textual information describing the task. To this end, we propose a new architecture with separate modules for chemical and natural language inputs, and a contrastive pretraining objective on data from large biochemical databases. In extensive experiments, we show that our method CLAMP yields improved predictive performance on few-shot learning benchmarks and zero-shot problems in drug discovery. We attribute the advances of our method to the modularized architecture and to our pre-training objective.
Variational Autoencoding Neural Operators
Authors: Seidman, Jacob H and Kissas, Georgios and Pappas, George J. and Perdikaris, Paris
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate modeling and physical systems. A natural way of modeling functional data is by learning operators between infinite dimensional spaces, leading to discretization invariant representations that scale independently of the sample grid resolution. Here we present Variational Autoencoding Neural Operators (VANO), a general strategy for making a large class of operator learning architectures act as variational autoencoders. For this purpose, we provide a novel rigorous mathematical formulation of the variational objective in function spaces for training. VANO first maps an input function to a distribution over a latent space using a parametric encoder and then decodes a sample from the latent distribution to reconstruct the input, as in classic variational autoencoders. We test VANO with different model set-ups and architecture choices for a variety of benchmarks. We start from a simple Gaussian random field where we can analytically track what the model learns and progressively transition to more challenging benchmarks including modeling phase separation in Cahn-Hilliard systems and real world satellite data for measuring Earth surface deformation.
Neural Markov Jump Processes
Authors: Seifner, Patrick and Sanchez, Ramses J
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Markov jump processes are continuous-time stochastic processes with a wide range of applications in both natural and social sciences. Despite their widespread use, inference in these models is highly non-trivial and typically proceeds via either Monte Carlo or expectation-maximization methods. In this work we introduce an alternative, variational inference algorithm for Markov jump processes which relies on neural ordinary differential equations, and is trainable via back-propagation. Our methodology learns neural, continuous-time representations of the observed data, that are used to approximate the initial distribution and time-dependent transition probability rates of the posterior Markov jump process. The time-independent rates of the prior process are in contrast trained akin to generative adversarial networks. We test our approach on synthetic data sampled from ground-truth Markov jump processes, experimental switching ion channel data and molecular dynamics simulations. Source code to reproduce our experiments is available online.
Bayesian online change point detection with Hilbert space approximate Student-t process
Authors: Sellier, Jeremy and Dellaportas, Petros
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: In this paper, we introduce a variant of Bayesian online change point detection with a reducedrank Student-t process (TP) and dependent Student-t noise, as a nonparametric time series model. Our method builds and improves upon the state-of-the-art Gaussian process (GP) change point model benchmark of Saatci et al. (2010). The Student-t process generalizes the concept of a GP and hence yields a more flexible alternative. Additionally, unlike a GP, the predictive variance explicitly depends on the training observations, while the use of an entangled Student-t noise model preserves analytical tractability. Our approach also uses a Hilbert space reduced-rank representation of the TP kernel, derived from an eigenfunction expansion of the Laplace operator (Solin & Sarkka, 2020), to alleviate its computational complexity. Improvements in prediction and training time are demonstrated with real-world data-sets
Incentivizing Exploration with Linear Contexts and Combinatorial Actions
Authors: Sellke, Mark
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We advance the study of incentivized bandit exploration, in which arm choices are viewed as recommendations and are required to be Bayesian incentive compatible. Recent work of Sellke-Slivkins (Operations Research 2022) has shown that for the special case of independent arms, after collecting enough initial samples, the popular Thompson sampling algorithm becomes incentive compatible. This was generalized to the combinatorial semibandit in Hu-Ngo-Slivkins-Wu (NeurIPS 2022). We give an analog of this result for linear bandits, where the independence of the prior is replaced by a natural convexity condition. This opens up the possibility of efficient and regret-optimal incentivized exploration in high-dimensional action spaces. In the semibandit model, we also improve the sample complexity for the pre-Thompson sampling phase of initial data collection.
Explainability as statistical inference
Authors: Senetaire, Hugo Henri Joseph and Garreau, Damien and Frellsen, Jes and Mattei, Pierre-Alexandre
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and cast interpretability as a statistical inference problem. We propose a general deep probabilistic model designed to produce interpretable predictions. The model’s parameters can be learned via maximum likelihood, and the method can be adapted to any predictor network architecture, and any type of prediction problem. Our model is akin to amortized interpretability methods, where a neural network is used as a selector to allow for fast interpretation at inference time. Several popular interpretability methods are shown to be particular cases of regularized maximum likelihood for our general model. Using our framework, we identify imputation as a common issue of these models. We propose new datasets with ground truth selection which allow for the evaluation of the features importance map and show experimentally that multiple imputation provides more reasonable interpretations.
Multi-View Masked World Models for Visual Robotic Manipulation
Authors: Seo, Younggyo and Kim, Junsu and James, Stephen and Lee, Kimin and Shin, Jinwoo and Abbeel, Pieter
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Visual robotic manipulation research and applications often use multiple cameras, or views, to better perceive the world. How else can we utilize the richness of multi-view data? In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation. Specifically, we train a multi-view masked autoencoder which reconstructs pixels of randomly masked viewpoints and then learn a world model operating on the representations from the autoencoder. We demonstrate the effectiveness of our method in a range of scenarios, including multi-view control and single-view control with auxiliary cameras for representation learning. We also show that the multi-view masked autoencoder trained with multiple randomized viewpoints enables training a policy with strong viewpoint randomization and transferring the policy to solve real-robot tasks without camera calibration and an adaptation procedure. Video demonstrations are available at: https://sites.google.com/view/mv-mwm.
One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding
Authors: Severo, Daniel and Townsend, James and Khisti, Ashish J and Makhzani, Alireza
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We present a one-shot method for compressing large labeled graphs called Random Edge Coding. When paired with a parameter-free model based on Pólya’s Urn, the worst-case computational and memory complexities scale quasi-linearly and linearly with the number of observed edges, making it efficient on sparse graphs, and requires only integer arithmetic. Key to our method is bits-back coding, which is used to sample edges and vertices without replacement from the edge-list in a way that preserves the structure of the graph. Optimality is proven under a class of random graph models that are invariant to permutations of the edges and of vertices within an edge. Experiments indicate Random Edge Coding can achieve competitive compression performance on real-world network datasets and scales to graphs with millions of nodes and edges.
ModelDiff: A Framework for Comparing Learning Algorithms
Authors: Shah, Harshay and Park, Sung Min and Ilyas, Andrew and Madry, Aleksander
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We study the problem of (learning) algorithm comparison, where the goal is to find differences between models trained with two different learning algorithms. We begin by formalizing this goal as one of finding distinguishing feature transformations, i.e., input transformations that change the predictions of models trained with one learning algorithm but not the other. We then present ModelDiff, a method that leverages the datamodels framework (Ilyas et al., 2022) to compare learning algorithms based on how they use their training data. We demonstrate ModelDiff through three case studies, comparing models trained with/without data augmentation, with/without pre-training, and with different SGD hyperparameters.
Auxiliary Learning as an Asymmetric Bargaining Game
Authors: Shamsian, Aviv and Navon, Aviv and Glazer, Neta and Kawaguchi, Kenji and Chechik, Gal and Fetaya, Ethan
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets. However, this approach may present several difficulties: (i) optimizing multiple objectives can be more challenging, and (ii) how to balance the auxiliary tasks to best assist the main task is unclear. In this work, we propose a novel approach, named AuxiNash, for balancing tasks in auxiliary learning by formalizing the problem as generalized bargaining game with asymmetric task bargaining power. Furthermore, we describe an efficient procedure for learning the bargaining power of tasks based on their contribution to the performance of the main task and derive theoretical guarantees for its convergence. Finally, we evaluate AuxiNash on multiple multi-task benchmarks and find that it consistently outperforms competing methods.
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Authors: Shao, Zhihong and Gong, Yeyun and Shen, Yelong and Huang, Minlie and Duan, Nan and Chen, Weizhu
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Large language models can perform various reasoning tasks by using chain-of-thought prompting, which guides them to find answers through step-by-step demonstrations. However, the quality of the prompts depends on the demonstrations given to the models, and creating many of them by hand is costly. We introduce Synthetic prompting, a method that leverages a few handcrafted examples to prompt the model to generate more examples by itself, and selects effective demonstrations to elicit better reasoning. Our method alternates between a backward and forward process to generate new examples. The backward process generates a question that match a sampled reasoning chain, so that the question is solvable and clear. The forward process produces a more detailed reasoning chain for the question, improving the quality of the example. We evaluate our method on numerical, symbolic, and algorithmic reasoning tasks, and show that it outperforms existing prompting techniques.
Complementary Attention for Multi-Agent Reinforcement Learning
Authors: Shao, Jianzhun and Zhang, Hongchang and Qu, Yun and Liu, Chang and He, Shuncheng and Jiang, Yuhang and Ji, Xiangyang
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: In cooperative multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) shows great promise for a trade-off between independent Q-learning and joint action learning. However, vanilla CTDE methods assumed a fixed number of agents could hardly adapt to real-world scenarios where dynamic team compositions typically suffer from dramatically variant partial observability. Specifically, agents with extensive sight ranges are prone to be affected by trivial environmental substrates, dubbed the "distracted attention" issue; ones with limited observation can hardly sense their teammates, degrading the cooperation quality. In this paper, we propose Complementary Attention for Multi-Agent reinforcement learning (CAMA), which applies a divide-and-conquer strategy on input entities accompanied with the complementary attention of enhancement and replenishment. Concretely, to tackle the distracted attention issue, highly contributed entities’ attention is enhanced by the execution-related representation extracted via action prediction with an inverse model. For better out-of-sight-range cooperation, the lowly contributed ones are compressed to brief messages with a conditional mutual information estimator. Our CAMA facilitates stable and sustainable teamwork, which is justified by the impressive results reported on the challenging StarCraftII, MPE, and Traffic Junction benchmarks.
Regularization-free Diffeomorphic Temporal Alignment Nets
Authors: Shapira Weber, Ron and Freifeld, Oren
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: In time-series analysis, nonlinear temporal misalignment is a major problem that forestalls even simple averaging. An effective learning-based solution for this problem is the Diffeomorphic Temporal Alignment Net (DTAN), that, by relying on a diffeomorphic temporal transformer net and the amortization of the joint-alignment task, eliminates drawbacks of traditional alignment methods. Unfortunately, existing DTAN formulations crucially depend on a regularization term whose optimal hyperparameters are dataset-specific and usually searched via a large number of experiments. Here we propose a regularization-free DTAN that obviates the need to perform such an expensive, and often impractical, search. Concretely, we propose a new well-behaved loss that we call the Inverse Consistency Averaging Error (ICAE), as well as a related new triplet loss. Extensive experiments on 128 UCR datasets show that the proposed method outperforms contemporary methods despite not using a regularization. Moreover, ICAE also gives rise to the first DTAN that supports variable-length signals. Our code is available at https://github.com/BGU-CS-VIL/RF-DTAN.
Toward Efficient Gradient-Based Value Estimation
Authors: Sharifnassab, Arsalan and Sutton, Richard S.
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Gradient-based methods for value estimation in reinforcement learning have favorable stability properties, but they are typically much slower than Temporal Difference (TD) learning methods. We study the root causes of this slowness and show that Mean Square Bellman Error (MSBE) is an ill-conditioned loss function in the sense that its Hessian has large condition-number. To resolve the adverse effect of poor conditioning of MSBE on gradient based methods, we propose a low complexity batch-free proximal method that approximately follows the Gauss-Newton direction and is asymptotically robust to parameterization. Our main algorithm, called RANS, is efficient in the sense that it is significantly faster than the residual gradient methods while having almost the same computational complexity, and is competitive with TD on the classic problems that we tested.
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Authors: Sharrock, Louis and Nemeth, Christopher
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: In recent years, particle-based variational inference (ParVI) methods such as Stein variational gradient descent (SVGD) have grown in popularity as scalable methods for Bayesian inference. Unfortunately, the properties of such methods invariably depend on hyperparameters such as the learning rate, which must be carefully tuned by the practitioner in order to ensure convergence to the target measure at a suitable rate. In this paper, we introduce a suite of new particle-based methods for scalable Bayesian inference based on coin betting, which are entirely learning-rate free. We illustrate the performance of our approach on a range of numerical examples, including several high-dimensional models and datasets, demonstrating comparable performance to other ParVI algorithms with no need to tune a learning rate.
On Kinetic Optimal Probability Paths for Generative Models
Authors: Shaul, Neta and Chen, Ricky T. Q. and Nickel, Maximilian and Le, Matthew and Lipman, Yaron
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Recent successful generative models are trained by fitting a neural network to an a-priori defined tractable probability density path taking noise to training examples. In this paper we investigate the space of Gaussian probability paths, which includes diffusion paths as an instance, and look for an optimal member in some useful sense. In particular, minimizing the Kinetic Energy (KE) of a path is known to make particles’ trajectories simple, hence easier to sample, and empirically improve performance in terms of likelihood of unseen data and sample generation quality. We investigate Kinetic Optimal (KO) Gaussian paths and offer the following observations: (i) We show the KE takes a simplified form on the space of Gaussian paths, where the data is incorporated only through a single, one dimensional scalar function, called the <em>data separation function</em>. (ii) We characterize the KO solutions with a one dimensional ODE. (iii) We approximate data-dependent KO paths by approximating the data separation function and minimizing the KE. (iv) We prove that the data separation function converges to $1$ in the general case of arbitrary normalized dataset consisting of $n$ samples in $d$ dimension as $n/\sqrtdi̊ghtarrow 0$. A consequence of this result is that the Conditional Optimal Transport (Cond-OT) path becomes <em>kinetic optimal</em> as $n/\sqrtd\g̊htarrow 0$. We further support this theory with empirical experiments on ImageNet.
Sequential Changepoint Detection via Backward Confidence Sequences
Authors: Shekhar, Shubhanshu and Ramdas, Aaditya
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We present a simple reduction from sequential estimation to sequential changepoint detection (SCD). In short, suppose we are interested in detecting changepoints in some parameter or functional $þeta$ of the underlying distribution. We demonstrate that if we can construct a confidence sequence (CS) for $þeta$, then we can also successfully perform SCD for $þeta$. This is accomplished by checking if two CSs — one forwards and the other backwards — ever fail to intersect. Since the literature on CSs has been rapidly evolving recently, the reduction provided in this paper immediately solves several old and new change detection problems. Further, our “backward CS”, constructed by reversing time, is new and potentially of independent interest. We provide strong nonasymptotic guarantees on the frequency of false alarms and detection delay, and demonstrate numerical effectiveness on several problems.
Cold Analysis of Rao-Blackwellized Straight-Through Gumbel-Softmax Gradient Estimator
Authors: Shekhovtsov, Alexander
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Many problems in machine learning require an estimate of the gradient of an expectation in discrete random variables with respect to the sampling distribution. This work is motivated by the development of the Gumbel-Softmax family of estimators, which use a temperature-controlled relaxation of discrete variables. The state-of-the art in this family, the Gumbel-Rao estimator uses an extra internal sampling to reduce the variance, which may be costly. We analyze this estimator and show that it possesses a zero temperature limit with a surprisingly simple closed form. The limit estimator, called ZGR, has favorable bias and variance properties, it is easy to implement and computationally inexpensive. It decomposes as the average of the straight through (ST) estimator and DARN estimator — two basic but not very well performing on their own estimators. We demonstrate that the simple ST–ZGR family of estimators practically dominates in the bias-variance tradeoffs the whole GR family while also outperforming SOTA unbiased estimators.
Towards Understanding and Improving GFlowNet Training
Authors: Shen, Max W and Bengio, Emmanuel and Hajiramezanali, Ehsan and Loukas, Andreas and Cho, Kyunghyun and Biancalani, Tommaso
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Generative flow networks (GFlowNets) are a family of algorithms that learn a generative policy to sample discrete objects $x$ with non-negative reward $R(x)$. Learning objectives guarantee the GFlowNet samples $x$ from the target distribution $p^*(x) ∝ R(x)$ when loss is globally minimized over all states or trajectories, but it is unclear how well they perform with practical limits on training resources. We introduce an efficient evaluation strategy to compare the learned sampling distribution to the target reward distribution. As flows can be underdetermined given training data, we clarify the importance of learned flows to generalization and matching $p^*(x)$ in practice. We investigate how to learn better flows, and propose (i) prioritized replay training of high-reward $x$, (ii) relative edge flow policy parametrization, and (iii) a novel guided trajectory balance objective, and show how it can solve a substructure credit assignment problem. We substantially improve sample efficiency on biochemical design tasks.
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
Authors: Shen, Maohao and Bu, Yuheng and Wornell, Gregory W.
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data used to train a collection of source models. Existing methods for multi-source-free domain adaptation (MSFDA) typically train a target model using pseudo-labeled data produced by the source models, which focus on improving the pseudo-labeling techniques or proposing new training objectives. Instead, we aim to analyze the fundamental limits of MSFDA. In particular, we develop an information-theoretic bound on the generalization error of the resulting target model, which illustrates an inherent bias-variance trade-off. We then provide insights on how to balance this trade-off from three perspectives, including domain aggregation, selective pseudo-labeling, and joint feature alignment, which leads to the design of novel algorithms. Experiments on multiple datasets validate our theoretical analysis and demonstrate the state-of-art performance of the proposed algorithm, especially on some of the most challenging datasets, including Office-Home and DomainNet.
On Penalty-based Bilevel Gradient Descent Method
Authors: Shen, Han and Chen, Tianyi
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization, meta-learning and reinforcement learning. However, bilevel problems are difficult to solve and recent progress on scalable bilevel algorithms mainly focuses on bilevel optimization problems where the lower-level objective is either strongly convex or unconstrained. In this work, we tackle the bilevel problem through the lens of the penalty method. We show that under certain conditions, the penalty reformulation recovers the solutions of the original bilevel problem. Further, we propose the penalty-based bilevel gradient descent algorithm and establish its finite-time convergence for the constrained bilevel problem without lower-level strong convexity. The experimental results showcase the efficiency of the proposed algorithm.
Non-autoregressive Conditional Diffusion Models for Time Series Prediction
Authors: Shen, Lifeng and Kwok, James
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Recently, denoising diffusion models have led to significant breakthroughs in the generation of images, audio and text. However, it is still an open question on how to adapt their strong modeling ability to model time series. In this paper, we propose TimeDiff, a non-autoregressive diffusion model that achieves high-quality time series prediction with the introduction of two novel conditioning mechanisms: future mixup and autoregressive initialization. Similar to teacher forcing, future mixup allows parts of the ground-truth future predictions for conditioning, while autoregressive initialization helps better initialize the model with basic time series patterns such as short-term trends. Extensive experiments are performed on nine real-world datasets. Results show that TimeDiff consistently outperforms existing time series diffusion models, and also achieves the best overall performance across a variety of the existing strong baselines (including transformers and FiLM).
Cross-Modal Fine-Tuning: Align then Refine
Authors: Shen, Junhong and Li, Liam and Dery, Lucio M. and Staten, Corey and Khodak, Mikhail and Neubig, Graham and Talwalkar, Ameet
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA adapts to a target task via an align-then-refine workflow: given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretraining modality. The pretrained model is then fine-tuned on the embedded data to exploit the knowledge shared across modalities. Through extensive experiments, we show that ORCA obtains state-of-the-art results on 3 benchmarks containing over 60 datasets from 12 modalities, outperforming a wide range of hand-designed, AutoML, general-purpose, and task-specific cross-modal methods. We highlight the importance of data alignment via a series of ablation studies and exemplify ORCA’s utility in data-limited regimes.
Auxiliary Modality Learning with Generalized Curriculum Distillation
Authors: Shen, Yu and Wang, Xijun and Gao, Peng and Lin, Ming
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: Driven by the need from real-world applications, Auxiliary Modality Learning (AML) offers the possibility to utilize more information from auxiliary data in training, while only requiring data from one or fewer modalities in test, to save the overall computational cost and reduce the amount of input data for inferencing. In this work, we formally define “Auxiliary Modality Learning” (AML), systematically classify types of auxiliary modality (in visual computing) and architectures for AML, and analyze their performance. We also analyze the conditions under which AML works well from the optimization and data distribution perspectives. To guide various choices to achieve optimal performance using AML, we propose a novel method to assist in choosing the best auxiliary modality and estimating an upper bound performance before executing AML. In addition, we propose a new AML method using generalized curriculum distillation to enable more effective curriculum learning. Our method achieves the best performance compared to other SOTA methods.
TGRL: An Algorithm for Teacher Guided Reinforcement Learning
Authors: Shenfeld, Idan and Hong, Zhang-Wei and Tamar, Aviv and Agrawal, Pulkit
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: We consider solving sequential decision-making problems in the scenario where the agent has access to two supervision sources: $\textitreward signal$ and a $\textitteacher$ that can be queried to obtain a $\textitgood$ action for any state encountered by the agent. Learning solely from rewards, or reinforcement learning, is data inefficient and may not learn high-reward policies in challenging scenarios involving sparse rewards or partial observability. On the other hand, learning from a teacher may sometimes be infeasible. For instance, the actions provided by a teacher with privileged information may be unlearnable by an agent with limited information (i.e., partial observability). In other scenarios, the teacher might be sub-optimal, and imitating their actions can limit the agent’s performance. To overcome these challenges, prior work proposed to jointly optimize imitation and reinforcement learning objectives but relied on heuristics and problem-specific hyper-parameter tuning to balance the two objectives. We introduce Teacher Guided Reinforcement Learning (TGRL), a principled approach to dynamically balance following the teacher’s guidance and leveraging RL. TGRL outperforms strong baselines across diverse domains without hyperparameter tuning.
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
Authors: Sheng, Ying and Zheng, Lianmin and Yuan, Binhang and Li, Zhuohan and Ryabinin, Max and Chen, Beidi and Liang, Percy and Re, Christopher and Stoica, Ion and Zhang, Ce
Published in: Proceedings of the 40th International Conference on Machine Learning (Main Conference Track), 2023

Abstract: The high computational and memory requirements of large language model (LLM) inference make it feasible only with multiple high-end accelerators. Motivated by the emerging demand for latency-insensitive tasks with batched processing, this paper initiates the study of high-throughput LLM inference using limited resources, such as a single commodity GPU. We present FlexGen, a high-throughput generation engine for running LLMs with limited GPU memory. FlexGen can be flexibly configured under various hardware resource constraints by aggregating memory and computation from the GPU, CPU, and disk. By solving a linear programming problem, it searches for efficient patterns to store and access tensors. FlexGen further compresses the weights and the attention cache to 4 bits with negligible accuracy loss. These techniques enable FlexGen to have a larger space of batch size choices and thus significantly increase maximum throughput. As a result, when running OPT-175B on a single 16GB GPU, FlexGen achieves significantly higher throughput compared to state-of-the-art offloading systems, reaching a generation throughput of 1 token/s for the first time with an effective batch size of 144. On the HELM benchmark, FlexGen can benchmark a 30B model with a 16GB GPU on 7 representative sub-scenarios in 21 hours. The code is available at https://github.com/FMInference/FlexGen.